CN110221278A - A kind of SAS movement compensation method based on multi sensor combination - Google Patents
A kind of SAS movement compensation method based on multi sensor combination Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G01S7/539—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract
The present invention relates to Imaging sonar signal processings and SAS movement compensation technical field, more particularly to a kind of SAS movement compensation method based on multi sensor combination, comprising: be based on synthetic aperture sonar movement measurement system, the sonar movement velocity of inertial navigation measurement and the sonar movement velocity of Doppler log measurement are obtained；The two is merged, the optimal estimation value of sonar movement velocity is obtained；The optimal estimation value of the sonar movement velocity is integrated, the physical plane track and day for calculating sonar are to track；According to the physical plane track of sonar and day to track, the ideal plane track and day that are fitted under criterion of least squares are to track；Calculate the swaying error between synthetic aperture sonar physical plane track and ideal plane track；The practical day of synthetic aperture sonar is calculated to track and ideal day to the heave error between track；Practical path difference is calculated, then is converted into time delay and the echo data that synthetic aperture sonar acquires is compensated.
Description
Technical Field
The invention belongs to the technical field of imaging sonar signal processing and synthetic aperture sonar motion compensation, and particularly relates to a synthetic aperture sonar motion compensation method based on multi-sensor combination.
Background
The synthetic aperture sonar utilizes the movement of a small aperture array along the azimuth direction (track direction) to synthesize a virtual large aperture, and performs coherent superposition processing on the received echoes at different positions according to the spatial position and the phase relation to obtain a high-resolution image. The precondition for realizing high-quality imaging by the synthetic aperture sonar is that the sonar does uniform linear motion along the azimuth direction. In actual work, the synthetic aperture sonar is influenced by factors such as water flow and wind waves, deviates from an ideal motion state to generate a motion error, and the longer the distance is, the larger the influence of the error is. Therefore, in order to obtain a high-quality image, it is necessary to estimate and compensate for a motion error of the synthetic aperture sonar.
Synthetic aperture sonar motion compensation algorithms are mainly classified into 3 types: the first type, based on the motion compensation of a motion measurement system, utilizes a high-precision sensor to measure the motion parameters of the towed body, such as the attitude, the speed and the like, and estimates and compensates the motion error; the second type is motion compensation based on echoes, makes full use of the advantages of multiple subarrays, estimates motion errors through the correlation of two frames of echoes before and after, and is suitable for scenes with small motion errors and strong point-free targets; in the third category, motion compensation based on autofocusing is used to extract and eliminate phase errors affecting image quality from sonar data, and is often used to eliminate residual errors.
The motion measurement system becomes the main basis of motion error estimation with the advantages of high accuracy, good robustness and the like. Synthetic aperture sonar motion measurement systems typically include a variety of heterogeneous motion sensors, typically Inertial Navigation (INS), Doppler Velocity Log (DVL), depth gauges, and Global Positioning System (GPS). When the sonar moves underwater, the inertial navigation and Doppler log often output speed information at the same time, and data redundancy exists. The existing method usually adopts a Kalman filtering method to obtain the optimal estimation value of the sonar movement speed. However, the premise of the optimal estimation realized by the Kalman filtering is that the model is accurate and the statistical characteristics of random interference signals are known, which is often difficult to realize in a real system; and aiming at Kalman filtering, model difference easily causes the phenomena of estimation precision reduction and filtering divergence, so that the problem that the sonar movement speed cannot be accurately calculated is caused.
Disclosure of Invention
The invention aims to solve the defects of the existing motion compensation method based on a motion measurement system, and provides a synthetic aperture sonar motion compensation method based on multi-sensor combination.
In order to achieve the purpose, the invention provides a synthetic aperture sonar motion compensation method based on multi-sensor combination, which considers the influence of the swaying error and the heave error on the synthetic aperture sonar imaging and estimates the motion error by the multi-sensor combination; the method specifically comprises the following steps:
based on a synthetic aperture sonar movement measurement system, acquiring sonar movement speed measured by inertial navigation and sonar movement speed measured by a Doppler log;
fusing sonar movement speed measured by inertial navigation and sonar movement speed measured by a Doppler log to obtain an optimal estimated value of the sonar movement speed;
integrating the optimal estimated value of the sonar movement speed, and calculating the actual plane track and the sky track of the sonar;
according to the actual plane track and the sky track of the sonar, fitting an ideal plane track and an ideal sky track under the least square criterion;
calculating the swaying error between the actual plane track and the ideal plane track of the synthetic aperture sonar;
calculating a heave error between an actual natural-direction track and an ideal natural-direction track of the synthetic aperture sonar;
and calculating the actual acoustic path difference according to the swaying error and the heave error, and then converting the actual acoustic path difference into time delay to compensate the echo data acquired by the synthetic aperture sonar.
