CN110286357B - Target motion positioning method based on underwater sound detection - Google Patents

Target motion positioning method based on underwater sound detection Download PDF

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CN110286357B
CN110286357B CN201910618959.4A CN201910618959A CN110286357B CN 110286357 B CN110286357 B CN 110286357B CN 201910618959 A CN201910618959 A CN 201910618959A CN 110286357 B CN110286357 B CN 110286357B
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CN110286357A (en
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孙伟
韩煜
周青
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CETC 36 Research Institute
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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
    • G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
    • G01S15/50—Systems of measurement, based on relative movement of the target
    • 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
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
    • G01S5/20—Position of source determined by a plurality of spaced direction-finders

Abstract

The invention relates to a target motion positioning method based on underwater sound detection, and belongs to the technical field of target positioning. The method comprises the following steps: s1, acquiring time domain waveform data of the multi-channel node through a plurality of underwater sound detection nodes; s2, calculating to obtain a time delay difference measurement value according to the cross-spectrum function of time domain waveforms between other nodes and the first node in sequence; s3, constructing a Kalman filtering algorithm frame according to the delay difference measurement value and the target real-time state change information; and S4, performing difference statistics judgment on statistics of the delay difference measurement value according to a preset threshold value to improve a Kalman filtering algorithm, and positioning the target after obtaining a target motion parameter estimation result. The method utilizes TDOA measurement information, combines an extended Kalman filtering algorithm, obtains target state information in real time, and utilizes the information to correct a covariance matrix, so that target motion parameter estimation is quickly realized, and the method is better suitable for motion parameter estimation under the condition of target strong maneuvering to position target motion.

Description

Target motion positioning method based on underwater sound detection
Technical Field
The invention relates to the technical field of target positioning, in particular to a target motion positioning method based on underwater sound detection.
Background
Positioning technology is one of the most important technologies for underwater acoustic equipment. At present, with the development of a distributed node cooperative positioning technology, an underwater acoustic network is adopted, and target information of each sensor in the network is fused and then positioned, so that a research hotspot is concerned. Target location technologies for within a network can be divided into three categories: based on time difference of arrival (TDOA); positioning based on energy information (RSS); based on angle of arrival location (AOA). Since the energy attenuation is large in underwater acoustic environments, the RSS based approach is not suitable. AOA requires direction-finding information for a single node, which is not typically available to underwater acoustic network nodes. Therefore, the TDOA method is currently the most widely used positioning method in the underwater acoustic network.
At present, there are two main methods for estimating parameters applied to TDOA positioning technology, one of which is to construct an equation set by using multiple TDOA measurement values, and then solve a possible solution of a target by using the least square idea. And in the other mode, a measurement equation is constructed by utilizing the measurement information, and the EKF thought is combined to calculate the motion parameters of the target in a recursion way. Considering that the underwater acoustic environment is more complex, the disturbance of the measurement information is larger, so the second method is more used. However, the conventional EKF needs to depend on the initial value, and the convergence effect is not good when the target is made to be strong maneuvering.
Disclosure of Invention
In view of the foregoing analysis, the present invention aims to provide a target motion positioning method based on underwater sound detection, so as to solve the problems that the traditional extended kalman filter algorithm in the existing TDOA positioning technology depends on an initial value, and when a target makes a strong maneuver, adaptation cannot be made in time, and finally, estimation accuracy is reduced or diverged.
The purpose of the invention is mainly realized by the following technical scheme:
the invention provides a target motion positioning method based on underwater sound detection, which comprises the following steps:
s1, acquiring time domain waveform data of the multi-channel node through a plurality of underwater sound detection nodes;
s2, calculating to obtain an independent time delay difference measurement value according to the cross-spectrum function of the time domain waveforms between other nodes and the first node in sequence;
s3, constructing a Kalman filtering algorithm frame according to the delay difference measurement value and the target real-time state change information;
and S4, performing difference statistics judgment on statistics of the delay difference measurement value according to a preset threshold value to improve a Kalman filtering algorithm, and positioning the target after obtaining a target motion parameter estimation result.
