CN117849705A - High-robustness multi-target DOA tracking method, system, chip and device - Google Patents
High-robustness multi-target DOA tracking method, system, chip and device Download PDFInfo
- Publication number
- CN117849705A CN117849705A CN202410249595.8A CN202410249595A CN117849705A CN 117849705 A CN117849705 A CN 117849705A CN 202410249595 A CN202410249595 A CN 202410249595A CN 117849705 A CN117849705 A CN 117849705A
- Authority
- CN
- China
- Prior art keywords
- target
- azimuth
- representing
- survival
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 80
- 239000013598 vector Substances 0.000 claims abstract description 65
- 238000001228 spectrum Methods 0.000 claims abstract description 24
- 238000001514 detection method Methods 0.000 claims abstract description 13
- 230000004083 survival effect Effects 0.000 claims description 55
- 239000011159 matrix material Substances 0.000 claims description 51
- 238000005259 measurement Methods 0.000 claims description 39
- 230000008569 process Effects 0.000 claims description 19
- 238000010586 diagram Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 12
- 230000004044 response Effects 0.000 claims description 11
- 230000001186 cumulative effect Effects 0.000 claims description 7
- 238000009825 accumulation Methods 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 4
- 230000007704 transition Effects 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 2
- 230000007547 defect Effects 0.000 abstract description 2
- 238000012545 processing Methods 0.000 description 7
- 238000004891 communication Methods 0.000 description 4
- 238000013461 design Methods 0.000 description 4
- 230000007613 environmental effect Effects 0.000 description 4
- 238000003491 array Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 239000000654 additive Substances 0.000 description 2
- 230000000996 additive effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 241000764238 Isis Species 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Landscapes
- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
Abstract
The application discloses a target tracking method, aiming at the problem that the target azimuth tracking performance is seriously reduced in the existing target azimuth estimation method when the strong interference influence exists in a detection range, aiming at the defects in the prior art, the application provides a high-robustness multi-target DOA tracking method, a system, a chip and equipment, which adaptively determine a beam forming weight vector, enable the difference between a steady-state beam pattern obtained according to the beam forming weight vector and an expected beam pattern to meet the preset requirement, roughly estimate the target azimuth by calculating azimuth spectrum output, predict the target azimuth associated parameter at the next moment by utilizing a GM model, correct the predicted target azimuth associated parameter at the next moment by the beam forming weight vector, eliminate the influence of underwater environment noise on the performance of the target azimuth tracking method, and realize high-precision high-robustness underwater target azimuth tracking.
Description
Technical Field
The application belongs to a target tracking method, and particularly relates to a high-robustness multi-target DOA tracking method, a system, a chip and equipment.
Background
Underwater target azimuth estimation based on passive sonar arrays is an important research topic in sonar signal processing. The passive sonar array does not emit acoustic signals and detects and positions underwater targets by receiving the acoustic signals of the underwater targets.
The existing target azimuth estimation method can be divided into a traditional target azimuth estimation method and a target azimuth tracking method according to principles. The target azimuth tracking method is mainly realized based on a frame of a Bayesian filtering algorithm, and a changed target azimuth and a signal received by a passive sonar array are respectively regarded as a state and measurement with random noise disturbance. When the traditional target azimuth tracking method estimates the target azimuth, the traditional target azimuth estimation method not only depends on measurement obtained by a passive sonar array, but also considers the kinematic characteristics of the target, so that compared with the target azimuth tracking method, the traditional target azimuth estimation method is higher in robustness and accuracy.
However, when the influence of strong interference exists in the detection range, the existing target azimuth estimation method has the problem that the target azimuth tracking performance is seriously reduced, so that the application range is limited.
Disclosure of Invention
Aiming at the problem that the target azimuth tracking performance is seriously reduced in the existing target azimuth estimation method when the strong interference influence exists in the detection range, the technical problem to be solved is to provide a high-robustness multi-target DOA tracking method, a system, a chip and equipment for solving the technical problem that the target azimuth tracking performance is seriously reduced due to underwater environmental noise aiming at the defects in the prior art.