As an improvement of the above technical means, the synthetic aperture sonar movement measurement system includes: inertial navigation, doppler log and GPS; an inertial navigation instrument and a Doppler log are arranged in the synthetic aperture sonar, and the inertial navigation instrument is used for measuring sonar movement speed and attitude data of the inertial navigation instrument; the Doppler log is used for measuring the sonar movement speed of the Doppler log; the synthetic aperture sonar is connected with the GPS through a towing cable and used for inputting latitude data into inertial navigation. The attitude data comprises yaw angle, pitch angle and roll angle of inertial navigation.
As one improvement of the technical scheme, the motion speed measured by inertial navigation and the motion speed measured by a doppler log are fused to obtain the optimal estimation value of the sonar motion speed; the method specifically comprises the following steps:
according to the structure of a synthetic aperture sonar movement measurement system, Sage-Husa filtering is adopted, and a complete state method is adopted to establish the state equation and the measurement equation of inertial navigation and DVL: wherein, the state equations of inertial navigation and DVL are:
wherein, ξ9×1Representing synthetic aperture sonar motion measurement system noise; mean value of qn(ii) a Variance of Qn；
Wherein, Ve,Vn,VuRepresenting the sonar movement speed of 3 directions of sonar in the northeast of the east of the inertial navigation measurement respectively; l represents latitude; r represents the radius of the earth; w is aieRepresenting the rotational angular velocity of the earth; f. ofe,fn,fuRespectively representing specific force vectors of the accelerometer in 3 directions in the northeast; delta VeI,δVnI,δVuIRespectively representing the speed errors of the inertial navigation in 3 directions in the northeast;respectively representing inertial navigation yaw angle, pitch angle and roll angle errors; delta VeD,δVnD,δVuDRespectively representing the speed errors of the DVL in 3 directions in the northeast;
inertial navigation and DVL metrology equations:
wherein,representing the sonar movement speed of 3 directions of a sonar in the northeast of the day measured by the DVL;
η3×1to measure noise; mean value of rn(ii) a Variance is Rn；
Discretizing a state equation and a measurement equation of the inertial navigation and Doppler log according to a linear system theory, filtering according to a Sage-Husa basic equation, and iterating to obtain a sonar speed error estimation value of the inertial navigation in the northeast 3 directions;
sonar speed error estimation value in northeast 3 directions by using inertial navigationSonar movement speed V for correcting inertial navigation outpute,Vn,Vu；
The optimal estimation value of the motion speed of the sonar in the northeast 3 directions can be obtained
Specifically, according to the linear system theory, the state equation and the measurement equation of inertial navigation and DVL are discretized, and the discretization result of the state quantity is XkMeasuring the result of the discretization as ZkThe system noise sequence expectation matrix is qkThe system noise sequence variance matrix is QkThe expected array of the measured noise sequence is rkMeasuring noiseAcoustic sequence variance matrix of Rk. Where k represents the kth time. And then filtering according to Sage-Husa basic equation and carrying out iterative process to obtain a sonar speed error estimation value of inertial navigation, and correcting the sonar movement speed output by the inertial navigation by using the estimated value of the sonar speed error of the inertial navigation to obtain the optimal estimated value of the sonar movement speed.
According to the Sage-Husa filtering, recursive filtering is carried out on measurement data of sonar movement speed by using inertial navigation and DVL according to a minimum mean square error criterion, and meanwhile, statistical characteristic parameters of system noise of inertial navigation, system noise of DVL, measurement noise of inertial navigation and measurement noise of DVL are estimated and corrected in real time through a time-varying noise statistical estimator, so that the purposes of reducing Sage-Husa filtering error, inhibiting filtering divergence and improving filtering precision are achieved.
As one improvement of the technical scheme, the optimal estimated value of the sonar movement speed is integrated, and the actual plane track and the sky track of the sonar are calculated; the method specifically comprises the following steps:
and selecting an optimal estimated value of the velocity of the synthetic aperture sonar in the northeast direction within a certain period of time, wherein the period of time comprises a plurality of pulse time intervals. From the initial moment of the time, multiplying the optimal estimated value of the sonar speed by the sonar pulse time interval to obtain the displacement of the synthetic aperture sonar along the direction in the pulse time interval, and continuously accumulating the displacement to obtain the total displacement of the synthetic aperture sonar along the northeast direction, wherein the total displacement in the northeast direction is the actual plane track of the sonar;
selecting an optimal estimated value of the speed of the synthetic aperture sonar in the heaven direction within a certain period of time, wherein the period of time comprises a plurality of pulse time intervals; and multiplying the optimal estimated value of the sonar speed by the sonar pulse time interval from the initial time of the period to obtain the displacement of the synthetic aperture sonar along the direction in the pulse time interval, and continuously accumulating the displacements to obtain the total displacement of the synthetic aperture sonar along the direction of the day, wherein the total displacement of the direction of the day is the actual day-direction track of the sonar.
