CN114909608B - Non-excavation pipeline positioning method based on MIMU/mileage wheel/photoelectric speed measurement module combination - Google Patents
Non-excavation pipeline positioning method based on MIMU/mileage wheel/photoelectric speed measurement module combination Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16L—PIPES; JOINTS OR FITTINGS FOR PIPES; SUPPORTS FOR PIPES, CABLES OR PROTECTIVE TUBING; MEANS FOR THERMAL INSULATION IN GENERAL
- F16L55/00—Devices or appurtenances for use in, or in connection with, pipes or pipe systems
- F16L55/26—Pigs or moles, i.e. devices movable in a pipe or conduit with or without self-contained propulsion means
- F16L55/28—Constructional aspects
- F16L55/30—Constructional aspects of the propulsion means, e.g. towed by cables
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16L—PIPES; JOINTS OR FITTINGS FOR PIPES; SUPPORTS FOR PIPES, CABLES OR PROTECTIVE TUBING; MEANS FOR THERMAL INSULATION IN GENERAL
- F16L55/00—Devices or appurtenances for use in, or in connection with, pipes or pipe systems
- F16L55/26—Pigs or moles, i.e. devices movable in a pipe or conduit with or without self-contained propulsion means
- F16L55/48—Indicating the position of the pig or mole in the pipe or conduit
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- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16L—PIPES; JOINTS OR FITTINGS FOR PIPES; SUPPORTS FOR PIPES, CABLES OR PROTECTIVE TUBING; MEANS FOR THERMAL INSULATION IN GENERAL
- F16L2101/00—Uses or applications of pigs or moles
- F16L2101/30—Inspecting, measuring or testing
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Abstract
The invention discloses a non-excavation pipeline positioning method based on a MIMU/mileage wheel/photoelectric speed sensor combination. The invention combines the photoelectric speed measuring sensor with the mileage wheel to realize the purpose of redundant speed measurement by utilizing the characteristics of non-contact speed measurement and no slipping of the photoelectric speed measuring sensor, and utilizes the Federal Kalman filtering algorithm based on the maximum correlation entropy M estimation to realize the effective data fusion of MIMU, the mileage wheel and the information of the photoelectric speed measuring sensor on the basis, thereby solving the problem of the reduced pipeline positioning performance caused by the unstable speed measuring information of the mileage wheel. The invention effectively reduces the positioning error caused by the skidding of the mileage wheel.
Description
Technical Field
The invention belongs to the field of underground pipeline geographic position information measurement, and particularly relates to a non-excavation pipeline positioning method based on MIMU/mileage wheel/photoelectric speed measurement module combination.
Background
Underground pipelines are important components of urban infrastructure and bear important functions such as information, energy, water heating resource circulation, waste disposal and the like in cities. As urban areas continue to develop, the number of underground pipelines is increasing, and pipeline distribution is more and more complex. This brings difficulty to the use and maintenance of underground pipelines and the development of urban underground engineering. Therefore, it is necessary to build an accurate three-dimensional map for urban underground pipelines to prevent pipelines from being damaged by projects such as excavation and construction. Currently, common nondestructive pipeline positioning technologies, including a ground penetrating radar positioning method, a multi-frequency electromagnetic positioning method and a magnetometer positioning method, have limitations, and the positioning performance is limited by various factors such as pipeline materials, contents, pipeline diameter and depth, and the property of overlying soil. Different from the method, the pipeline positioner collects the running angular speed and acceleration information by using an inertial measurement unit (Inertial Measurement Unit, IMU) in the running process of the pipeline, and realizes the effective positioning of the pipeline by strapdown inertial calculation. However, because of the cumulative principle errors in low precision mems inertial measurement unit (Micro-Electro-Mechanical Systems IMU, MIMU) strapdown solutions, the necessary external metrology information is typically required to correct the inertial solution cumulative errors to ensure that the pipe positioning errors remain within acceptable limits. In the pipeline positioning process, the mileage wheel speed measurement information is generally used as external measurement information to correct the inertia calculation error. Besides, the known position points on the pipeline can also update the position of the pipeline positioner, so that the accumulation of inertial calculation errors is further suppressed.