In order to achieve the above purpose, the present application is implemented by adopting the following technical scheme:
in a first aspect, the present application proposes a high-robustness multi-target DOA tracking method, comprising:
determining a beam forming weight vector, so that the difference between a steady-state beam pattern obtained according to the beam forming weight vector and an expected beam pattern meets a preset requirement;
scanning beams corresponding to the beam forming weight vectors in an azimuth space, calculating azimuth spectrum output, and obtaining coarse estimation measurement of the target azimuth according to the azimuth spectrum output;
in the tracking process, predicting target azimuth associated parameters at the next moment through a GM model;
correcting the target azimuth associated parameter at the next moment through the beam forming weight vector and the target azimuth rough estimation measurement to obtain a corrected target azimuth associated parameter;
and determining the target number, the target state and the target azimuth tracking result according to the corrected target azimuth association parameters.
Further, the determining the beam forming weight vector to make the difference between the steady-state beam pattern obtained according to the beam forming weight vector and the expected beam pattern meet the preset requirement includes:
s1-1, calculating a beam forming weight vector:
wherein,representing beam forming weight vectors, ">Represents a constant factor, ">Steady-state interference covariance matrix representing passive sonar array output>Representation->A passive sonar array response vector corresponding to the direction;
s1-2, marking a steady-state beam diagram corresponding to the beam forming weight vector as a result diagram, and marking a beam diagram expected to be obtained as an expected diagram;
s1-3, comparing a side lobe level on the result graph with an expected value corresponding to the expected graph in each direction, and if the side lobe level is higher than the expected value, increasing the interference source intensity in the corresponding direction; if the side lobe level is lower than the expected value, reducing the interference source intensity in the corresponding direction;
s1-4, repeating the steps S1-1 to S1-3 until the difference between the sidelobe level on the result diagram and the expected value corresponding to the expected diagram meets the preset requirement in each direction.
Further, in step S1-3, the increasing the intensity of the interference source in the corresponding direction and the decreasing the intensity of the interference source in the corresponding direction include:
wherein,indicate->Interference source intensity after secondary adjustment, +.>Indicate->Post-secondary adjustment (th)Iterative gain factor of individual interferers, +.>Indicate->Interference source intensity after secondary adjustment, +.>Representing fundamental beam sidelobe region presenceThe number of interference sources in->Indicate->Direction of the individual interference sources->Represents the lower limit of the region in which the main lobe is located, +.>Representing the upper limit of the region where the main lobe is located;
the interference source is a virtual interference source existing in a fundamental wave beam sidelobe area.
Further, the computing an azimuth spectrum output comprises:
wherein,representing azimuth spectrum output,/->Represents the scan angle +.>Represents the conjugate transpose->Representing the array received signal sample covariance matrix.
Further, the target azimuth association parameter includes: the set potential distribution, the weight value of the survival target component, the average value of the survival target component and the mean square error matrix of the survival target component;
the predicting, by the GM model, the target azimuth correlation parameter at the next moment includes:
predicting the potential set distribution at the next moment:
wherein,representation->Time prediction->Predicted set potential distribution of time of day->Representation->Moment of time new generation target set potential distribution +.>Representing from->The individual elements are->Number of combinations of elements, < >>For target survival probability, ++>Representation->Time of day distribution, ->Representing the distribution of survival target set potential,/->Representing the target number quadratic accumulation parameter, +.>Representing the target number accumulation parameter,/->Representing a target number argument;
predicting the weight of the survival target component at the next moment:
wherein,representation->Time prediction->Time-of-day survival target component weights, +.>Indicating that the target sequence number is to be displayed,representing the number of surviving target components,/-, and>representation->The weight of the survival target component at the moment;
predicting the survival target component mean value at the next moment:
wherein,representation->Time prediction->Time-of-day survival target component mean,/-)>State transition matrix representing a target dynamics model, +.>Representation->The mean value of the survival target components at the moment;
predicting a survival target component mean square error matrix at the next moment:
wherein,representation->Time prediction->Time-of-day survival target component mean square error matrix, +.>Representing noise driving matrix>Representing process noise variance>Representation ofk-1 moment mean square error matrix,>representing the time interval between adjacent moments in the tracking process.
