CN114909608A - Trenchless pipeline positioning method based on MIMU/mile wheel/photoelectric speed measurement sensor combination - Google Patents
Trenchless pipeline positioning method based on MIMU/mile wheel/photoelectric speed measurement sensor 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
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- F16L55/26—Pigs or moles, i.e. devices movable in a pipe or conduit with or without self-contained propulsion means
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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
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- 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
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Abstract
The invention discloses a non-excavation pipeline positioning method based on an MIMU (micro inertial measurement Unit)/mileage wheel/photoelectric speed measurement sensor combination. The invention utilizes the non-contact speed measurement and non-skid characteristics of the photoelectric speed measurement sensor to combine the photoelectric speed measurement sensor with the odometer to realize the purpose of redundant speed measurement, and utilizes the Federal Kalman filtering algorithm based on the maximum correlation entropy M estimation to realize the effective data fusion of the MIMU, the odometer wheel and the photoelectric speed measurement sensor information on the basis, thereby solving the problem of pipeline positioning performance reduction caused by unstable odometer wheel speed measurement information. The invention effectively reduces the positioning error caused by the slipping of the mileage wheel.
Description
Technical Field
The invention belongs to the field of underground pipeline geographical position information measurement, and particularly relates to a non-excavation pipeline positioning method based on MIMU/mileage wheel/photoelectric speed measurement sensor combination.
Background
The underground pipeline is an important component of urban infrastructure and has important functions of information, energy, water heating resource circulation, waste discharge and the like in cities. Due to the continuous development of urbanization, the number of underground pipelines is increasing, and the distribution of pipelines is more and more complicated. This brings difficulty to the use and maintenance of underground pipelines and the development of urban underground engineering. Therefore, it is necessary to establish an accurate three-dimensional map for the urban underground pipelines to prevent the pipelines from being damaged by excavation, construction and other projects. At present, common nondestructive pipeline positioning technologies, including ground penetrating radar positioning method, multi-frequency electromagnetic positioning method and magnetometer positioning method, have limitations, and positioning performance is limited by various factors such as pipeline materials, contents, pipeline diameter and depth and properties of overlying soil. Different from the method, the pipeline positioning instrument acquires the operation angular velocity and acceleration information by using an Inertial Measurement Unit (IMU) in the operation process of the pipeline, and realizes effective positioning of the pipeline through strapdown Inertial solution. However, because the accumulated principle error exists in the Micro-Electro-Mechanical Systems IMU (MIMU) strapdown solution, the accumulated error is usually corrected by the necessary external measurement information, so as to ensure that the positioning error of the pipeline is kept within the acceptable range. In the process of positioning the pipeline, the inertial calculation error is usually corrected by using the odometer wheel speed measurement information as external measurement information. Besides, the known position points on the pipeline can also update the position of the pipeline positioning instrument, and further inhibit the accumulation of inertial calculation errors.
In published articles, for example, in the article "pipe defect positioning technology based on volume kalman smoothing filter", published in journal 4 of "journal of the science of sensing technology" by the Yang theory of Shenyang industry university in 2015, a pipe defect positioning system is formed by using MIMU and a mileage wheel, and the data fusion of the MIMU and the mileage wheel is realized based on a volume kalman filter algorithm; a pipeline combined positioning system is constructed in a text of detection positioning scheme feasibility research in a small-caliber pipeline by MEMS inertial navigation, which is published by a bovine Xiaojie of Wuhan university in 2016 in journal of technical journal of sensing, volume 29, and phase 1, wherein a used data fusion algorithm is an extended Kalman filter; the application of the integrated navigation technology in the oil and gas pipeline surveying and mapping system, published by the research institute of Beijing automated control equipment in 2008, Chaojiang in the journal of China journal of the technical university, volume 16 and phase 6, is mainly researched and utilized to form a long-distance oil and gas pipeline surveying and mapping system by using a fiber-optic gyroscope strapdown inertial navigation system, a mileage wheel and a fixed-distance magnetic marker, and realize the data fusion of the fiber-optic gyroscope strapdown inertial navigation system and the mileage wheel based on an L-D improved Kalman filtering algorithm; in a text of pipeline center line measurement method based on inertial navigation, which is published by the Chinese pipeline petroleum company, lie ui in journal, volume 32, volume 9, of oil and gas storage and transportation in 2013, a laser gyro strapdown inertial navigation system, a mileage wheel and GPS data are effectively fused based on a Kalman filtering algorithm so as to construct a pipeline center line measurement system; in a text entitled "pipeline detection high-precision positioning method based on reverse solution", published in journal of volume 26, 4 of "Chinese journal of inertial technology academic, by Huangfengrong, Hebei Industrial university in 2018, a pipeline positioning method based on a strapdown inertial navigation system, a odometer and position mark points is mainly researched, wherein a filtering algorithm used by both forward solution and reverse solution is a Kalman filter; the Yang governing practice of Shenyang industry university in 2013 mainly researches a forward volume Kalman filtering algorithm taking a mileage wheel speed as an observed quantity and a backward smoothing two-stage filtering algorithm taking a datum point position as a starting point in a mileage correction algorithm for detection in a pipeline geographic coordinate published in journal No. 34 and No. 1 of Instrument and Meter journal, so as to realize optimal estimation on a pipeline defect geographic coordinate. In summary, the conventional pipeline positioning method is mainly based on the combination of a strapdown inertial navigation system and a mileage wheel, and performs effective data fusion on data of two sensors by using a relevant data fusion algorithm, and further corrects a pipeline positioning error by using a GPS or a position mark point on the basis. However, because the mileage wheel belongs to a contact type speed measurement sensor, when the inner wall of the pipeline is uneven or oil stains exist, the mileage wheel is easy to be separated from the pipeline wall so as to cause a slipping phenomenon, and unstable speed measurement information of the mileage wheel directly causes the reduction of the positioning performance of the pipeline. Therefore, the method for positioning the multi-sensor combined pipeline, which can inhibit the influence of the slipping of the mileage wheel on the positioning performance of the pipeline, is innovative.