In published articles, for example, in a section of "pipeline defect positioning technology based on volume Kalman smooth filtering" published in journal 4 of the university of Shenyang industry, journal of sensing technology, yang Lijian, MIMU and mileage wheel are utilized to form a pipeline defect positioning system, and MIMU and mileage wheel data fusion is realized based on a volume Kalman filtering algorithm; niu Xiao in the university of Wuhan in 2016, journal 29, volume 1, research on feasibility of detection and positioning scheme in small-bore pipeline by MEMS inertial navigation, in one article, a pipeline combined positioning system is constructed based on MIMU, mileage wheel, ground marker and pipeline motion constraint, wherein the used data fusion algorithm is an extended Kalman filter; the application of combined navigation technology in oil and gas pipeline mapping system published in 16 th and 6 th volumes of journal of China inertial technology journal of Beijing automated control equipment institute Yue Bujiang in 2008 is mainly studied in one document, a long oil and gas pipeline mapping system is formed by using a fiber optic gyroscope strapdown inertial navigation system, a mileage wheel and a distance magnetic scale, and the data fusion of the fiber optic gyroscope strapdown inertial navigation system and the mileage wheel is realized based on an L-D improved Kalman filtering algorithm; in 2013, the China pipeline petroleum company Li Rui also effectively fuses a laser gyro strapdown inertial navigation system, a mileage wheel and GPS data based on a Kalman filtering algorithm in a text of an inertial navigation-based pipeline center line measurement method published in a journal 32 of oil and gas storage and transportation and a 9 period so as to construct a pipeline center line measurement system; the Huang Fengrong of the university of Hebei industry in 2018 is mainly used for researching a pipeline positioning method based on a strapdown inertial navigation system, an odometer and a position mark point in a 'pipeline detection precision positioning method based on reverse solution' published in a journal of China society of inertial technology, volume 26 and period 4, wherein a filtering algorithm used by either forward solution or reverse solution is a Kalman filter; the university Yang Lijian of Shenyang industry in 2013 mainly researches a forward volume Kalman filtering algorithm taking the speed of a mileage wheel as an observed quantity and a reverse smoothing two-stage filtering algorithm taking the position of a datum point as a starting point in a pipeline geographic coordinate internal detection mileage correction algorithm published in a journal of instrumentation journal 34, 1, and realizes the optimal estimation of pipeline defect geographic coordinates. In summary, the current common pipeline positioning method is mainly based on the combination of the strapdown inertial navigation system and the mileage wheel, and effective data fusion is performed on the two sensor data by means of a related data fusion algorithm, so that the pipeline positioning error is further corrected by using a GPS or a position mark point. However, as the mileage wheel belongs to the contact type speed measuring sensor, when the inner wall of the pipeline is uneven or oil stains exist, the mileage wheel is easy to separate from the contact with the pipe wall, so that the slipping phenomenon occurs, and the unstable speed measuring information of the mileage wheel directly leads to the reduction of the positioning performance of the pipeline. Therefore, the research on the multi-sensor-combination pipeline positioning method capable of inhibiting the influence of the skid of the mileage wheels on the pipeline positioning performance is innovative.
Disclosure of Invention
The invention aims to provide a non-excavation pipeline positioning method based on MIMU/mileage wheel/photoelectric speed measurement module combination.
The aim of the invention is realized by the following technical scheme:
the non-excavation pipeline positioning method based on the MIMU/mileage wheel/photoelectric speed measurement module combination specifically comprises the following steps:
step 1: measuring latitude of pipeline starting point by total stationLongitude lambda 0 And height h 0 ；
Step 2: the pipeline positioning instrument provided with the MIMU, the mileage wheel and the photoelectric speed measuring sensor is arranged at the pipeline inlet to be started, and the latitude obtained in the step 1 is obtainedLongitude lambda 0 And height h 0 Manually binding the sample into a pipeline positioner data processor and completing initial alignment;
step 3: the pipe positioner is pulled by a pulling rope to pass through the pipe to reach the end point of the pipe and is pulled back reversely, so as to obtain sensor data in the forward running process and sensor data in the reverse running process of the pipe positioner along the pipe, wherein the sensor data comprises the angular velocity output of the MIMU gyroscopeMIMU accelerometer specific force output f b Mileage increment delta S of the mileage wheel and displacement increment delta X output by the photoelectric speed measuring sensor;
step 4: angular velocity information obtained in the process of running the pipeline along the pipeline by using the pipeline positioner in step 3Sum of specific force information f b Performing strapdown inertial calculation to obtain a strapdown attitude matrix +.>Strapdown solving speed->Strapdown resolution location
Step 5: calculating to obtain corresponding mileage wheel speed information by using mileage wheel mileage increment delta S and output displacement increment delta X of photoelectric speed measuring sensor obtained in each sampling period in the process of running the pipeline positioning instrument along the pipeline in step 3Speed information of photoelectric speed measuring sensor>The formula is as follows:
wherein T is s Is the sampling period;
step 6: based on the mileage wheel speed information obtained in the step 5 by taking MIMU as a reference systemInformation on the speed of strapdown resolution obtained in step 4 +.>And strapdown gesture matrix->The measurement information in the sub-filter 1 of the federal filter structure is established as follows:
wherein V is DE ,V DN ,V DU Is the mileage wheel speedThe degree information is projected on a navigation coordinate system;
step 7: taking MIMU as a reference system, and based on the speed information of the photoelectric speed measurement sensor obtained in the step 5Information on the speed of strapdown resolution obtained in step 4 +.>And strapdown gesture matrix->The measurement information in the sub-filter 2 of the federal filter structure is established as follows:
wherein V is LE ,V LN ,V LU Projecting the speed information of the photoelectric speed measuring sensor on a navigation coordinate system;
step 8: establishing a state equation and a measurement equation of the sub-filter 1 and the sub-filter 2 in the federal filtering structure;