Further, the constructing of the kalman filter algorithm framework in step S3 includes an equation of the measurement information obtained based on the delay dispersion measured value and an equation of the state parameter of the target real-time state change information, and specifically includes the following steps:
s31, setting parameter estimation quantity of target initial state
Figure GDA0002929039060000021
And variance of parameter estimator of initial state
Figure GDA0002929039060000022
Wherein r isx(t0)，ry(t0)，vx(t0)，vy(t0) Respectively a target initial position horizontal component, a target initial position vertical component, a target initial velocity horizontal component and a target initial velocity vertical component,
Figure GDA0002929039060000023
respectively, the estimator variances of the corresponding initial states;
and S32, predicting the estimator at the next moment according to the estimator at the previous moment to obtain a state estimated value according to the following state equation:
Figure GDA0002929039060000031
predicting the covariance matrix at the next moment according to the covariance matrix at the previous moment, and obtaining an estimated covariance matrix according to the following prediction equation:
Pk/k-1＝APk-1AT+Qk-1；
wherein Q isk-1Is a process noise variance matrix;
s33, calculating according to the pre-estimated covariance matrix and the measurement matrix and the measured noise covariance matrix according to the following formula to obtain an optimal filter gain matrix:
Figure GDA0002929039060000032
wherein R iskMeasuring a noise covariance matrix;
and S34, adjusting the parameter estimator of the state according to the measurement information and the measurement matrix and the gain matrix according to the following formula:
Figure GDA0002929039060000033
and S35, updating the covariance matrix of the filtering error according to the following formula according to the gain matrix and the measurement matrix:
Pk＝(I-KkHk)Pk/k-1。
further, the state parameter equation of the target real-time state change information is as follows:
X(tk)＝AX(tk-1)+Wk-1；
wherein the content of the first and second substances,
Figure GDA0002929039060000034
to transfer the matrix, Wk-1Is a process noise vector, X (t)k)＝[rx(tk) ry(tk) vx(tk) vy(tk)]TParameter estimators for target states, rx(tk),ry(tk) Respectively a horizontal component of the target position and a vertical component of the target position, vx(tk),vy(tk) Respectively, a target velocity horizontal component and a target velocity vertical component, and k is a time sequence.
Further, an equation for obtaining the measurement information according to the delay difference measurement value is as follows: zk＝HkX(tk)+nk；
Wherein the content of the first and second substances,
Figure GDA0002929039060000041
for measuring the matrix, ZkFor measuring information, τ12,τ13,τ14,...,τ1nTime delay to node n and node 1, 2,3,4Differential measurement values, each measurement value being independent of the other, X (t)k)＝[rx(tk) ry(tk) vx(tk) vy(tk)]TParameter estimators for target states, rx(tk),ry(tk) Respectively a horizontal component of the target position and a vertical component of the target position, vx(tk),vy(tk) Is a horizontal component of the target velocity and a vertical component of the target velocity, nkTo measure noise, k is a sequence of time instants.
Further, in step S2, the cross-spectrum function formula of the time-domain waveform between the other node and the first node in turn is as follows:
Figure GDA0002929039060000042
wherein, Y1(m) represents the frequency spectrum corresponding to the time domain waveform of the channel node 1,
Figure GDA0002929039060000043
representing the conjugate, S, of the frequency spectrum corresponding to the time-domain waveform of the channel node n1n(m) represents a cross-spectral function, and N represents a sequence length.
Further, take S according to the following formula1n(m) phase derived delay difference measurement:
Figure GDA0002929039060000044
wherein f ismRepresents the m-th single frequency point, fsRepresenting the sampling rate of the time domain waveform.
Further, the performing a difference statistical decision on the statistics of the delay difference measurement values according to the preset threshold to improve the kalman filter algorithm in step S4 to obtain the target motion parameter estimation result includes the following steps:
s41, obtaining statistic through multiple test statistics results and setting a threshold value;
s42, judging whether the target has strong maneuvering or not by comparing the statistic with the set threshold value;
s43, if the target is subjected to strong maneuver, modifying a Kalman filtering algorithm frame to obtain a target motion parameter estimation result; otherwise, the Kalman filtering algorithm framework is not corrected, and the target motion parameter estimation result is obtained through recursion.
Further, the step S41 of obtaining statistics and setting a threshold value according to the multiple test statistics includes accumulating the n-1 delay difference measurement values of the n underwater acoustic detection nodes to obtain statistics, and setting a threshold value according to the multiple test statistics.
Further, the step S42 of determining whether the target has a strong maneuver by comparing the statistic with the set threshold value includes:
if the statistic is smaller than the set threshold, judging that the target maneuvering is finished;
and if the statistical quantity is not satisfied and is smaller than the set threshold value, judging that the target has strong maneuvering.
Further, the modified kalman filter algorithm framework includes: preserving covariance matrix Pk/k-1And keeping the diagonal values unchanged, and performing zero clearing treatment on the rest.