As one improvement of the technical scheme, the ideal plane track and the sky track under the least square criterion are fitted according to the actual plane track and the sky track of the sonar; the method specifically comprises the following steps:
fitting a straight line to the actual horizontal plane track of the sonar by using a least square method to serve as an ideal track of the sonar on the horizontal plane;
a straight line is fitted to the actual space-direction track of the sonar by using a least square method, and the straight line is used as the ideal track of the sonar in the space direction.
As one improvement of the technical scheme, the method comprises the steps of calculating the swaying error between the actual plane track and the ideal plane track of the synthetic aperture sonar; the method specifically comprises the following steps:
the motion error between the actual plane track of the sonar on the northeast horizontal plane and the ideal plane track of the sonar on the horizontal plane is called the swaying error;
the yaw error Δ x is then:
wherein A' is xeThe coefficient of the first order term of (c); y isn＝[yn1,yn2,...,ynN]T；
As one improvement of the technical scheme, the heave error between the actual-day track and the ideal-day track of the synthetic aperture sonar is calculated; the method specifically comprises the following steps:
the motion error between the actual day-oriented track of the sonar in the direction of the sky and the ideal day-oriented track of the sonar in the direction of the sky is called heave error;
let zu＝[zu1,zu2,...,zuN]Then the heave error can be expressed as:
Δh＝zu-hm (12)
wherein, among others,zukis a sonar space displacement coordinate.
As one improvement of the above technical solution, the actual acoustic path difference is calculated according to the swaying error and the heave error, and then converted into time delay to compensate the echo data collected by the synthetic aperture sonar; the method specifically comprises the following steps:
the motion error can influence the actual acoustic path difference between the sonar ideal track and the actual track, and the time delay can be obtained by dividing 2 times of the actual acoustic path difference by the sound velocity.
By substituting equations (8) and (12) for equation (13), the actual acoustic path difference Δ r' can be calculated:
wherein, the ground distance between the x sonar and the center of the surveying and mapping belt; h is the height from the bottom of the sonar:
multiplying the actual sound path difference delta r' by 2 and dividing by the sound velocity to obtain time delay; wherein the sound velocity is 1500 m/s;
and the time delay correction is carried out on the echo data acquired by the sonar, so that the motion compensation of the echo data can be obtained.
Compared with the prior art, the invention has the beneficial effects that:
under the condition that the statistical characteristics of inertial navigation and DVL noise are unknown, the method adopts a Sage-Husa filtering method to processVelocity measurements of inertial navigation and DVL by a time-varying noise statistical estimator qk,Qk,rk,RkThe statistical characteristic parameters of the system noise and the measured noise are estimated and corrected in real time, the error of the Sage-Husa filtering model is reduced, and the estimation precision of the sonar speed is improved. In addition, the method of the invention can accurately and effectively estimate the motion error, improve the accuracy of motion error compensation and obviously improve the imaging effect of sonar.
Drawings
FIG. 1 is a schematic structural view of a synthetic aperture sonar system of the present invention;
FIG. 2 is a schematic diagram of a synthetic aperture sonar imaging geometry model of the present invention;
FIG. 3 is a schematic structural view of a synthetic aperture sonar movement measurement system of the present invention;
FIG. 4 is a schematic diagram of a specific iterative process of filtering by using Sage-Husa basic equation in step 1) in the multi-sensor combination-based synthetic aperture sonar motion compensation method of the present invention;
FIG. 5(a) is Sage-Husa filtering, existing conventional Kalman filtering, existing H, of the method of the present invention∞A filtered north velocity error versus time diagram;
FIG. 5(b) is Sage-Husa filtering, existing conventional Kalman filtering, existing H, of the method of the present invention∞A graphical representation of the filtered east velocity error versus time;
FIG. 5(c) is Sage-Husa filtering, existing conventional Kalman filtering, existing H, of the method of the present invention∞A schematic of the filtered sky-direction velocity error versus time;
FIG. 6(a) is a prior art conventional Kalman filtering point target imaging result;
FIG. 6(b) isExisting H∞Filtering the point target imaging result;
FIG. 6(c) is a Sage-Husa filtered point target imaging result of the method of the present invention;
FIG. 7(a) is a prior art conventional Kalman filtering point target azimuth profile;
FIG. 7(b) shows conventional H∞A sectional view of the target direction of the filtering points;
FIG. 7(c) is a Sage-Husa filter point target azimuth cross-section of the method of the present invention.
Detailed Description
The invention will now be further described with reference to the accompanying drawings.