Further, the correcting the target azimuth correlation parameter at the next moment through the beam forming weight vector and the target azimuth rough estimation measurement includes:
correcting the potential collection distribution at the next moment:
wherein,indicating +.>Distribution of the moment of concentration->Representation->The 0 th order set potential cumulative parameter at the moment,representing a predicted target component weight vector, +.>Denoted as->Measurement of time of day->Representing a predicted set potential distribution;
correcting the weight value of the survival target component at the next moment:
wherein,indicating +.>Time-of-day survival target component weights, +.>Representation ofkProbability of detection of time,/-time>Representing predictive weights->Representing a predicted target-s distribution,/->Represents a 1 st order set potential cumulative parameter, +.>Indicating measurement of->Indicating the strength of false alarm poisson->The expression measurement is->Intensity of false alarm at time, < >>Representing discard element->Posterior Collection->；
Correcting the survival target component mean value at the next moment:
wherein,indicating +.>Time-of-day survival target component mean,/-)>Representing the state of the predicted target,representing Kalman filtering gain, < >>Representing a predictive measure;
correcting the mean square error matrix of the survival target component at the next moment:
wherein,indicating +.>Time-of-day survival target component mean square error matrix, +.>Representing the Jacobian matrix of the measurement function, +.>Representing an identity matrix>Representing a predicted mean square error matrix of the target component.
Further, the determining the target number, the target state and the target azimuth tracking result according to the corrected target azimuth association parameter includes:
taking the corresponding maximum value of the corrected potential distributionAs target number estimation value +.>；
Taking the weight value of the survival target component with the largest weight valueAnd taking the average value of the survival target components corresponding to the target components as a target state estimation value, and taking the first item of the target state estimation value as a target azimuth tracking result.
In a second aspect, the present application proposes a high-robustness multi-target DOA tracking system comprising:
the self-adaptive module is used for determining a beam forming weight vector, so that the difference between a steady-state beam pattern obtained according to the beam forming weight vector and an expected beam pattern meets the preset requirement;
the rough estimation module is used for scanning beams corresponding to the beam forming weight vectors in an azimuth space, calculating azimuth spectrum output, and obtaining target azimuth rough estimation measurement according to the azimuth spectrum output;
the prediction module is used for predicting target azimuth association parameters at the next moment through the GM model in the tracking process;
the correction module is used for correcting the target azimuth associated parameter at the next moment through the beam forming weight vector and the target azimuth rough estimation measurement to obtain a corrected target azimuth associated parameter;
and the estimation module is used for determining the target number, the target state and the target azimuth tracking result according to the corrected target azimuth association parameters.
In a third aspect, the present application proposes a chip comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the high-robustness multi-objective DOA tracking method described above.
In a fourth aspect, the present application proposes an electronic device,
including the chip described above.
Compared with the prior art, the application has the following beneficial effects:
the method for tracking the target azimuth of the multi-target DOA comprises the steps of adaptively determining a beam forming weight vector, enabling a steady-state beam pattern obtained according to the beam forming weight vector and a beam pattern expected to be different to meet preset requirements, roughly estimating the target azimuth through calculating azimuth spectrum output, predicting target azimuth associated parameters at the next moment by using a GM model, correcting the predicted target azimuth associated parameters at the next moment through the beam forming weight vector, eliminating the influence of underwater environmental noise on performance of the target azimuth tracking method, and realizing high-precision and high-robustness underwater target azimuth tracking.
The application also provides a high-robustness multi-target DOA tracking system, a chip and electronic equipment, and the high-robustness multi-target DOA tracking system based on the high-robustness multi-target DOA tracking method has all the advantages of the high-robustness multi-target DOA tracking method.