step 9: initializing a sub-filter 1 and a sub-filter 2 in the federal filtering structure;
step 10: iterative estimation is carried out in the sub-filter 1 and the sub-filter 2 based on maximum correlation entropy M estimation robust Kalman filtering, so as to obtain state vector estimation and estimation error covariance matrixes of the sub-filter 1 and the sub-filter 2; wherein the state vector estimated by the kth sub-filter 1 is estimated as X 1,k The estimated error covariance matrix is P 1,k The state vector estimated by the kth sub-filter 2 is estimated as X 2,k The estimated error covariance matrix is P 2,k ；
Step 11: transmitting the state vector estimated value and the estimated error covariance matrix obtained in the step 10 to a main filter, and carrying out sub-filter data fusion in the main filter by adopting a fusion reset mode so as to obtain a main filter state vectorEstimating an estimation error covariance matrix; wherein the kth main filter state vector estimate X g,k And estimation error covariance matrix P g,k The formula is as follows:
step 12: using the main filter kth step state vector estimate X obtained in step 11 g,k And estimation error covariance matrix P g,k Resetting the kth state vector estimate X in sub-filter 1 1,k Estimation error covariance matrix P 1,k System noise variance matrix Q 1,k The formula is as follows:
at the same time, the kth state vector estimation X of the main filter obtained in step 11 is utilized g,k And estimation error covariance matrix P g,k Resetting the kth state vector estimate X in sub-filter 2 2,k Estimation error covariance matrix P 2,k System noise variance matrix Q 2,k The formula is as follows:
wherein Q is 0 The noise variance matrix of the main filter system can be valued according to experience; beta 1,k ,β 2,k The information of the kth sub-filter 1 and the sub-filter 2 are respectively assigned with factors, and the formula is as follows:
wherein T is 1 ,T 2 The fault alarm threshold values of the sub-filter 1 and the sub-filter 2 can be taken according to experience; Λ type 1,k ,Λ 2,k Respectively sub-filteringThe k-th step fault detection function values of the wave filter 1 and the sub-filter 2 are equal;
step 13: repeating steps 10-12 until all the data obtained in the forward running process of the pipeline positioner along the pipeline are processed, and using all the main filter state vector estimation values X obtained in step 11 g In 1 st, 2 nd and 3 rd dimension elements, i.e. strapdown inertial solution position error delta P E ,δP N ,δP U The MIMU strapdown calculation position information obtained in the feedback correction step 4 is given by the following formula:
corrected position information P E ,P N ,P U Outputting positioning information as forward running pipelines;
step 14: angular velocity information obtained in the process of operating the pipeline positioner in the reverse direction along the pipeline in the step 3 is utilizedSpecific force information f b And repeating the steps 4 to 13 to obtain the positioning information P 'of the reverse running pipeline by the mileage increment delta S of the mileage wheel and the output displacement increment delta X of the photoelectric speed measuring sensor obtained in each sampling period' E ,P′ N ,P′ U And on the basis of this and forward direction operation pipeline positioning information P E ,P N ,P U And carrying out weighted fusion to obtain pipeline positioning information, wherein the formula is as follows:
wherein L is the length of the pipeline; l (L) k The length of the fusion position point from the end point of the pipeline;is a distance L-L from the starting point k Forward running pipe positioning information of length;is far from the end point L of the pipeline k Length of the reverse run pipe positioning information.
Further, the state equation of the federal filter structure sub-filter 1 established in the step 8 is:
wherein, the state vector X calculates the position error delta P by strapdown E ,δP N ,δP U Strapdown calculation speed error δV E ,δV N ,δV U Strapdown resolution misalignment angle phi E ,φ N ,φ U Drift epsilon of gyroscope x ,ε y ,ε z Zero offset of accelerometerThe composition is as follows:
f (t) is a system state transition matrix, G (t) is a system process noise input matrix, W (t) is a system noise vector, and the specific form is as follows:
W(t)＝[w gx w gy w gz w ax w ay w az ] T
i in matrix 3×3 Is a 3×3 dimensional unit array, 0 a×b For an a x b-dimensional zero matrix,for the strapdown gesture matrix obtained in step 4, w gx ,w gy ,w gz ,w ax ,w ay ,w az For gyroscope and accelerometer noise, +.>X is the accelerometer specific force output f b Projection +.>Is an antisymmetric array of->The strapdown gesture matrix obtained in the step 4 can be utilizedOutputting the accelerometer specific force obtained in the step 3 +.>The projection is obtained on a navigation coordinate system, and the formula is as follows:
the state equation of the sub-filter 2 in step 8 is the same as that of the sub-filter 1. The measurement equation of the sub-filter 1 in step 8 is:
Z 1 (t)＝H 1 (t)X(t)+V 1 (t)
wherein, the system measures information Z 1 (t) obtained in step 6, V 1 (t) is the mileage wheel measurement noise, H 1 (t) is a measurement matrix of the specific form:
the measurement equation of the sub-filter 2 in step 8 is:
Z 2 (t)＝H 2 (t)X(t)+V 2 (t)
wherein, the system measures information Z 2 (t) obtained in step 7, V 2 (t) measuring noise by a photoelectric speed measuring sensor, H 2 (t) is a measurement matrix, and the specific form is:
further, the design of the robust kalman filter is as follows, wherein the maximum correlation entropy M in step 10 is estimated as follows:
discretizing the state equation and the measurement equation of the sub-filter obtained in the step 8 respectively to obtain:
wherein phi is k|k-1 ,Γ k-1 ,H k The system state transition matrix, the system process noise input matrix and the measurement matrix are respectively discretized; x is X k ,Z k The system state vector and the measurement vector of the kth step are respectively; w (W) k For the system noise vector, the Gaussian distribution is satisfied, the mean value is zero, and the covariance matrix is Q k ；V k To measure noise vector, the Gaussian distribution is also satisfied, the mean value is zero, and the covariance matrix is R k ；
The k-step filtering iterative calculation steps are as follows:
X k|k-1 ＝Φ k|k-1 X k-1
Ψ k ＝diag[G σ (e k,j )]
X k ＝X k|k-1 -K k (Z k -H k X k|k-1 )
P k ＝(I-K k H k )P k|k-1
wherein X is k|k-1 A state one-step predicted value; p (P) k|k-1 A covariance matrix of the state one-step prediction error; e, e k To normalize the residual error, and e k,j For normalizing residual e k The j-th element of (a); psi k Is a weight matrix;the measurement noise covariance matrix is corrected by the weight matrix; k (K) k Is a filtering gain matrix; p (P) k Estimating an error covariance matrix for the state; kernel function G σ (e k,j ) Selected as a gaussian function, the formula is as follows:
where σ is the kernel bandwidth.