Disclosure of Invention
The invention aims to provide a trenchless pipeline positioning method based on MIMU/odometer wheel/photoelectric speed measuring sensor combination.
The purpose of the invention is realized by the following technical scheme:
a trenchless pipeline positioning method based on MIMU/mile wheel/photoelectric speed measurement sensor combination specifically comprises the following steps:
step 1: measuring the latitude of the starting point of the pipeline by using a total stationLongitude λ 0 And height h 0 ;
Step 2: placing the pipeline positioning instrument provided with the MIMU, the mileage wheel and the photoelectric speed measuring sensor at the inlet of the pipeline to start, and placing the latitude obtained in the step 1Longitude λ 0 And height h 0 Manually binding the target object into a data processor of a pipeline locator and finishing initial alignment;
and step 3: pulling the pipeline locator to pass through the pipeline to reach the pipeline end point by using a pull rope and pulling back the pipeline locator reversely to obtain sensor data of the pipeline locator in the forward running process and sensor data in the reverse running process, wherein the sensor data comprises angular speed output of an MIMU gyroscopeMIMU accelerometer specific force output f b Mileage wheel mileage increment Δ S anddisplacement increment delta X output by the photoelectric speed measuring sensor;
and 4, step 4: utilizing the angular velocity information obtained in the process that the pipeline locator in the step 3 runs along the pipelineSum specific force information f b Performing strapdown inertial solution to obtain a strapdown attitude matrixSpeed of strapdown solutionAnd strapdown resolving the location
And 5: calculating to obtain corresponding odometer speed information by using the odometer wheel mileage increment delta S obtained in each sampling period in the process of running the pipeline locator along the pipeline in the step 3 and the output displacement increment delta X of the photoelectric speed measuring sensorSpeed information of photoelectric speed measuring sensorThe formula is as follows:
wherein, T s Is a sampling period;
step 6: using MIMU as reference system, and based on the obtained odometer speed information in step 5And the strapdown resolving speed information obtained in the step 4With strapdown attitude matrixEstablishing measurement information in the federal filter structure subfilter 1, wherein the formula is as follows:
wherein, V DE ,V DN ,V DU Projecting the speed information of the mileage wheel in a navigation coordinate system;
and 7: taking the MIMU as a reference system, and based on the speed information of the photoelectric speed measuring sensor obtained in the step 5And the strapdown resolving speed information obtained in the step 4With strapdown attitude matrixEstablishing measurement information in the federal filter structure sub-filter 2, wherein the formula is as follows:
wherein, V LE ,V LN ,V LU Projecting speed information of the photoelectric speed measurement sensor in a navigation coordinate system;
and step 8: establishing a state equation and a measurement equation of a sub-filter 1 and a sub-filter 2 in a federal filter structure;
and step 9: initializing a sub-filter 1 and a sub-filter 2 in a federated filtering structure;
step 10: performing iterative estimation in the sub-filters 1 and 2 based on the maximum correlation entropy M estimation robust Kalman filtering to obtain state vector estimation values and estimation values of the sub-filters 1 and 2An error covariance matrix; wherein, the state vector estimated by the k-th sub-filter 1 is estimated to be X 1,k The covariance matrix of the estimation error is P 1,k The state vector estimated by the k-th sub-filter 2 is estimated as X 2,k The covariance matrix of the estimation error 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 performing sub-filter data fusion in the main filter by adopting a fusion reset mode so as to obtain a state vector estimated value and an estimated error covariance matrix of the main filter; wherein, the k-th main filter state vector estimation value X g,k And estimation error covariance matrix P g,k The formula is as follows:
step 12: using the k-th state vector estimate X of the main filter obtained in step 11 g,k And estimation error covariance matrix P g,k Resetting state vector estimate X in step k in sub-filter 1 1,k Estimating an error covariance matrix P 1,k And the system noise variance matrix Q 1,k The formula is as follows:
at the same time, the k-th state vector estimation X of the main filter obtained in step 11 is used g,k And estimation error covariance matrix P g,k Resetting the state vector estimate X of step k in sub-filter 2 2,k Estimating an error covariance matrix P 2,k And the system noise variance matrix Q 2,k The formula is as follows:
wherein Q is 0 As a main filter system noise variance matrix, can be empirically derivedTaking values; beta is a 1,k ,β 2,k The information of the k-th step sub-filter 1 and sub-filter 2 is assigned with factors, respectively, as follows:
wherein, T 1 ,T 2 The failure alarm threshold values of the sub-filter 1 and the sub-filter 2 are respectively selected according to experience; lambda 1,k ,Λ 2,k The k-th fault detection function values of the sub-filter 1 and the sub-filter 2 are respectively obtained;
step 13: repeating the steps 10 to 12 until all the data obtained in the process of the pipeline positioning instrument running along the pipeline in the forward direction are processed, and utilizing all the main filter state vector estimated values X obtained in the step 11 g Middle 1, 2, 3-dimensional element, namely strapdown inertial solution position error deltaP E ,δP N ,δP U And (3) feeding back the MIMU strapdown resolving position information obtained in the correction step 4, wherein the formula is as follows:
the corrected position information P E ,P N ,P U Outputting as the positioning information of the forward running pipeline;
step 14: utilizing the angular velocity information obtained in the process that the pipeline locator in the step 3 runs along the pipeline in the reverse directionSpecific force information f b And obtaining mileage increment delta S of the mileage wheel and displacement increment delta X output by the photoelectric speed measuring sensor in each sampling period, and repeating the steps from 4 to 13 to obtain positioning information P 'of the reverse running pipeline' E ,P′ N ,P′ U And on the basis of the above-mentioned information P for positioning pipeline E ,P N ,P U Carrying out weighting fusion to obtain pipeline positioning information, wherein the formula is as follows:
wherein L is the length of the pipeline; l is k The fusion position point is away from the pipeline end point length;is a distance starting point L-L k Forward running pipe location information for length;is a distance L from the end point of the pipeline k Length of the counter-running pipe location information.
Further, the state equation of the federal filter structure subfilter 1 established in step 8 is as follows:
wherein the state vector X is solved for the position error deltaP by strapdown E ,δP N ,δP U Strapdown resolving speed error delta V E ,δV N ,δV U Strapdown solution misalignment angle phi E ,φ N ,φ U Gyro drift epsilon x ,ε y ,ε z And accelerometer zero offsetComposition, the formula 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
in matrix I 3×3 Is a 3 × 3 dimensional unit array, 0 a×b Is a zero matrix with a dimension of a multiplied by b,for the strapdown attitude matrix, w, obtained in step 4 gx ,w gy ,w gz ,w ax ,w ay ,w az Is the noise of the gyroscope and the accelerometer,for specific force output f of accelerometer b Projecting in a navigational coordinate systemAn antisymmetric array ofThe strapdown attitude matrix obtained in step 4 can be utilizedOutputting the specific force of the accelerometer obtained in the step 3Projected to the navigational coordinate system, the formula is as follows:
the state equation of the sub-filter 2 in step 8 is the same as the state equation of the sub-filter 1. In step 8, the measurement equation of the neutron filter 1 is as follows:
Z 1 (t)=H 1 (t)X(t)+V 1 (t)
wherein, the system measurement information Z 1 (t) obtained from step 6, V 1 (t) Mileage wheel measurement noise, H 1 (t) is a specific form of the measurement matrix:
in step 8, the measurement equation of the neutron filter 2 is as follows:
Z 2 (t)=H 2 (t)X(t)+V 2 (t)
wherein, the system measurement information Z 2 (t) obtained from step 7, V 2 (t) is the measurement noise of the photoelectric speed measuring sensor, H 2 (t) is a measurement matrix, and the specific form is as follows:
further, the maximum correlation entropy M estimation robust kalman filter in step 10 is designed as follows:
respectively discretizing the sub-filter state equation and the measurement equation obtained in the step 8 to obtain:
wherein phi k|k-1 ,Γ k-1 ,H k Respectively a discretized system state transition matrix, a discretized system process noise input matrix and a discretized measurement matrix; x k ,Z k Respectively a system state vector and a measurement vector in the k step; w k Is a system noise vector, satisfies Gaussian distribution, has a mean of zero and a covariance matrix of Q k ;V k For measuring the noise vector, a Gaussian distribution is also satisfied, the mean value is zero, and the covariance matrix is R k ;
The k filtering iterative computation step comprises the following steps:
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 k|k-1 A state one-step prediction value; p k|k-1 Predicting an error covariance matrix for the state one step; e.g. of a cylinder k To normalize residual errors, e k,j To normalize residual e k The jth element of (a); Ψ k Is a weight matrix;the measured noise covariance matrix is corrected by using the weight matrix; k k Is a filter gain matrix; p k Estimating an error covariance matrix for the state; kernel function G σ (e k,j ) Is selected as a Gaussian function, and the formula is as follows:
where σ is the kernel bandwidth.