Drawings
For a clearer description of the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and should therefore not be considered limiting in scope, and that other related drawings can be obtained according to these drawings without the inventive effort of a person skilled in the art.
FIG. 1 is a schematic diagram of a first process of a high-robustness multi-objective DOA tracking method according to the present application;
FIG. 2 is a second flow chart of a high-robustness multi-objective DOA tracking method according to the present application;
FIG. 3 is a schematic diagram showing a comparison of a simulated target azimuth change track and an azimuth tracking track in an embodiment of the present application;
fig. 4 is a schematic diagram of the connection of a high-robustness multi-target DOA tracking system according to the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the embodiments of the present application, it should be noted that, if the terms "upper," "lower," "horizontal," "inner," and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or the azimuth or the positional relationship in which the inventive product is conventionally put in use, it is merely for convenience of describing the present application and simplifying the description, and does not indicate or imply that the apparatus or element to be referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus should not be construed as limiting the present application. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Furthermore, the term "horizontal" if present does not mean that the component is required to be absolutely horizontal, but may be slightly inclined. As "horizontal" merely means that its direction is more horizontal than "vertical", and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the embodiments of the present application, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" should be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art as the case may be.
The basic principle of estimating the underwater target azimuth based on the passive sonar array is to locate and track the target by utilizing the propagation characteristics of sound waves and the receiving mode of the sonar array. When the sound wave propagates in the water, reflection and refraction occur when the sound wave encounters an obstacle. By receiving these acoustic signals, the passive sonar array can infer the position and motion state of the target. Continuous tracking and positioning of underwater targets can be achieved through continuous updating and calibration.
A passive sonar array is an acoustic detection device that receives and measures acoustic signals from an underwater target to determine the position, velocity, and direction of the target. Passive sonar arrays typically consist of a series of hydrophones arranged in an array for receiving acoustic signals. The array may be fixed on the sea floor, towed in the water or mounted on a surface boat. However, when there is a strong interference in the detection range, the existing target azimuth estimation methods all have the problem that the target azimuth tracking performance is seriously reduced.
Based on the above problems, the present application proposes a high-robustness multi-objective DOA tracking method, system, chip and device, and the following further details are described in the present application with reference to the embodiments and the accompanying drawings:
fig. 1 is a schematic flow chart of a high-robustness multi-target DOA tracking method, wherein the DOA represents a target direction angle, and the total spelling is Direction of Arrival. The method may include:
s101, determining a beam forming weight vector, and enabling the difference between a steady-state beam pattern obtained according to the beam forming weight vector and an expected beam pattern to meet a preset requirement.
It should be noted that, the beamforming weight vector refers to a weight vector used for forming a beam in array signal processing, and aims to maximize the receiving gain of an array antenna for a signal in a specific direction, and minimize interference and noise in other directions. By optimizing the beam forming weight vector, MVDR beam forming can provide better directional gain and interference suppression capability, so that the detection and positioning accuracy of the passive sonar array to the target signal is improved.
In addition, after the beam forming weight vector is determined, a corresponding steady-state beam pattern can be obtained.
S102, scanning in an azimuth space by using beams corresponding to the beam forming weight vectors, calculating azimuth spectrum output, and obtaining coarse estimation measurement of the target azimuth according to the azimuth spectrum output.
In practical application, the beam scans in the azimuth space, so that the target azimuth information can be acquired, and the target azimuth is roughly estimated.
S103, in the tracking process, predicting target azimuth associated parameters at the next moment through a GM model.
It should be noted that, the target tracking is continuously performed, and the passive sonar array continuously performs detection and positioning at successive times.
The GM Model (gray Model) is a predictive Model for handling incomplete information systems, and known information can be used to predict unknown information. In passive sonar array detection, a gray model can be used to process and predict changes in target bearing correlation parameters.