Further, wherein the fault detection function Λ in step 12 1,k ,Λ 2,k The specific calculation formula is as follows:
wherein Z is 1,k ,H 1,k ,X 1,k|k-1 ,R 1,k ,P 1,k|k-1 The k-th measurement vector, the measurement matrix, the sub-filter 1,A state one-step prediction value, a measurement noise covariance matrix and a state one-step prediction error covariance matrix; similarly, Z 2,k ,H 2,k ,X 2,k|k-1 ,R 2,k ,P 2,k|k-1 The kth step measurement vector, the measurement matrix, the state one-step prediction value, the measurement noise covariance matrix and the state one-step prediction error covariance matrix in the sub-filter 2 are respectively.
The invention has the beneficial effects that:
the invention provides a method for solving the problem that the pipeline positioning performance is reduced due to the fact that a mileage wheel slips, measurement information fails and inertia resolving errors accumulate in the positioning process of a pipeline positioning instrument, the method is combined with a mileage wheel to achieve the purpose of redundant speed measurement by utilizing the characteristics of non-contact speed measurement and no slip of a photoelectric speed measurement sensor, and the method is used for achieving effective data fusion of MIMU, the mileage wheel and information of the photoelectric speed measurement sensor by utilizing a federal Kalman filtering algorithm based on maximum correlation entropy M estimation on the basis, so that the problem that the pipeline positioning performance is reduced due to unstable speed measurement information of the mileage wheel is solved.
In order to verify the beneficial effects of the invention, the non-excavation pipeline positioning method based on the MIMU/mileage wheel/photoelectric speed sensor combination and the MIMU/mileage wheel combination positioning method based on a conventional Kalman Filter (KF) algorithm are subjected to simulation comparison. In the simulation process, the straight pipeline, the bent pipeline and the deep pipeline track existing in the urban underground pipeline are simulated, the simulated underground pipeline track is 140m long, the pipeline positioning instrument is pulled through the pipeline at a constant speed of 0.4m/s to acquire data, and the sampling frequency is 100HZ. Setting the zero offset of the MIMU gyroscope to be 12 degrees/h, and setting the white noise of the gyroscope to be 12 degrees/hZero offset of accelerometer is 0.075mg, white noise of accelerometer is +.>The scale coefficient of the mileage wheel is 0.995, and the scale coefficient of the photoelectric speed measuring sensor is 0.98. In order to simulate the skidding phenomenon of the mileage wheel in the working process of the pipeline positioner, the simulation is carried outThe output of the mileage wheel is set to zero for 5s at 100s and 200s respectively.
As can be seen from fig. 2, when the mileage wheel slips, the MIMU/mileage wheel combination positioning error based on the conventional KF algorithm increases significantly, the eastern direction mean square error reaches 1.2053m, the north direction mean square error reaches 2.4018m, and the daily direction mean square error is 0.1066m. According to the MIMU/mileage wheel/photoelectric speed sensor combination positioning method, when the mileage wheel slips, the photoelectric speed sensor can still output correct speed measurement speed information to correct the inertial navigation system, so that positioning errors caused by the slippage of the mileage wheel can be effectively reduced, the eastern mean square error is 0.0919m, the north mean square error is 0.0560m, and the daily mean square error is 0.0566m. Simulation results show that the MIMU/mileage wheel/photoelectric speed sensor combined positioning method can effectively reduce positioning errors caused by slippage of mileage wheels, and compared with the MIMU/mileage wheel combined positioning method, the eastern positioning accuracy of a pipeline is improved by 92.3%, the northbound positioning accuracy is improved by 97.6%, and the daily positioning accuracy is improved by 46.9%.