Further, the fault detection function Λ in step 12 is provided 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 Respectively measuring a vector, a measuring matrix, a state one-step prediction value, a measuring noise covariance matrix and a state one-step prediction error covariance matrix in the kth step in the sub-filter 1; in the same way, 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 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 realizing redundant speed measurement by combining a photoelectric speed measurement sensor and a mileage wheel aiming at the problems of the slippage of the mileage wheel, the failure of measurement information and the reduction of the pipeline positioning performance caused by the accumulation of inertia resolving errors in the positioning process of a pipeline positioning instrument, and realizes the effective data fusion of the MIMU, the mileage wheel and the information of the photoelectric speed measurement sensor by using a Federal Kalman filtering algorithm based on the maximum correlation entropy M estimation on the basis of the characteristics that the photoelectric speed measurement sensor carries out non-contact speed measurement and does not have the slippage, thereby solving the problem of the reduction of the pipeline positioning performance caused by the instability of the speed measurement information of the mileage wheel.
In order to verify the beneficial effects of the method, the simulation comparison is carried out on the MIMU/odometer wheel/photoelectric speed measurement sensor combination-based trenchless pipeline positioning method and the MIMU/odometer wheel combination positioning method based on the conventional Kalman Filtering (KF) algorithm. In the simulation process, straight pipeline, bent pipeline and deep pipeline tracks existing in the urban underground pipeline are simulated, the length of the simulated underground pipeline track is 140m, a pipeline locator is used for drawing the pipeline at a constant speed of 0.4m/s to acquire data, and the sampling frequency is 100 HZ. Setting MIMU gyroscope zero bias to 12DEG/h, white noise of gyroscopeThe accelerometer has zero bias of 0.075mg and white noise of the accelerometerThe scale factor of the mileage wheel is 0.995, and the scale factor of the photoelectric speed measuring sensor is 0.98. In order to simulate the slipping phenomenon of the odometer wheel in the working process of the pipeline positioning instrument, the output of the odometer wheel is set to zero for 5s at 100s and 200s of simulation respectively.
As can be seen from FIG. 2, when the odometer wheel slips, the MIMU/odometer wheel combination positioning error based on the conventional KF algorithm is obviously increased, the mean square error in the east direction reaches 1.2053m, the mean square error in the north direction reaches 2.4018m, and the mean square error in the sky direction is 0.1066 m. According to the combined positioning method of the MIMU/mileage wheel/photoelectric speed measurement sensor, when the mileage wheel slips, the photoelectric speed measurement sensor can still output correct speed measurement information to correct the inertial navigation system, so that the positioning error caused by the slipping of the mileage wheel can be effectively reduced, the mean square error of the east direction is 0.0919m, the mean square error of the north direction is 0.0560m, and the mean square error of the sky direction is 0.0566 m. Simulation results show that the combined positioning method of the MIMU/odometer wheel/photoelectric speed measuring sensor can effectively reduce positioning errors caused by slipping of the odometer wheel, and compared with the combined positioning method of the MIMU/odometer wheel, the east positioning accuracy of the pipeline is improved by 92.3%, the north positioning accuracy is improved by 97.6%, and the day positioning accuracy is improved by 46.9%.