The invention has the innovation that the sonar movement speed measured by inertial navigation and DVL is fused by adopting a Sage-Husa filtering method to obtain the optimal estimated value of the sonar speed. Then, integrating the optimal estimated value of the sonar movement speed, and calculating the actual track of the sonar movement; under the least square criterion, the actual track of the sonar is fitted to the ideal track of the sonar, and the swaying error and the heaving error are calculated. And finally, converting the motion error into time delay to compensate the echo data acquired by the sonar. The process of the invention is described in detail below:
as shown in FIG. 1, the invention provides a synthetic aperture sonar motion compensation method based on multi-sensor combination, which considers the influence of the swaying error and the heave error on the synthetic aperture sonar imaging and estimates the motion error by the multi-sensor combination; the method specifically comprises the following steps:
step 1) obtaining sonar movement speed measured by inertial navigation and sonar movement speed measured by a Doppler log based on a synthetic aperture sonar movement measurement system;
specifically, the synthetic aperture sonar movement measurement system includes: inertial navigation, doppler log and GPS; an inertial navigation instrument and a Doppler log are arranged in the synthetic aperture sonar, and the inertial navigation instrument is used for measuring sonar movement speed and attitude data of the inertial navigation instrument; the Doppler log is used for measuring the sonar movement speed of the Doppler log; the synthetic aperture sonar is connected with the GPS through a towing cable and used for inputting latitude data into inertial navigation. The attitude data comprises a yaw angle, a pitch angle and a roll angle of inertial navigation;
step 2) adopting Sage-Husa filtering to fuse sonar movement speed measured by inertial navigation and sonar movement speed measured by a Doppler log to obtain an optimal estimation value of the sonar movement speed;
the method specifically comprises the following steps:
according to the structure of a synthetic aperture sonar motion measurement system, Sage-Husa filtering is utilized, and a complete state method is adopted to establish a state equation and a measurement equation of inertial navigation and DVL: wherein,
the structure of the synthetic aperture sonar movement measurement system is shown in fig. 3, and includes: inertial navigation, DVL and GPS; inertial navigation and DVL are arranged in the synthetic aperture sonar, the inertial navigation outputs sonar movement speed and attitude data, and the DVL outputs sonar movement speed; the synthetic aperture sonar is connected with the GPS through a towing cable and used for inputting latitude data into inertial navigation. Because the inertial navigation needs latitude data assistance in normal work, the GPS is used as an external device to input the latitude data into the inertial navigation through a towing cable, so that the inertial navigation outputs attitude data. The attitude data comprises yaw angle, pitch angle and roll angle of inertial navigation:
equations of state for inertial navigation and DVL
Wherein, ξ9×1Representing the noise of a synthetic aperture sonar motion measurement system, namely the system noise of inertial navigation and DVL, which is white noise; mean value of qn(ii) a Variance of Qn；
Wherein, Ve,Vn,VuRepresenting the sonar movement speed of 3 directions of sonar in the northeast of the east of the inertial navigation measurement respectively; l represents latitude; r represents the radius of the earth; w is aieRepresenting the rotational angular velocity of the earth; f. ofe,fn,fuRespectively representing specific force vectors of the accelerometer in 3 directions in the northeast; delta VeI,δVnI,δVuIRespectively representing the speed errors of the inertial navigation in 3 directions in the northeast;respectively representing inertial navigation yaw angle, pitch angle and roll angle errors; delta VeD,δVnD,δVuDRespectively representing the speed errors of the DVL in 3 directions in the northeast;
inertial navigation and DVL metrology equation Z:
wherein,representing the sonar movement speed of 3 directions of a sonar in the northeast of the day measured by the DVL;
η3×1for measurement noise, white noise; mean value of rn(ii) a Variance is Rn；
According to the linear system theory, discretizing a state equation and a measurement equation of the inertial navigation and Doppler log, filtering according to Sage-Husa basic equation, and iterating to obtain a sonar speed error estimation value of the inertial navigation in the northeast 3 directions
Sonar speed error estimation value in northeast 3 directions by using inertial navigationSonar movement speed V for correcting inertial navigation outpute,Vn,Vu；
The optimal estimation value of the motion speed of the sonar in the northeast 3 directions can be obtained
Specifically, according to the linear system theory, the state equation and the measurement equation of inertial navigation and DVL are discretized, and the discretization result of the state quantity X is obtained as a discretization quantity XkMeasuring the discretization of the quantity Z as a discrete quantity ZkThe system noise sequence expectation matrix is qkThe system noise sequence variance matrix is QkThe expected array of the measured noise sequence is rkMeasuring the variance matrix of the noise sequence as Rk. Wherein, the state quantity X and the quantity in the state equation and the measurement equationThe measurements Z are all continuous quantities; k represents the kth time. And then filtering according to Sage-Husa basic equation and carrying out iterative process to obtain a sonar speed error estimation value of inertial navigation, and correcting the sonar movement speed output by the inertial navigation by using the estimated value of the sonar speed error of the inertial navigation to obtain the optimal estimated value of the sonar movement speed.