The technical scheme of the invention has the beneficial effects that: the invention discloses a target motion positioning method based on underwater sound detection, which utilizes a plurality of underwater sound detection nodes to passively receive underwater radiation noise data of ships or active sonar detection data, obtains time delay difference information between a target and the nodes through cross spectrum functions of other nodes and a first node in sequence, and then utilizes improved Kalman filtering to realize real-time positioning of a strong maneuvering target.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a flowchart of a target motion positioning method based on underwater acoustic detection according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating the estimation results of various state parameters of a standard EKF algorithm in accordance with an embodiment of the present invention;
FIG. 3 is a comparison graph of a target tracking trajectory and a true value of a standard EKF algorithm in accordance with an embodiment of the present invention;
FIG. 4 is a diagram of the estimated results of various state parameters of the improved algorithm of the embodiment of the present invention;
FIG. 5 is a diagram illustrating a comparison of a target tracking trajectory with a true value in an improved algorithm according to an embodiment of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
One embodiment of the present invention, as shown in fig. 1, discloses a target motion positioning method based on underwater sound detection, comprising the following steps:
s1, acquiring time domain waveform data of the multi-channel node through a plurality of underwater sound detection nodes;
s2, calculating to obtain an independent time delay difference measurement value according to the cross-spectrum function of the time domain waveforms between other nodes and the first node in sequence;
s3, constructing a Kalman filtering algorithm frame according to the delay difference measurement value and the target real-time state change information;
and S4, performing difference statistics judgment on statistics of the delay difference measurement value according to a preset threshold value to improve a Kalman filtering algorithm, and positioning the target after obtaining a target motion parameter estimation result.
Compared with the prior art, the target motion parameter estimation is quickly realized by using TDOA measurement information and combining with an extended Kalman filtering algorithm through acquiring target state information in real time and correcting a covariance matrix by using the information, so that the target motion parameter estimation is better adapted to the motion parameter estimation under the condition of target strong maneuvering, and the problem of filtering emission can be better avoided.
In specific practical application, taking 4 underwater acoustic detection nodes as an example, the target motion parameter calculation process based on the improved EKF algorithm mainly comprises the following steps:
a. four hydrophones are used to receive target radiated noise data or signals emitted by an active sound source.
b. Using the cross-spectrum function between the other three nodes and the first node, and obtaining the time delay difference measurement value by taking the phase of the cross-spectrum function
Figure GDA0002929039060000071
If the information is broadband information, the delay difference measurement value is a statistical average value.
c. And constructing a measurement matrix H by using the time delay difference measurement value.
d. And setting a plurality of caches for storing the time delay difference data aiming at a plurality of targets, and perceiving the state change information of the targets in real time to obtain a plurality of statistics.
e. And judging the obtained statistic, and if the statistic meets the condition, correcting the pre-estimated covariance matrix.
f. Keeping other steps of the EKF unchanged, and finally obtaining a parameter estimation result.
It should be noted that the positions of the 4 underwater acoustic detection nodes can be arranged at random theoretically, but in consideration of the positioning performance of the target, the arrangement in a square structure is suggested. And (4) receiving data by utilizing time domains of 4 underwater sound detection nodes, and removing outliers through smooth filtering processing. Assume that the target is strongly maneuvered in a < still-motion-still > manner.