Drawings
FIG. 1 is a flow chart of an embodiment of a trenchless pipe positioning method based on the combination of MIMU/odometer wheel/photoelectric speed measuring sensor according to the present invention;
FIG. 2 is a comparison graph of the MIMU/odometer wheel combined positioning effect based on the conventional KF algorithm and the combined positioning result based on the MIMU/odometer wheel/photoelectric speed measuring sensor in the invention;
fig. 3 is a comparison result of the MIMU/odometer wheel combined positioning error based on the conventional KF algorithm and the combined positioning error based on the MIMU/odometer wheel/photoelectric speed measuring sensor in the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Step 1: measuring the latitude of the starting point of the pipeline by using a total stationLongitude lambda 0 And height h 0 ;
Step 2: the pipe locator provided with the MIMU, the mileage wheel and the photoelectric speed measuring sensor is arranged at the inlet of the pipe to be started, and the latitude obtained in the step 1 isLongitude λ 0 And height h 0 Manually binding the target object into a data processor of a pipeline locator and finishing initial alignment;
and step 3: pulling the pipeline locator to pass through the pipeline to reach the pipeline end point by using a pull rope and pulling back the pipeline locator reversely to obtain sensor data of the pipeline locator in the forward running process and sensor data in the reverse running process, wherein the sensor data comprises angular speed output of an 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;
and 4, step 4: utilizing the angular velocity information obtained in the process that the pipeline locator in the step 3 runs along the pipelineSum specific force information f b Performing strapdown inertial solution to obtain a strapdown attitude matrixSpeed of strapdown solutionAnd strapdown resolving the location
And 5: calculating to obtain corresponding odometer speed information by using the odometer wheel mileage increment delta S obtained in each sampling period in the process of running the pipeline locator along the pipeline in the step 3 and the output displacement increment delta X of the photoelectric speed measuring sensorSpeed information of photoelectric speed measuring sensorNamely, it is
In the formula: t is s Is a sampling period;
step 6: using MIMU as reference system, and obtaining the speed information of the mileage wheel based on the step 5And the strapdown resolving speed information obtained in the step 4With strapdown attitude matrixEstablishing measurement information in the Federal Filter Structure sub-Filter 1, i.e.
In the formula: v DE ,V DN ,V DU Projecting the speed information of the mileage wheel in a navigation coordinate system;
and 7: taking the MIMU as a reference system, and based on the speed information of the photoelectric speed measuring sensor obtained in the step 5And the strapdown resolving speed information obtained in the step 4With strapdown attitude matrixEstablishing measurement information in the Federal Filter Structure sub-Filter 2, i.e.
In the formula: v LE ,V LN ,V LU Projecting speed information of the photoelectric speed measurement sensor in a navigation coordinate system;
and 8: establishing a state equation of a sub-filter 1 in a federal filtering structure, wherein the specific form is as follows:
in the formula: solving the state vector X for the position error deltaP by strapdown E ,δP N ,δP U Strapdown resolving speed error delta V E ,δV N ,δV U Strapdown solution misalignment angle phi E ,φ N ,φ U Gyro drift epsilon x ,ε y ,ε z And accelerometer zero offsetIs composed of, 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
in matrix I 3×3 Is a 3 × 3 dimensional unit array, 0 a×b Is a zero matrix with a dimension of a multiplied by b,for the strapdown attitude matrix, w, obtained in step 4 gx ,w gy ,w gz ,w ax ,w ay ,w az Is the noise of the gyroscope and the accelerometer,for specific force output f of accelerometer b Projecting in a navigational coordinate systemAn antisymmetric array ofThe strapdown attitude matrix obtained in step 4 can be utilizedOutputting the specific force of the accelerometer obtained in the step 3Projected onto a navigational coordinate system, i.e.
The state equation for building the sub-filter 2 in the federal filter structure is the same as the state equation for the sub-filter 1. Further, a measurement equation of the neutron filter 1 in the federal filter structure is established, and the specific form is as follows:
Z 1 (t)=H 1 (t)X(t)+V 1 (t)
in the formula: system measurement information Z 1 (t) obtained from step 6, V 1 (t) Mileage wheel measurement noise, H 1 (t) is a measurement matrix, and the specific form is as follows:
establishing a measurement equation of the neutron filter 2 in the federal filter structure, wherein the specific form is as follows:
Z 2 (t)=H 2 (t)X(t)+V 2 (t)
in the formula: system measurement information Z 2 (t) obtained from step 7, V 2 (t) is the measurement noise of the photoelectric speed measurement sensor, H 2 (t) is a measurement matrix, and the specific form is as follows:
and step 9: initializing a sub-filter 1 and a sub-filter 2 in a federated filtering structure;
step 10: estimating robust Kalman filtering based on the maximum correlation entropy M in the sub-filters 1 and 2 to carry out iterative estimation so as to obtain state vector estimation values and estimation error covariance matrixes of the sub-filters 1 and 2; wherein, the state vector estimated by the k-th sub-filter 1 is estimated to be X 1,k The covariance matrix of the estimation error is P 1,k The state vector estimated by the k-th sub-filter 2 is estimated as X 2,k The covariance matrix of the estimation error is P 2,k ;
The maximum correlation entropy M estimation robust Kalman filter is designed as follows: respectively discretizing the sub-filter state equation and the measurement equation obtained in the step 8 to obtain:
in the formula:Φ k/k-1 ,Γ k-1 ,H k respectively a discretized system state transition matrix, a discretized system process noise input matrix and a discretized measurement matrix; x k ,Z k Respectively a system state vector and a measurement vector in the k step; w k Is a system noise vector, satisfies a Gaussian distribution, has a mean of zero and a covariance matrix of Q k ;V k For measuring the noise vector, a Gaussian distribution is also satisfied, the mean value is zero, and the covariance matrix is R k ;
The k filtering iterative computation step comprises the following steps:
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
in the formula: x k|k-1 A state one-step prediction value; p k|k-1 Predicting an error covariance matrix for the state one step; e.g. of the type k To normalize residual errors, e k,j To normalize residual e k The jth element of (a); Ψ k Is a weight matrix;the measured noise covariance matrix is corrected by using the weight matrix; k is k Is a filter gain matrix; p k Estimating an error covariance matrix for the state; kernel function G σ (e k,j ) Is chosen as a Gaussian function, i.e.