Drawings
FIG. 1 is a flow chart of an implementation of a non-excavation pipeline positioning method based on a MIMU/odometer wheel/photoelectric velocimetry sensor combination of the present invention;
FIG. 2 is a graph comparing MIMU/mileage wheel combination positioning effect based on a conventional KF algorithm with MIMU/mileage wheel/photoelectric speed sensor combination positioning result based on the present invention;
fig. 3 is a comparison result of the MIMU/mileage wheel combination positioning error based on the conventional KF algorithm and the MIMU/mileage wheel/photoelectric speed sensor combination positioning error based on the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Step 1: measuring latitude of pipeline starting point by total stationLongitude lambda 0 And height h 0 ；
Step 2: will install MIMU, mileage wheel and photoelectricity speed sensorThe pipeline positioner is arranged at the pipeline inlet to start, and the latitude obtained in the step 1 is obtainedLongitude lambda 0 And height h 0 Manually binding the sample into a pipeline positioner data processor and completing initial alignment;
step 3: the pipe positioner is pulled by a pulling rope to pass through the pipe to reach the end point of the pipe and is pulled back reversely, so as to obtain sensor data in the forward running process and sensor data in the reverse running process of the pipe positioner along the pipe, wherein the sensor data comprises the angular velocity output of the MIMU gyroscopeMIMU accelerometer specific force output f b Mileage increment delta S of the mileage wheel and displacement increment delta X output by the photoelectric speed measuring sensor;
step 4: angular velocity information obtained in the process of running the pipeline along the pipeline by using the pipeline positioner in step 3Sum of specific force information f b Performing strapdown inertial calculation to obtain a strapdown attitude matrix +.>Strapdown solving speed->Strapdown resolution position +.>
Step 5: calculating to obtain corresponding mileage wheel speed information by using mileage wheel mileage increment delta S and output displacement increment delta X of photoelectric speed measuring sensor obtained in each sampling period in the process of running the pipeline positioning instrument along the pipeline in step 3And photoelectric speed measurement and transmissionSensor speed information +.>I.e.
Wherein: t (T) s Is the sampling period;
step 6: based on the mileage wheel speed information obtained in the step 5 by taking MIMU as a reference systemInformation on the speed of strapdown resolution obtained in step 4 +.>And strapdown gesture matrix->Establishing measurement information in the federal filter structure sub-filter 1, i.e
Wherein: v (V) DE ,V DN ,V DU Projecting the mileage speed information on a navigation coordinate system;
step 7: taking MIMU as a reference system, and based on the speed information of the photoelectric speed measurement sensor obtained in the step 5Information on the speed of strapdown resolution obtained in step 4 +.>And strapdown gesture matrix->Establishing a sub-filter of a federal filtering structure2, i.e. the measurement information in
Wherein: v (V) LE ,V LN ,V LU Projecting the speed information of the photoelectric speed measuring sensor on a navigation coordinate system;
step 8: the method comprises the steps of establishing a state equation of a neutron filter 1 in a federal filtering structure, wherein the specific form is as follows:
wherein: the state vector X solves for the position error delta P by strapdown E ,δP N ,δP U Strapdown calculation speed error δV E ,δV N ,δV U Strapdown resolution misalignment angle phi E ,φ N ,φ U Drift epsilon of gyroscope x ,ε y ,ε z Zero offset of accelerometerComposition, i.e
F (t) is a system state transition matrix, G (t) is a system process noise input matrix, W (t) is a system noise vector, and the specific form is as follows:
W(t)＝[w gx w gy w gz w ax w ay w az ] T
i in matrix 3×3 Is a 3×3 dimensional unit array, 0 a×b For an a x b-dimensional zero matrix,for the strapdown gesture matrix obtained in step 4, w gx ,w gy ,w gz ,w ax ,w ay ,w az For gyroscope and accelerometer noise, +.>X is the accelerometer specific force output f b Projection +.>Is an antisymmetric array of->The strapdown gesture matrix obtained in the step 4 can be utilizedOutputting the accelerometer specific force obtained in the step 3 +.>Projection onto a navigational coordinate system, i.e
The state equation of the sub-filter 2 of the federal filter structure is established to be the same as that of the sub-filter 1. Further, a measurement equation of the neutron filter 1 in the federal filter structure is established, and the concrete form is as follows:
Z 1 (t)＝H 1 (t)X(t)+V 1 (t)
wherein: system measurement information Z 1 (t) obtained in step 6, V 1 (t) is the mileage wheel measurement noise, H 1 (t) is a measurement matrix, and the specific form is:
the measuring equation of the neutron filter 2 in the federal filter structure is established, and the concrete form is as follows:
Z 2 (t)＝H 2 (t)X(t)+V 2 (t)
wherein: system measurement information Z 2 (t) obtained in step 7, V 2 (t) measuring noise by a photoelectric speed measuring sensor, H 2 (t) is a measurement matrix, and the specific form is:
step 9: initializing a sub-filter 1 and a sub-filter 2 in the federal filtering structure;
step 10: iterative estimation is carried out in the sub-filter 1 and the sub-filter 2 based on maximum correlation entropy M estimation robust Kalman filtering, so as to obtain state vector estimation and estimation error covariance matrixes of the sub-filter 1 and the sub-filter 2; wherein the state vector estimated by the kth sub-filter 1 is estimated as X 1,k The estimated error covariance matrix is P 1,k The state vector estimated by the kth sub-filter 2 is estimated as X 2,k The estimated error covariance matrix is P 2,k ；