According to the Sage-Husa filtering, recursive filtering is carried out on measurement data of sonar movement speed by using inertial navigation and DVL according to a minimum mean square error criterion, and meanwhile, statistical characteristic parameters of system noise of inertial navigation, system noise of DVL, measurement noise of inertial navigation and measurement noise of DVL are estimated and corrected in real time through a time-varying noise statistical estimator, so that the purposes of reducing Sage-Husa filtering model errors, inhibiting filtering divergence and improving filtering precision are achieved.
Wherein, the specific iterative process is shown in figure 4,
wherein b belongs to (0,1) and is a forgetting factor, dkAs a weighting factor, phik,k-1For one-step transition from time k-1 to time k, i.e. F9×9As a result of the discretization, the results,for a state one-step prediction value, vkTo be new, KkFor filter gain, PkTo estimate the mean square error, Pk/k-1The mean square error is predicted for one step from time k-1 to time k,as a discrete quantity XkThe state-estimation value of (a) is,array q is expected for system noise sequenceskIs determined by the estimated value of (c),for system noise sequence variance matrix QkIs determined by the estimated value of (c),array expectation for measuring noise sequenceIs determined by the estimated value of (c),for measuring noise sequence variance matrix RkAn estimate of (d).
Specifically, in this embodiment, for the northeast 3-direction, the optimal estimated value of the sonar movement speed is obtained as follows:
and establishing a state equation and a measurement equation of the 3-direction inertial navigation and DVL in the northeast direction according to the structure of the motion measurement system. According to the linear system theory, the state equation and the measurement equation of the 3-direction inertial navigation and DVL in the northeast are discretized, filtering is performed according to the Sage-Husa basic equation, and the iteration process is shown in FIG. 4.
And obtaining a sonar speed error estimation value of the inertial navigation in the 3-direction of the northeast through Sage-Husa filtering, and correcting the sonar movement speed in the 3-direction of the northeast output by the inertial navigation by using the estimated value of the sonar speed error of the inertial navigation to obtain an optimal estimation value of the sonar movement speed in the 3-direction of the northeast.
Step 3) integrating the optimal estimated value of the sonar movement speed, and calculating the actual plane track and the sky track of the sonar; in particular, the amount of the solvent to be used,
and selecting an optimal estimated value of the velocity of the synthetic aperture sonar in the northeast direction within a certain period of time, wherein the period of time comprises a plurality of pulse time intervals. From the initial moment of the time, multiplying the optimal estimated value of the sonar speed by the sonar pulse time interval to obtain the displacement of the synthetic aperture sonar along the direction in the pulse time interval, and continuously accumulating the displacement to obtain the total displacement of the synthetic aperture sonar along the northeast direction, wherein the total displacement in the northeast direction is the actual plane track of the sonar;
selecting an optimal estimated value of the speed of the synthetic aperture sonar in the heaven direction within a certain period of time, wherein the period of time comprises a plurality of pulse time intervals; and multiplying the optimal estimated value of the sonar speed by the sonar pulse time interval from the initial time of the period to obtain the displacement of the synthetic aperture sonar along the direction in the pulse time interval, and continuously accumulating the displacements to obtain the total displacement of the synthetic aperture sonar along the direction of the day, wherein the total displacement of the direction of the day is the actual day-direction track of the sonar.
In this embodiment, as shown in fig. 2, the initial time position is used as the origin, and the optimal estimated value of the sonar movement speed in the east and north directions of the sonar is integrated to obtain the actual plane track of the sonar; integrating the optimal estimated value of the motion speed of the sonar in the sky to obtain the actual space track of the sonar;
the actual plane trajectory of sonar obtained after integration is expressed as { (x)e1,yn1),(xe2,yn2),...,(xeN,ynN) Expressing the sonar sky-direction actual track obtained after integration as zu1,zu2,...,zuN) And N is the number of sampling points.
Step 4), fitting an ideal plane track and an ideal plane track under the least square criterion according to the actual plane track and the sky track of the sonar;
the method specifically comprises the following steps:
fitting a straight line to the actual horizontal plane track of the sonar by using a least square method to serve as an ideal track of the sonar on the horizontal plane;
a straight line is fitted to the actual space-direction track of the sonar by using a least square method, and the straight line is used as the ideal track of the sonar in the space direction.
In this embodiment, to obtain the northeast ideal planar straight track, yn＝xeA '+ B' estimates the actual horizontal plane track of the sonar by using a least square method, and fits a straight line track as an ideal plane track in the northeast direction; using least square method to align soundA straight line is fitted to the track in the actual weather of the sonar, and the straight line is used as the ideal track of the sonar in the weather.