In the formula: sigma is kernel function bandwidth;
step 11: and (3) transmitting the state vector estimated value and the estimation error covariance matrix obtained in the step (10) to a main filter, and performing sub-filter data fusion in the main filter by adopting a fusion reset mode to obtain the state vector estimated value and the estimation error covariance matrix of the main filter. Wherein, the k-th main filter state vector estimation value X g,k And estimation error covariance matrix P g,k The specific calculation formula is as follows:
step 12: using the k-th state vector estimate X of the main filter obtained in step 11 g,k And estimation error covariance matrix P g,k Resetting the state vector estimate X in step k in sub-filter 1 1,k Estimating an error covariance matrix P 1,k And the system noise variance matrix Q 1,k I.e. by
At the same time, the k-th state vector estimation value X of the main filter obtained in step 11 is used g,k And estimation error covariance matrix P g,k Resetting the state vector estimate X of step k in sub-filter 2 2,k Estimating an error covariance matrix P 2,k And the system noise variance matrix Q 2,k I.e. by
In the formula: q 0 The noise variance matrix of the main filter system can be taken according to experience; beta is a 1,k ,β 2,k Respectively distributing factors for the information of the k-th step sub-filter 1 and the sub-filter 2, wherein the specific calculation formula is as follows:
in the formula: t is 1 ,T 2 The failure alarm threshold values of the sub-filter 1 and the sub-filter 2 are respectively selected according to experience; lambda 1,k ,Λ 2,k The k-th step fault detection function values of the sub-filter 1 and the sub-filter 2 are respectively, and the specific calculation formulas are respectively as follows:
in the formula: z 1,k ,H 1,k ,X 1,k|k-1 ,R 1,k ,P 1,k|k-1 Respectively measuring a vector, a measuring matrix, a state one-step prediction value, a measuring noise covariance matrix and a state one-step prediction error covariance matrix in the kth step in the sub-filter 1; in the same way, Z 2,k ,H 2,k ,X 2,k|k-1 ,R 2,k ,P 2,k|k-1 Respectively measuring a vector, a measuring matrix, a state one-step prediction value, a measuring noise covariance matrix and a state one-step prediction error covariance matrix in the kth step in the sub-filter 2;
step 13: repeating the steps 10-12 until all the data obtained in the process of running the pipeline locator along the forward direction of the pipeline are processed, and utilizing all the state vector estimated values X of the main filter obtained in the step 11 g Middle 1, 2, 3Dimensional element, i.e. strapdown inertial solution position error deltaP E ,δP N ,δP U Feedback correction of the MIMU strapdown solution position information obtained in step 4, i.e.
The corrected position information P E ,P N ,P U Outputting as the positioning information of the forward running pipeline;
step 14: utilizing the angular velocity information obtained in the process that the pipeline locator in the step 3 runs along the pipeline in the reverse directionSpecific force information f b And (4) repeating the steps from 4 to 13 to obtain reverse running pipeline positioning information P 'by using the mileage wheel mileage increment obtained in each sampling period as S and the displacement increment delta X output by the photoelectric speed measuring sensor' E ,P′ N ,P′ U And on the basis of the above-mentioned information P for positioning pipeline E ,P N ,P U Performing weighted fusion to obtain pipe location information, i.e.