The maximum correlation entropy M estimation robust Kalman filter is designed as follows: discretizing the state equation and the measurement equation of the sub-filter obtained in the step 8 respectively to obtain:
wherein: phi (phi) k/k-1 ,Γ k-1 ,H k The system state transition matrix, the system process noise input matrix and the measurement matrix are respectively discretized; x is X k ,Z k The system state vector and the measurement vector of the kth step are respectively; w (W) k For the system noise vector, the Gaussian distribution is satisfied, the mean value is zero, and the covariance matrix is Q k ；V k To measure noise vector, the Gaussian distribution is also satisfied, the mean value is zero, and the covariance matrix is R k ；
The k-step filtering iterative calculation steps are as follows:
X k|k-1 ＝Φ k|k-1 X k-1
X k ＝X k|k-1 -K k (Z k -H k X k|k-1 )
P k ＝(I-K k H k )P k|k-1
wherein: x is X k|k-1 A state one-step predicted value; p (P) k|k-1 A covariance matrix of the state one-step prediction error; e, e k To normalize the residual error, and e k,j For normalizing residual e k The j-th element of (a); psi k Is a weight matrix;the measurement noise covariance matrix is corrected by the weight matrix; k (K) k Is a filtering gain matrix; p (P) k Estimating an error covariance matrix for the state; kernel function G σ (e k,j ) Selected as a Gaussian function, i.e
Wherein: sigma is kernel bandwidth;
step 11: and (3) transmitting the state vector estimated value and the estimated error covariance matrix obtained in the step (10) to a main filter, and carrying out sub-filter data fusion in the main filter by adopting a fusion reset mode so as to obtain the state vector estimated value and the estimated error covariance matrix of the main filter. Wherein the kth main filter state vector estimate X g,k And estimation error covariance matrix P g,k The specific calculation formula is as follows:
step 12: using the main filter kth step state vector estimate X obtained in step 11 g,k And estimation error covariance matrix P g,k Resetting the kth state vector estimate X in sub-filter 1 1,k Estimation error covariance matrix P 1,k System noise variance matrix Q 1,k I.e.
At the same time, the kth state vector estimation X of the main filter obtained in step 11 is utilized g,k And estimation error covariance matrix P g,k Resetting the kth state vector estimate X in sub-filter 2 2,k Estimation error covariance matrix P 2,k System noise variance matrix Q 2,k I.e.
Wherein: q (Q) 0 The noise variance matrix of the main filter system can be valued according to experience; beta 1,k ,β 2,k Information distribution for the k-th sub-filter 1 and sub-filter 2, respectivelyThe specific calculation formula of the factor is as follows:
wherein: t (T) 1 ,T 2 The fault alarm threshold values of the sub-filter 1 and the sub-filter 2 can be taken according to experience; Λ type 1,k ,Λ 2,k The values of the k-th fault detection function values of the sub-filter 1 and the sub-filter 2 are respectively shown in the following specific calculation formulas:
wherein: z is Z 1,k ,H 1,k ,X 1,k|k-1 ,R 1,k ,P 1,k|k-1 The k-th step measurement vector, the measurement matrix, the state one-step predicted value, the measurement noise covariance matrix and the state one-step prediction error covariance matrix in the sub-filter 1 are respectively; similarly, Z 2,k ,H 2,k ,X 2,k|k-1 ,R 2,k ,P 2,k|k-1 The k-th step measurement vector, the measurement matrix, the state one-step predicted value, the measurement noise covariance matrix and the state one-step prediction error covariance matrix in the sub-filter 2 are respectively;
step 13: repeating the steps 10-12 until all the data obtained in the forward running process of the pipeline positioner along the pipeline are processed, and estimating X by using all the main filter state vectors obtained in the step 11 g In 1 st, 2 nd and 3 rd dimension elements, i.e. strapdown inertial solution position error delta P E ,δP N ,δP U Feedback correction of MIMU strapdown resolved position information obtained in step 4, i.e
Corrected position information P E ,P N ,P U Outputting positioning information as forward running pipelines;
step 14: angular velocity information obtained in the process of operating the pipeline positioner in the reverse direction along the pipeline in the step 3 is utilizedSpecific force information f b And repeating the steps 4 to 13 to obtain the positioning information P 'of the reverse running pipeline by the mileage increment delta S of the mileage wheel and the output displacement increment delta X of the photoelectric speed measuring sensor obtained in each sampling period' E ,P′ N ,P′ U And on the basis of this and forward direction operation pipeline positioning information P E ,P N ,P U Obtaining pipeline positioning information by weighting and fusing, namely
Wherein: l is the length of the pipeline; l (L) k The length of the fusion position point from the end point of the pipeline;is a distance L-L from the starting point k Forward running pipe positioning information of length;is far from the end point L of the pipeline k Length of the reverse run pipe positioning information.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A non-excavation pipeline positioning method based on MIMU/mileage wheel/photoelectric speed sensor combination is characterized in that: the method specifically comprises the following steps:
step 1: measuring latitude of pipeline starting point by total stationLongitude lambda 0 And height h 0 ；
Step 2: the pipeline positioning instrument provided with the MIMU, the mileage wheel and the photoelectric speed measuring sensor is arranged at the pipeline inlet to be started, and the latitude obtained in the step 1 is obtainedLongitude lambda 0 And height h 0 Manually binding the sample into a pipeline positioner data processor and completing initial alignment;