Step 5) calculating the swaying error between the actual plane track and the ideal plane track of the synthetic aperture sonar; in particular, the amount of the solvent to be used,
the motion error between the actual plane track of the sonar on the northeast horizontal plane and the ideal plane track of the sonar on the horizontal plane is called the swaying error; in particular, the amount of the solvent to be used,
constructing a linear observation equation as follows:
ynk＝xekA′+B′+εk,k＝1,2,...,N (4)
wherein, ynkIs a sonar north displacement coordinate; x is the number ofekIs a sonar east displacement coordinate; epsilonkIs ynkAnd xekLinearly combined observation noise.
Let yn＝[yn1,yn2,...,ynN]T,xe＝[xe1,xe2,...,xeN]T,ε＝[ε1,ε2,...,εN]Writing equation (3) in matrix form:
yn＝wK+ε (5)
wherein,a' is the coefficient of the first order term of x; b' is a constant term.
Constructing rules according to least squares, minimizing the sum of squares of errors, i.e.
J1When the minimum is reached, a least square estimation of K is obtained
Therefore, the walk-around error Δ x can be obtained as:
calculating a heave error between an actual natural-direction track and an ideal natural-direction track of the synthetic aperture sonar; the method specifically comprises the following steps:
the motion error between the actual day-oriented track of the sonar in the direction of the sky and the ideal day-oriented track of the sonar in the direction of the sky is called heave error;
constructing a linear observation equation as follows:
zuk＝hm+σk,k＝1,2,...,N (9)
wherein z isukIs a sonar space displacement coordinate; h ismThe flight path is an ideal track in the sky direction and is a constant; sigmakIs zukIs observed as noise.
Constructing rules according to least squares, minimizing the sum of squares of errors, i.e.
J2To the minimum, obtain
Wherein z isukIs a sonar space displacement coordinate;
let zu＝[zu1,zu2,...,zuN]Then the heave error can be expressed as:
Δh＝zu-hm (12)
wherein, the swaying error and the heaving error are motion errors; in order to obtain a motion error between an actual track and an ideal track, a sonar motion speed needs to be acquired first, and then the sonar motion speed is integrated to obtain the actual plane track and the sky track. When the towed sonar works, a tow boat is connected with a tow body through a towing cable to sail in water, and due to the fact that underwater acoustic environments are complex and changeable, sensor noise statistical characteristic parameters are difficult to describe accurately, a Sage-Husa filtering model is selected to be established, the noise statistical characteristic parameters are estimated through the measuring values of the sensors, sonar movement speed data measured by inertial navigation and DVL are fused, and the optimal estimation value of the sonar movement speed is achieved.
Step 6), calculating an actual acoustic path difference according to the swaying error and the heave error, and then converting the actual acoustic path difference into time delay to compensate echo data collected by the synthetic aperture sonar; the method specifically comprises the following steps:
because the motion error can influence the actual acoustic path difference between the sonar ideal track and the actual track, the time delay can be obtained by dividing the sound velocity by 2 times of the actual acoustic path difference. The actual path difference Δ r' is a difference between the actual track of sonar and the ideal track. In particular, the amount of the solvent to be used,
by substituting equations (8) and (12) for equation (13), the actual acoustic path difference Δ r' can be calculated:
wherein x is the ground distance between the sonar and the center of the surveying and mapping belt; h is the height from the bottom of the sonar:
multiplying the actual sound path difference delta r' by 2 and dividing by the sound velocity to obtain time delay; wherein the sound velocity is 1500 m/s;
and the time delay correction is carried out on the echo data acquired by the sonar, so that the motion compensation of the echo data can be obtained.
The specific derivation process of the actual acoustic path difference Δ r' is as follows:
a synthetic aperture sonar imaging geometric model is established, sonar imaging is according to the sound path of an actual track, as shown in figure 2, the synthetic aperture sonar moves along the direction parallel to the Y axis, and the vertical distance from the XOY plane is h. Assuming that at a certain moment, the synthetic aperture sonar moves to point a, the coordinate value is (0,0, h), there is a point target P within the beam range irradiated by the synthetic aperture sonar, and the coordinate value is (x, y,0), the theoretical acoustic path AP between the synthetic aperture sonar and the target P is represented as r':
assuming that the swaying error in the X-axis direction is Δ X; the heave error in the Z-axis direction is Δ h, and the actual position of the synthetic aperture sonar is at point B (- Δ x,0, h + Δ h), then the actual acoustic path BP between the sonar and the target P is denoted as r':
the theoretical path difference is then expressed as Δ r:
assuming that the sonar azimuth beam is sufficiently narrow, it can be considered that targets at different azimuths from the same range gate have the same acoustic path difference, and by some approximation, the actual acoustic path difference Δ r'
In this example, the parameters used in the present invention are shown in table 1 below:
introduction of conventional Kalman Filter and H∞Filtering, as a comparative experiment with the Sage-Husa filtering, the speed estimation error curve in the northeast 3 direction is shown in fig. 5(a), 5(b) and 5(c), and the speed estimation performance index in the northeast 3 direction is shown in table 2 below:
as can be seen from fig. 5(a), 5(b) and 5(c), the Sage-Husa algorithm continuously performs recursive filtering by adaptively estimating the statistical characteristics of the sensor noise, so that the speed error is controlled in a small range, and the filtering effect is the best. The statistical result in table 2 clearly reflects the superiority of the Sage-Husa algorithm in the filtering effect, and the performance is very stable. The Sage-Husa method can improve the accuracy by at least 37% according to the root mean square error result.