In a specific embodiment of the present invention, the constructing a kalman filter algorithm framework in step S3 based on the equation of the measurement information obtained by the delay difference measurement value and the equation of the state parameter of the target real-time state change information specifically includes the following steps:
s31, setting parameter estimation quantity of target initial state
Figure GDA0002929039060000072
And variance of parameter estimator of initial state
Figure GDA0002929039060000073
Wherein r isx(t0)，ry(t0)，vx(t0)，vy(t0) Respectively a target initial position horizontal component, a target initial position vertical component, a target initial velocity horizontal component and a target initial velocity vertical component,
Figure GDA0002929039060000074
respectively, the estimator variances of the corresponding initial states;
and S32, predicting the estimator at the next moment according to the estimator at the previous moment to obtain a state estimated value according to the following state equation:
Figure GDA0002929039060000081
predicting the covariance matrix at the next moment according to the covariance matrix at the previous moment, and obtaining an estimated covariance matrix according to the following prediction equation:
Pk/k-1＝APk-1AT+Qk-1；
wherein Q isk-1Is a process noise variance matrix;
s33, calculating according to the pre-estimated covariance matrix and the measurement matrix and the measured noise covariance matrix according to the following formula to obtain an optimal filter gain matrix:
Figure GDA0002929039060000082
wherein R iskMeasuring a noise covariance matrix;
and S34, adjusting the parameter estimator of the state according to the measurement information and the measurement matrix and the gain matrix according to the following formula:
Figure GDA0002929039060000083
and S35, updating the covariance matrix of the filtering error according to the following formula according to the gain matrix and the measurement matrix:
Pk＝(I-KkHk)Pk/k-1。
in a specific embodiment of the present invention, the state parameter equation of the target real-time state change information is:
X(tk)＝AX(tk-1)+Wk-1；
wherein the content of the first and second substances,
Figure GDA0002929039060000084
to transfer the matrix, Wk-1Is a process noise vector, X (t)k)＝[rx(tk) ry(tk) vx(tk) vy(tk)]TParameter estimators for target states, rx(tk),ry(tk) Respectively a horizontal component of the target position and a vertical component of the target position, vx(tk),vy(tk) Respectively, a target velocity horizontal component and a target velocity vertical component, and k is a time sequence.
In an embodiment of the present invention, an equation for obtaining the measurement information according to the delay difference measurement value is as follows: zk＝HkX(tk)+nk；
Wherein the content of the first and second substances,
Figure GDA0002929039060000091
for measuring the matrix, ZkFor measuring information, τ12,τ13,τ14,...,τ1nTime delay difference measurement values of node No. 2, node No. 3, node No. 4, node No. n and node No. 1, wherein the measurement values are independent of each other, and X (t)k)＝[rx(tk) ry(tk) vx(tk) vy(tk)]TParameter estimators for target states, rx(tk),ry(tk) Respectively a horizontal component of the target position and a vertical component of the target position, vx(tk),vy(tk) Is a horizontal component of the target velocity and a vertical component of the target velocity, nkTo measure noise, k is a sequence of time instants.
In specific practical application, a cross-spectrum or cross-correlation method is utilized to obtain a time delay difference measured value tau between every two nodesijAnd stored. And (3) constructing a measurement matrix by using 4 underwater sound detection nodes and adopting 3 independent delay difference measurement values, and calculating to obtain measurement information. That is, if there are N sounding nodes, there are N-1 independent delay difference measurements.
In a specific embodiment of the present invention, in step S2, the cross-spectrum function formula of the time domain waveforms between the other nodes and the first node in sequence is as follows:
Figure GDA0002929039060000092
wherein, Y1(m) represents the frequency spectrum corresponding to the time domain waveform of the channel node 1,
Figure GDA0002929039060000093
representing the conjugate, S, of the frequency spectrum corresponding to the time-domain waveform of the channel node n1n(m) represents a cross-spectral function, and N represents a sequence length.
In one embodiment of the present invention, S is obtained according to the following formula1n(m) phase derived delay difference measurement:
Figure GDA0002929039060000094
wherein f ismRepresents the m-th single frequency point, fsRepresenting the sampling rate of the time domain waveform.
In a specific embodiment of the present invention, the performing, according to the preset threshold, a difference statistics decision on the statistics of the time difference measurement value to improve the kalman filter algorithm in step S4 to obtain the target motion parameter estimation result includes the following steps:
s41, obtaining statistic through multiple test statistics results and setting a threshold value;
s42, judging whether the target has strong maneuvering or not by comparing the statistic with the set threshold value;
s43, if the target is subjected to strong maneuver, modifying a Kalman filtering algorithm frame to obtain a target motion parameter estimation result; otherwise, the Kalman filtering algorithm framework is not corrected, and the target motion parameter estimation result is obtained through recursion.
In specific practical application, 3 time delay difference components tau independent to 4 underwater sound observation nodes12,τ13,τ14Accumulating, and performing difference statistical judgment after 5 batches are accumulated (according to the statistical result of multiple tests), namely
Figure GDA0002929039060000101
The decision threshold was set to th 1-0.005 (statistical results of multiple trials). When the satisfied statistic is less than the threshold, the strong maneuver is ended and the stationary phase is entered. At this time, the covariance matrix P needs to be modifiedk/k-1The diagonal value of the matrix is mainly kept unchanged, and the rest is cleared. On one hand, target covariance information at the previous moment can be kept as much as possible, and on the other hand, disturbance terms disappear through zero clearing operation on the other terms, and parameter convergence can be accelerated. And if the statistic does not meet the judgment condition, keeping the original processing frame unchanged. The invention judges whether the target is subjected to maneuvering statistical judgment by accumulating multiple measurements, and modifies the covariance matrix to improve the parameter convergence result.