step 3: the pipe positioner is pulled by a pulling rope to pass through the pipe to reach the end point of the pipe and is pulled back reversely, so as to obtain sensor data in the forward running process and sensor data in the reverse running process of the pipe positioner along the pipe, wherein the sensor data comprises the angular velocity output of the MIMU gyroscopeMIMU accelerometer specific force output f b Mileage increment delta S of the mileage wheel and displacement increment delta X output by the photoelectric speed measuring sensor;
step 4: angular velocity information obtained in the process of running the pipeline along the pipeline by using the pipeline positioner in step 3Sum of specific force information f b Performing strapdown inertial calculation to obtain a strapdown attitude matrix +.>Strapdown solving speed->Strapdown resolution location
Step 5: calculating to obtain corresponding mileage wheel speed information by using mileage wheel mileage increment delta S and output displacement increment delta X of photoelectric speed measuring sensor obtained in each sampling period in the process of running the pipeline positioning instrument along the pipeline in step 3Speed information of photoelectric speed measuring sensor>The formula is as follows:
wherein T is s Is the sampling period;
step 6: based on the mileage wheel speed information obtained in the step 5 by taking MIMU as a reference systemInformation on the speed of strapdown resolution obtained in step 4 +.>And strapdown gesture matrix->The measurement information in the sub-filter 1 of the federal filter structure is established as follows:
wherein V is DE ，V DN ，V DU Projecting the mileage speed information on a navigation coordinate system;
step 7: taking MIMU as a reference system, and based on the speed information of the photoelectric speed measurement sensor obtained in the step 5Information on the speed of strapdown resolution obtained in step 4 +.>And strapdown gesture matrix->The measurement information in the sub-filter 2 of the federal filter structure is established as follows:
wherein V is LE ，V LN ，V LU Projecting the speed information of the photoelectric speed measuring sensor on a navigation coordinate system;
step 8: establishing a state equation and a measurement equation of the sub-filter 1 and the sub-filter 2 in the federal filtering structure;
step 9: initializing a sub-filter 1 and a sub-filter 2 in the federal filtering structure;
step 10: iterative estimation is carried out in the sub-filter 1 and the sub-filter 2 based on maximum correlation entropy M estimation robust Kalman filtering, so as to obtain state vector estimation and estimation error covariance matrixes of the sub-filter 1 and the sub-filter 2; wherein the state vector estimated by the kth sub-filter 1 is estimated as X 1，k The estimated error covariance matrix is P 1，k The state vector estimated by the kth sub-filter 2 is estimated as X 2，k The estimated error covariance matrix is P 2，k ；
Step 11: transmitting the state vector estimated value obtained in the step 10 and the estimated error covariance matrix to a main filter, and carrying out sub-filter data fusion in the main filter by adopting a fusion reset mode so as to obtain the state vector estimated value of the main filterCovariance matrix with estimation error; wherein the kth main filter state vector estimate X g，k And estimation error covariance matrix P g，k The formula is as follows:
step 12: using the main filter kth step state vector estimate X obtained in step 11 g，k And estimation error covariance matrix P g，k Resetting the kth state vector estimate X in sub-filter 1 1，k Estimation error covariance matrix P 1，k System noise variance matrix Q 1，k The formula is as follows:
at the same time, the kth state vector estimation X of the main filter obtained in step 11 is utilized g，k And estimation error covariance matrix P g，k Resetting the kth state vector estimate X in sub-filter 2 2，k Estimation error covariance matrix P 2，k System noise variance matrix Q 2，k The formula is as follows:
wherein Q is 0 The noise variance matrix of the main filter system can be valued according to experience; beta 1，k ，β 2，k The information of the kth sub-filter 1 and the sub-filter 2 are respectively assigned with factors, and the formula is as follows:
wherein T is 1 ，T 2 Fault alarm for sub-filter 1 and sub-filter 2 respectivelyThe threshold value can be valued according to experience; Λ type 1，k ，Λ 2，k The values of the k-th fault detection function of the sub-filter 1 and the sub-filter 2 are respectively;
step 13: repeating steps 10-12 until all the data obtained in the forward running process of the pipeline positioner along the pipeline are processed, and using all the main filter state vector estimation values X obtained in step 11 g In 1 st, 2 nd and 3 rd dimension elements, i.e. strapdown inertial solution position error delta P E ，δP N ，δP U The MIMU strapdown calculation position information obtained in the feedback correction step 4 is given by the following formula:
corrected position information P E ，P N ，P U Outputting positioning information as forward running pipelines;
step 14: angular velocity information obtained in the process of operating the pipeline positioner in the reverse direction along the pipeline in the step 3 is utilizedSpecific force information f b And repeating the steps 4 to 13 to obtain the positioning information P 'of the reverse running pipeline by the mileage increment delta S of the mileage wheel and the output displacement increment delta X of the photoelectric speed measuring sensor obtained in each sampling period' E ，P′ N ，P′ U And on the basis of this and forward direction operation pipeline positioning information P E ，P N ，P U And carrying out weighted fusion to obtain pipeline positioning information, wherein the formula is as follows:
wherein L is the length of the pipeline; l (L) k The length of the fusion position point from the end point of the pipeline;is a distance L-L from the starting point k Forward running pipe positioning information of length; />Is far from the end point L of the pipeline k Length of the reverse run pipe positioning information.