In the formula: l is the length of the pipeline; l is k The fusion position point is away from the pipeline end point length;is a distance starting point L-L k Length of forward running pipe positioning information;is a distance L from the end point of the pipeline k Length of the reverse run pipe location information.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement 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 trenchless pipeline positioning method based on MIMU/mile wheel/photoelectric speed measurement sensor combination is characterized in that: the method specifically comprises the following steps:
step 1: measuring the latitude of the starting point of the pipeline by using a total stationLongitude λ 0 And height h 0 ;
Step 2: placing the pipeline positioning instrument provided with the MIMU, the mileage wheel and the photoelectric speed measuring sensor at the inlet of the pipeline to start, and placing the latitude obtained in the step 1Longitude λ 0 And height h 0 Manually binding the target object into a data processor of a pipeline locator and finishing initial alignment;
and step 3: pulling the pipeline locator to pass through the pipeline by using a traction rope to reach the pipeline end point and pulling back the pipeline to obtain sensor data in the forward running process and the sensor data in the reverse running process of the pipeline locator, wherein the sensor data comprises angular speed 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;
and 4, step 4: utilizing the angular velocity information obtained in the process that the pipeline locator in the step 3 runs along the pipelineSum specific force information f b Performing strapdown inertial solution to obtain a strapdown attitude matrixSpeed of strapdown solutionAnd strapdown resolving the location
And 5: calculating to obtain corresponding odometer speed information by using the odometer wheel mileage increment delta S obtained in each sampling period in the process of running the pipeline locator along the pipeline in the step 3 and the output displacement increment delta X of the photoelectric speed measuring sensorSpeed information of photoelectric speed measuring sensorThe formula is as follows:
wherein, T s Is a sampling period;
and 6: using MIMU as reference system, and obtaining the speed information of the mileage wheel based on the step 5And the strapdown resolving speed information obtained in the step 4With strapdown attitude matrixEstablishing measurement information in the federal filter structure sub-filter 1, wherein the formula is as follows:
wherein, V DE ,V DN ,V DU Projecting the speed information of the mileage wheel in a navigation coordinate system;
and 7: taking the MIMU as a reference system, and based on the speed information of the photoelectric speed measuring sensor obtained in the step 5And the strapdown resolving speed information obtained in the step 4With strapdown attitude matrixEstablishing measurement information in the federal filter structure sub-filter 2, wherein the formula is as follows:
wherein, V LE ,V LN ,V LU Projecting speed information of the photoelectric speed measurement sensor in a navigation coordinate system;
and step 8: establishing a state equation and a measurement equation of a sub-filter 1 and a sub-filter 2 in a federal filter structure;
and step 9: initializing a sub-filter 1 and a sub-filter 2 in a federated filtering structure;
step 10: estimating robust Kalman filtering based on the maximum correlation entropy M in the sub-filters 1 and 2 to carry out iterative estimation so as to obtain state vector estimation values and estimation error covariance matrixes of the sub-filters 1 and 2; wherein, the state vector estimated by the k-th sub-filter 1 is estimated to be X 1,k The covariance matrix of the estimation error is P 1,k The state vector estimated by the k-th sub-filter 2 is estimated as X 2,k The covariance matrix of the estimation error is P 2,k ;
Step 11: transmitting the state vector estimated value and the estimation error covariance matrix obtained in the step 10 to a main filter, and performing sub-filter data fusion in the main filter by adopting a fusion reset mode so as to obtain a state vector estimated value and an estimation error covariance matrix of the main filter; wherein, the k-th main filter state vector estimation value X g,k And estimation error covariance matrix P g,k The formula is as follows:
step 12: using the k-th state vector estimate X of the main filter obtained in step 11 g,k And estimation error covariance matrix P g,k Resetting state vector estimate X in step k in sub-filter 1 1,k Estimating an error covariance matrix P 1,k And the system noise variance matrix Q 1,k The formula is as follows:
at the same time, the k-th state vector estimation value X of the main filter obtained in step 11 is used g,k And estimation error covariance matrix P g,k Resetting the state vector estimate X of step k in sub-filter 2 2,k Estimating an error covariance matrix P 2,k And the 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 taken according to experience; beta is a 1,k ,β 2,k The information of the k-th step sub-filter 1 and sub-filter 2 is assigned with factors, respectively, as follows:
wherein, T 1 ,T 2 The failure alarm threshold values of the sub-filter 1 and the sub-filter 2 are respectively selected according to experience; lambda 1,k ,Λ 2,k The k-th fault detection function values of the sub-filter 1 and the sub-filter 2 are respectively obtained;
step 13: repeating the steps 10 to 12 until all data obtained in the process of running the pipeline locator along the forward direction of the pipeline are processed, and utilizing all the main filter state vector estimated values X obtained in the step 11 g Middle 1, 2, 3 dimensional element, namely strapdown inertial solution position error deltaP E ,δP N ,δP U And (3) feeding back the MIMU strapdown resolving position information obtained in the correction step 4, wherein the formula is as follows:
the corrected position information P E ,P N ,P U Outputting as the positioning information of the forward running pipeline;
step 14: utilizing the angular velocity information obtained in the process that the pipeline locator in the step 3 runs along the pipeline in the reverse directionSpecific force information f b And obtaining mileage increment delta S of the mileage wheel and displacement increment delta X output by the photoelectric speed measuring sensor in each sampling period, and repeating the steps from 4 to 13 to obtain positioning information P 'of the reverse running pipeline' E ,P′ N ,P′ U And on the basis of the above-mentioned information P for positioning pipeline E ,P N ,P U Carrying out weighting fusion to obtain pipeline positioning information, wherein the formula is as follows:
wherein L is the length of the pipeline; l is k The fusion position point is away from the pipeline end point length;is a distance starting point L-L k Length of forward running pipe positioning information;is a distance L from the end point of the pipeline k Length of the counter-running pipe location information.