In an embodiment of the present invention, the obtaining statistics and setting the threshold value according to the multiple test statistics in step S41 includes accumulating the n-1 delay measurement values of the n underwater acoustic nodes to obtain statistics, and setting the threshold value according to the multiple test statistics.
In one embodiment of the present invention, the determining whether the target has a strong maneuver by comparing the statistics with the set threshold in step S42 includes:
if the statistic is smaller than the set threshold, judging that the target maneuvering is finished;
and if the statistical quantity is not satisfied and is smaller than the set threshold value, judging that the target has strong maneuvering.
A specific embodiment of the present invention is characterized in that the modified kalman filter algorithm framework includes: preserving covariance matrix Pk/k-1And keeping the diagonal values unchanged, and performing zero clearing treatment on the rest.
FIG. 2 shows a conventional standard EKF (extended Kalman Filter) algorithm for 4 estimators rx，ry，vx，vyThe estimated result of the simulation chart (the target makes a strong maneuver from static to moving and then static, the initial position of the target is (0.2m,0m), the end position is (1.8m,0m), the moving speed of the platform is 0.008m/s, and the total duration is 200 s). According to the motion parameter estimation result shown in fig. 2, the comparison between the flight path estimated by the parameters of the conventional standard EKF algorithm and the real flight path can be drawn, and referring to fig. 3, it can be seen that the difference between the flight path of the estimated value and the true value is large.
FIG. 4 is a block diagram of the improved Kalman filtering algorithm of the present invention for 4 estimators rx，ry，vx，vyThe estimated result of the simulation chart (the target makes a strong maneuver from static to moving and then static, the initial position of the target is (0.2m,0m), the end position is (1.8m,0m), the moving speed of the platform is 0.008m/s, and the total duration is 200 s). The motion parameter estimation result shown in fig. 4 can be used to draw a comparison between the flight path of the parameter estimation of the improved kalman filter algorithm and the real flight path, and referring to fig. 5, it can be seen that the degree of fitting between the flight path of the estimated value and the true value is significantly improved compared to fig. 3.
In summary, the invention discloses a target movement positioning method based on underwater sound detection, which comprises the following steps: s1, acquiring time domain waveform data of the multi-channel node through a plurality of underwater sound detection nodes; s2, calculating to obtain a time delay difference measurement value according to the cross-spectrum function of time domain waveforms between other nodes and the first node in sequence; s3, constructing a Kalman filtering algorithm frame according to the delay difference measurement value and the target real-time state change information; and S4, performing difference statistics judgment on statistics of the delay difference measurement value according to a preset threshold value to improve a Kalman filtering algorithm, and positioning the target after obtaining a target motion parameter estimation result. Compared with the prior art, the method utilizes TDOA measurement information, combines an extended Kalman filtering algorithm, obtains target state information in real time, corrects the covariance matrix by utilizing the information, quickly realizes target motion parameter estimation, better adapts to motion parameter estimation under the condition of target strong maneuvering, and can better avoid the problem of filtering emission.
Those skilled in the art will appreciate that all or part of the processes for implementing the methods in the above embodiments may be implemented by a computer program, which is stored in a computer-readable storage medium, to instruct associated hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (6)

1. A target motion positioning method based on underwater sound detection is characterized by comprising the following steps:
s1, acquiring time domain waveform data of the multi-channel node through a plurality of underwater sound detection nodes;
s2, calculating to obtain an independent time delay difference measurement value according to the cross-spectrum function of the time domain waveforms between other nodes and the first node in sequence;
the cross-spectral function formula is as follows:
Figure FDA0003007583670000011
wherein, Y1(m) represents the frequency spectrum corresponding to the time domain waveform of the channel node 1,
Figure FDA0003007583670000012
display unitConjugation, S, of the frequency spectrum corresponding to the time-domain waveform of the channel node n1n(m) represents a cross-spectral function, N represents a sequence length;
take S according to the following formula1n(m) phase derived delay difference measurement:
Figure FDA0003007583670000013
wherein f ismRepresents the m-th single frequency point, fsThe sampling rate of the time domain waveform is represented, k is a time sequence, and N is the number of underwater sound detection nodes;
s3, constructing a Kalman filtering algorithm frame according to the delay difference measurement value and the target real-time state change information;
s4, performing difference statistics on statistics of the delay difference measurement values according to a preset threshold, judging whether a Kalman filtering algorithm is improved or not, and positioning the target after obtaining a target motion parameter estimation result, wherein the method comprises the following steps:
s41, obtaining statistic through multiple test statistics results and setting a threshold value;
s42, judging whether the target has strong maneuvering or not by comparing the statistic with the set threshold value;
s43, if the target is subjected to strong maneuver, modifying a Kalman filtering algorithm frame to obtain a target motion parameter estimation result; otherwise, the Kalman filtering algorithm framework is not corrected, and a target motion parameter estimation result is obtained through recursion;
the modified kalman filter algorithm framework includes: preserving covariance matrix Pk/k-1And keeping the diagonal values unchanged, and performing zero clearing treatment on the rest.