2. The non-excavation pipeline positioning method based on the MIMU/mileage wheel/photoelectric speed sensor combination according to claim 1, wherein the method comprises the following steps: the state equation of the federal filter structure sub-filter 1 established in the step 8 is as follows:
wherein, the state vector X calculates the position error delta P by strapdown E ，δP N ，δP U Strapdown calculation speed error δV E ，δV N ，δV U Strapdown resolution misalignment angle phi E ，φ N ，φ U Drift epsilon of gyroscope x ，ε y ，ε z Zero offset of accelerometerThe composition is as follows:
f (t) is a system state transition matrix, G (t) is a system process noise input matrix, W (t) is a system noise vector, and the specific form is as follows:
W(t)＝[w gx w gy w gz w ax w ay w az ] T
i in matrix 3×3 Is a 3×3 dimensional unit array, 0 a×b For an a x b-dimensional zero matrix,for the strapdown gesture matrix obtained in step 4, w gx ，w gy ，w gz ，w ax ，w ay ，w az For gyroscope and accelerometer noise, +.>Specific force output f for accelerometer b Projection +.>Is an antisymmetric array of->The strapdown gesture matrix obtained in step 4 can be used +.>Outputting the accelerometer specific force obtained in the step 3 +.>The projection is obtained on a navigation coordinate system, and the formula is as follows:
the state equation of the sub-filter 2 in step 8 is the same as that of the sub-filter 1. The measurement equation of the sub-filter 1 in step 8 is:
Z 1 (t)＝H 1 (t)X(t)+V 1 (t)
wherein, the system measures information Z 1 (t) obtained in step 6, V 1 (t) is mileage wheel measurementNoise, H 1 (t) is a measurement matrix of the specific form:
the measurement equation of the sub-filter 2 in step 8 is:
Z 2 (t)＝H 2 (t)X(t)+V 2 (t)
wherein, the system measures information Z 2 (t) obtained in step 7, V 2 (t) measuring noise by a photoelectric speed measuring sensor, H 2 (t) is a measurement matrix, and the specific form is:
3. the non-excavation pipeline positioning method based on the MIMU/mileage wheel/photoelectric speed sensor combination according to claim 1, wherein the method comprises the following steps: the design of the maximum correlation entropy M estimation robust Kalman filter in the step 10 is as follows:
discretizing the state equation and the measurement equation of the sub-filter obtained in the step 8 respectively to obtain:
wherein phi is k|k-1 ，Γ k-1 ，H k The system state transition matrix, the system process noise input matrix and the measurement matrix are respectively discretized; x is X k ，Z k The system state vector and the measurement vector of the kth step are respectively; w (W) k For the system noise vector, the Gaussian distribution is satisfied, the mean value is zero, and the covariance matrix is Q k ；V k To measure noise vector, the Gaussian distribution is also satisfied, the mean value is zero, and the covariance matrix is R k ；
The k-step filtering iterative calculation steps are as follows:
X k|k-1 ＝Φ k|k-1 X k-1
Ψ k ＝diag[G σ (e k，j )]
X k ＝X k|k-1 -K k (Z k -H k X k|k-1 )
P k ＝(I-K k H k )P k|k-1
wherein X is k|k-1 A state one-step predicted value; p (P) k|k-1 A covariance matrix of the state one-step prediction error; e, e k To normalize the residual error, and e k，j For normalizing residual e k The j-th element of (a); psi k Is a weight matrix;the measurement noise covariance matrix is corrected by the weight matrix; k (K) k Is a filtering gain matrix; p (P) k Estimating an error covariance matrix for the state; kernel function G σ (e k，j ) Selected as a gaussian function, the formula is as follows:
where σ is the kernel bandwidth.
4. The non-excavation pipeline positioning method based on the MIMU/mileage wheel/photoelectric speed sensor combination according to claim 1, wherein the method comprises the following steps: wherein the fault detection function Λ in step 12 1，k ，Λ 2，k The specific calculation formula is as follows:
wherein Z is 1，k ，H 1，k ，X 1，k|k-1 ，R 1，k ，P 1，k|k-1 The k-th step measurement vector, the measurement matrix, the state one-step predicted value, the measurement noise covariance matrix and the state one-step prediction error covariance matrix in the sub-filter 1 are respectively; similarly, Z 2，k ，H 2，k ，X 2，k|k-1 ，R 2，k ，P 2，k|k-1 The kth step measurement vector, the measurement matrix, the state one-step prediction value, the measurement noise covariance matrix and the state one-step prediction error covariance matrix in the sub-filter 2 are respectively.
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