2. The method of claim 1 for positioning a trenchless pipe based on the combination of MIMU/odometer wheel/photoelectric speed sensor, wherein the method comprises the following steps: the state equation of the federal filter structure subfilter 1 established in the step 8 is as follows:
wherein the state vector X is solved for the position error deltaP by strapdown E ,δP N ,δP U Strapdown resolving speed error delta V E ,δV N ,δV U Strapdown solution misalignment angle phi E ,φ N ,φ U Gyro drift epsilon x ,ε y ,ε z And accelerometer zero offsetComposition, the formula 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
in matrix I 3×3 Is a 3 × 3 dimensional unit array, 0 a×b Is a zero matrix with a dimension of a multiplied by b,for the strapdown attitude matrix, w, obtained in step 4 gx ,w gy ,w gz ,w ax ,w ay ,w az Is the noise of the gyroscope and the accelerometer,for specific force output f of accelerometer b Projecting in a navigational coordinate systemAn antisymmetric array ofThe strapdown attitude matrix obtained in step 4 can be utilizedOutputting the specific force of the accelerometer obtained in the step 3Projected to the navigational coordinate system, the formula is as follows:
the state equation of the sub-filter 2 in step 8 is the same as the state equation of the sub-filter 1. In step 8, the measurement equation of the neutron filter 1 is as follows:
Z 1 (t)=H 1 (t)X(t)+V 1 (t)
wherein, the system measurement information Z 1 (t) obtained from step 6, V 1 (t) Mileage wheel measurement noise, H 1 (t) is a specific form of the measurement matrix:
in step 8, the measurement equation of the sub-filter 2 is as follows:
Z 2 (t)=H 2 (t)X(t)+V 2 (t)
wherein, the system measurement information Z 2 (t) obtained from step 7, V 2 (t) is the measurement noise of the photoelectric speed measuring sensor, H 2 (t) is a measurement matrix, and the specific form is as follows:
3. the method of claim 1 for positioning a trenchless pipe based on the combination of MIMU/odometer wheel/photoelectric speed sensor, wherein the method comprises the following steps: the maximum correlation entropy M estimation robust kalman filter in step 10 is designed as follows:
respectively discretizing the sub-filter state equation and the measurement equation obtained in the step 8 to obtain:
wherein phi k|k-1 ,Γ k-1 ,H k Respectively a discretized system state transition matrix, a discretized system process noise input matrix and a discretized measurement matrix; x k ,Z k Respectively a system state vector and a measurement vector in the k step; w k To be aThe system noise vector satisfies the Gaussian distribution, the mean value is zero, and the covariance matrix is Q k ;V k For measuring the noise vector, the Gaussian distribution is also satisfied, the mean is zero, and the covariance matrix is R k ;
The k filtering iterative computation step comprises the following steps:
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 k|k-1 A state one-step prediction value; p k|k-1 Predicting an error covariance matrix for the state one step; e.g. of the type k To normalize residual errors, e k,j To normalize residual e k The jth element of (a); Ψ k Is a weight matrix;the measured noise covariance matrix is corrected by using the weight matrix; k k Is a filter gain matrix; p k Is shaped likeA state estimation error covariance matrix; kernel function G σ (e k,j ) Is selected as a Gaussian function, and the formula is as follows:
where σ is the kernel bandwidth.
4. The method of claim 1 for positioning a trenchless pipe based on the combination of MIMU/odometer wheel/photoelectric speed sensor, 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 Respectively measuring a vector, a measuring matrix, a state one-step prediction value, a measuring noise covariance matrix and a state one-step prediction error covariance matrix in the kth step in the sub-filter 1; in the same way, 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 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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