And selecting a point target imaging result to check the accuracy and effectiveness of filtering. And (3) integrating the velocity in the northeast direction 3 obtained by filtering to obtain an actual track, calculating a motion error and compensating an original echo, wherein the imaging result is shown in fig. 6(a), a graph (b) and a graph (c). The point target distance-direction pulse pressure results obtained by the 3 filtering methods in fig. 6 are basically the same, and the direction-direction pulse pressure results have larger differences. Giving a point targetThe oriented cross-sectional views are compared, as shown in fig. 7(a), 7(b) and 7 (c). Using conventional Kalman Filter and H∞The point target after filtering compensation has more sidelobes with larger amplitude in the azimuth direction, which shows that the energy of the point target in the azimuth direction is not concentrated and defocusing is serious. Sage-Husa adaptive filtering can estimate the motion state more accurately, inhibit the peak value of a side lobe, increase the energy of a main lobe and enable the focusing effect of a target to be better. Table 3 is an indicator of the azimuthal radiation performance of the point target as shown in the following table:
the peak sidelobe ratio and the integral sidelobe ratio of the Sage-Husa method are both smaller than those of the other two methods, and the peak sidelobe ratio is close to an ideal value, so that the Sage-Husa filtering method can judge that the motion state estimation is more accurate, and the compensated image quality is higher.
The invention provides a synthetic aperture sonar motion compensation method based on multi-sensor combination, which is a synthetic aperture sonar motion compensation method based on multi-sensor combination of Sage-Husa filtering under the condition that the statistical characteristics of sensor noise are uncertain; the method uses Sage-Husa filtering algorithm to fuse data of various heterogeneous motion sensors, adaptively estimates the optimal value of sonar speed, calculates the swaying error and the heaving error, and finally compensates echo data acquired by sonar through time delay. Wherein the plurality of heterogeneous motion sensor data comprises: attitude and velocity data for inertial navigation, velocity data for DVL, and latitude data for GPS. The method has strong adaptability, and can estimate and correct the statistical characteristic parameters of the system noise and the measured noise in real time through the time-varying noise statistical estimator, thereby achieving the purposes of reducing model errors and inhibiting filtering divergence; the method reduces the influence of motion errors on imaging, has obvious motion compensation effect, and is suitable for motion measurement systems with uncertain noise covariance arrays and measurement noise covariance arrays of motion sensor systems.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. A synthetic aperture sonar motion compensation method based on multi-sensor combination comprises the following steps:
based on a synthetic aperture sonar movement measurement system, acquiring sonar movement speed measured by inertial navigation and sonar movement speed measured by a Doppler log;
fusing sonar movement speed measured by inertial navigation and sonar movement speed measured by a Doppler log to obtain an optimal estimated value of the sonar movement speed;
integrating the optimal estimated value of the sonar movement speed, and calculating the actual plane track and the sky track of the sonar;
according to the actual plane track and the sky track of the sonar, fitting an ideal plane track and an ideal sky track under the least square criterion;
calculating the swaying error between the actual plane track and the ideal plane track of the synthetic aperture sonar;
calculating a heave error between an actual natural-direction track and an ideal natural-direction track of the synthetic aperture sonar;
and calculating the actual acoustic path difference according to the swaying error and the heave error, and then converting the actual acoustic path difference into time delay to compensate the echo data acquired by the synthetic aperture sonar.
2. The method of claim 1, wherein the synthetic aperture sonar motion measurement system comprises: inertial navigation, doppler log and GPS; an inertial navigation instrument and a Doppler log are arranged in the synthetic aperture sonar, and the inertial navigation instrument is used for measuring sonar movement speed and attitude data of the inertial navigation instrument; the Doppler log is used for measuring the sonar movement speed of the Doppler log; the synthetic aperture sonar is connected with the GPS through a towing cable and used for inputting latitude data into inertial navigation.