2. The method according to claim 1, wherein the constructing a kalman filter algorithm framework in step S3 based on the equation of the measurement information obtained by the delay delta measured value and the equation of the state parameter of the target real-time state change information specifically includes the following steps:
s31, setting parameter estimation quantity of target initial state
Figure FDA0003007583670000021
And variance of parameter estimator of initial state
Figure FDA0003007583670000022
Wherein r isx(t0)，ry(t0)，vx(t0)，vy(t0) Respectively a target initial position horizontal component, a target initial position vertical component, a target initial velocity horizontal component and a target initial velocity vertical component,
Figure FDA0003007583670000023
respectively, the estimator variances of the corresponding initial states;
and S32, predicting the estimator at the next moment according to the estimator at the previous moment to obtain a state estimated value according to the following state equation:
Figure FDA0003007583670000024
predicting the covariance matrix at the next moment according to the covariance matrix at the previous moment, and obtaining an estimated covariance matrix according to the following prediction equation:
Pk/k-1＝APk-1AT+Qk-1；
wherein Q isk-1Is a process noise variance matrix;
s33, calculating according to the pre-estimated covariance matrix and the measurement matrix and the measured noise covariance matrix according to the following formula to obtain an optimal filter gain matrix:
Figure FDA0003007583670000025
wherein R iskMeasuring a noise covariance matrix;
and S34, adjusting the parameter estimator of the state according to the measurement information and the measurement matrix and the gain matrix according to the following formula:
Figure FDA0003007583670000026
and S35, updating the covariance matrix of the filtering error according to the following formula according to the gain matrix and the measurement matrix:
Pk＝(I-KkHk)Pk/k-1；
wherein A is a transition matrix, HkFor measuring the matrix, ZkIs the measurement information.
3. The method of claim 2, wherein the state parameter equation of the target real-time state change information is:
X(tk)＝AX(tk-1)+Wk-1；
wherein the content of the first and second substances,
Figure FDA0003007583670000031
to transfer the matrix, Wk-1Is a process noise vector, X (t)k)＝[rx(tk) ry(tk) vx(tk) vy(tk)]TParameter estimators for target states, rx(tk),ry(tk) Respectively a horizontal component of the target position and a vertical component of the target position, vx(tk),vy(tk) Respectively, a target velocity horizontal component and a target velocity vertical component, and k is a time sequence.
4. The method of claim 2, wherein the equation for obtaining the measurement information according to the delay variation measurement value is: zk＝HkX(tk)+nk；
Wherein the content of the first and second substances,
Figure FDA0003007583670000032
for measuring the matrix, ZkFor measuring information, τ12,τ13,τ14,...,τ1nTime delay difference measurement values of node No. 2, node No. 3, node No. 4, node No. n and node No. 1, wherein the measurement values are independent of each other, and X (t)k)＝[rx(tk) ry(tk) vx(tk) vy(tk)]TParameter estimators for target states, rx(tk),ry(tk) Respectively a horizontal component of the target position and a vertical component of the target position, vx(tk),vy(tk) Is a horizontal component of the target velocity and a vertical component of the target velocity, nkTo measure noise, k is a sequence of time instants.
5. The method of claim 1, wherein the step S41 of obtaining statistics and setting the threshold value through multiple tests includes accumulating the n-1 delay measurement values of the n sounding nodes to obtain statistics and setting the threshold value according to the multiple tests.
6. The method of claim 1, wherein determining whether the target is strongly maneuvered by comparing the statistics to a set threshold in step S42 comprises:
if the statistic is smaller than the set threshold, judging that the target maneuvering is finished;
and if the statistical quantity is not satisfied and is smaller than the set threshold value, judging that the target has strong maneuvering.
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