3. The method according to claim 1, characterized in that the motion speed measured by inertial navigation and the motion speed measured by a Doppler log are fused to obtain the optimal estimated value of the sonar motion speed; the method specifically comprises the following steps:
according to a synthetic aperture sonar movement measurement system, Sage-Husa filtering is adopted, a complete state method is adopted, and a state equation and a measurement equation of an inertial navigation and Doppler log are established: wherein the equation of state of the inertial navigation and Doppler logComprises the following steps:
wherein, ξ9×1Representing synthetic aperture sonar motion measurement system noise; mean value of qn(ii) a Variance of Qn；
Xdvl＝[δVeD δVnD δVuD]T；
Wherein, Ve,Vn,VuRepresenting the sonar movement speed of 3 directions of sonar in the northeast of the east of the inertial navigation measurement respectively; l represents latitude; r represents the radius of the earth; w is aieRepresenting the rotational angular velocity of the earth; f. ofe,fn,fuRespectively representing specific force vectors of the accelerometer in 3 directions in the northeast; delta VeI,δVnI,δVuIRespectively representing the speed errors of the inertial navigation in 3 directions in the northeast;respectively representing inertial navigation yaw angle, pitch angle and roll angle errors; delta VeD,δVnD,δVuDRespectively representing the speed errors of the Doppler log in 3 directions in the northeast;
the measurement equation Z of the inertial navigation and Doppler log is as follows:
wherein,respectively represent 3 directions of sonar measured by a Doppler log in the northeastThe speed of sonar movement;
η3×1to measure noise; mean value of rn(ii) a Variance is Rn；
According to the linear system theory, discretizing a state equation and a measurement equation of the inertial navigation and Doppler log, filtering according to Sage-Husa basic equation, and iterating to obtain a sonar speed error estimation value of the inertial navigation in the northeast 3 directions
Sonar speed error estimation value in northeast 3 directions by using inertial navigationSonar movement speed V for correcting inertial navigation outpute,Vn,Vu；
The optimal estimation value of the motion speed of the sonar in the northeast 3 directions can be obtained
4. The method according to claim 1, characterized in that, the optimal estimated value of the sonar movement speed is integrated, and the actual plane track and the sky track of the sonar are calculated; the method specifically comprises the following steps:
selecting an optimal estimated value of the velocity of the synthetic aperture sonar in the northeast direction within a certain period of time, wherein the period of time comprises a plurality of pulse time intervals; from the initial moment of the time, multiplying the optimal estimated value of the sonar speed by the sonar pulse time interval to obtain the displacement of the synthetic aperture sonar along the direction in the pulse time interval, and continuously accumulating the displacement to obtain the total displacement of the synthetic aperture sonar along the northeast direction, wherein the total displacement in the northeast direction is the actual plane track of the sonar;
selecting an optimal estimated value of the speed of the synthetic aperture sonar in the heaven direction within a certain period of time, wherein the period of time comprises a plurality of pulse time intervals; and multiplying the optimal estimated value of the sonar speed by the sonar pulse time interval from the initial time of the period to obtain the displacement of the synthetic aperture sonar along the direction in the pulse time interval, and continuously accumulating the displacements to obtain the total displacement of the synthetic aperture sonar along the direction of the day, wherein the total displacement of the direction of the day is the actual day-direction track of the sonar.
5. The method according to claim 1, characterized in that the ideal plane track and the sky track under the least square criterion are fitted according to the actual plane track and the sky track of the sonar; the method specifically comprises the following steps:
fitting a straight line to the actual horizontal plane track of the sonar by using a least square method to serve as an ideal track of the sonar on the horizontal plane;
a straight line is fitted to the actual space-direction track of the sonar by using a least square method, and the straight line is used as the ideal track of the sonar in the space direction.
6. The method according to claim 1, wherein the method calculates the swaying error between the synthetic aperture sonar actual plane track and the ideal plane track; the method specifically comprises the following steps:
the yaw error Δ x is then:
wherein A' is xeThe coefficient of the first order term of (c); y isn＝[yn1,yn2,...,ynN]T；
7. The method according to claim 6, characterized in that the heave error between the synthetic aperture sonar actual-day track and the ideal-day track is calculated; the method specifically comprises the following steps:
let zu＝[zu1,zu2,...,zuN]Then the heave error can be expressed as:
Δh＝zu-hm (12)
wherein,zukis a sonar space displacement coordinate.
8. The method according to claim 7, characterized in that, the actual acoustic path difference is calculated according to the swaying error and the heave error, and then the actual acoustic path difference is converted into time delay to compensate the echo data collected by the synthetic aperture sonar; the method specifically comprises the following steps:
actual acoustic path difference Δ r':
wherein x is the ground distance between the sonar and the center of the surveying and mapping belt; h is the height from the bottom of the sonar;
multiplying the actual sound path difference delta r' by 2 and dividing by the sound velocity to obtain time delay; wherein the sound velocity is 1500 m/s;
and the time delay correction is carried out on the echo data acquired by the sonar, so that the motion compensation of the echo data can be obtained.
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