S104, correcting the target azimuth related parameter at the next moment through the beam forming weight vector and the target azimuth rough estimation measurement, and obtaining the corrected target azimuth related parameter.
In practical application, the problem that the target azimuth tracking performance is seriously reduced can be effectively solved through correction.
S105, determining the target number, the target state and the target azimuth tracking result according to the corrected target azimuth association parameters.
As shown in fig. 2, a second flow chart of a high-robustness multi-target DOA tracking method according to the present application may include:
s201, array signal acquisition.
Setting the total number of tracking momentsKIs a positive integer which is used for the control of the power supply,ktime of day, thenThe time interval between adjacent moments isT。
In the present embodiment, the following is adoptedkThe tracking process at each time is described as an example.
By usingPThe element hydrophone array is used as a passive sonar array, and the frequency of receiving target radiation isIs used for hydrophone arrayPEach array element is 1 toPNumbered in turn. Each hydrophone (array element) on the hydrophone array converts the received underwater acoustic signal into an electric signal, and the electric signal passes through an amplifying circuit at each momentkSampling is performed using a data collector. The signal sampling rate is +.>In the followingkTime of day acquisitionMData of each snapshot. Collecting each array elementMThe data of each snapshot is recorded as a row vector and is recorded from the following partPThe signals collected by the array elements are orderly arranged from small to large according to the numbersP×MA dimension matrix, theP×MThe dimension matrix is the firstkTime of day array receive signal->。
Can be obtained according to the methodKArray received signal at each instant。
S202, self-adaptive wave beam optimization design.
Assuming the presence of side lobe regions of the fundamental beamJVirtual interference sources, e.g. byAnd->（/>) Respectively represent the firstThe intensity and direction of each interference source are that the steady-state interference covariance matrix output by the passive sonar array is:
（1）
wherein,for steady-state interference covariance matrix,>is->Matrix response vector corresponding to direction, +.>For the matrix element additive white noise power, +.>Is a unitary matrix->Is a conjugate transpose.
And forming a weight vector according to the MVDR (Minimum Variance Distortionless Response) beam forming, and obtaining a steady-state beam pattern under the condition of the intensity and the direction of the current interference source. The beam forming weight vector is:
（2）
wherein,representation beamformingWeight vector->Represents a constant factor, ">Steady-state interference covariance matrix representing passive sonar array output>Representation->And a passive sonar array response vector corresponding to the direction. Wherein (1)>The value range is generally between 0 and 1, usually close to 1.
And comparing the obtained steady-state beam pattern with the expected beam pattern, and if the side lobe level is higher than the expected value in a certain direction, increasing the intensity of the interference source in the corresponding direction, otherwise, reducing the intensity of the interference source in the corresponding direction. Number of interference sourcesJThe number of the interference sources is generally more than 2-3 times of the number of the array elements, the direction of the interference sources can be all-round, the intensity of the interference sources in the uniformly distributed main area can be set to be 0, namely, no interference source exists in the main lobe area, and the self-adaptive adjustment is only carried out on the intensity of the interference sources in the side lobe area. If at the firstIn the secondary self-adaptive adjustment process, the area of the main lobe area is +.>The interference source intensity in the next adjustment>Is set as follows:
（3）
wherein:
(4)
here, theIndicate->Interference source intensity after secondary adjustment, +.>Indicate->Interference source intensity after secondary adjustment, +.>Indicating the number of interference sources existing in the side lobe area of the fundamental wave beam, < >>Is->Post-secondary adjustment (th)jIterative gain factor of individual interferers, +.>Is the maximum value allowed by the alternative gain factor, < >>Representing the lower limit of the area in which the main lobe is located,represents the upper limit of the area where the main lobe is located, +.>Is->Normalized beam response in direction, +.>Is->A desired beam response in the direction.
Is typically non-zero, otherwise the source strength is zero in each iteration. After the iteration is finished, if the obtained beam response is higher than the expected response, the interference source intensity is increased in the next iteration; if the resulting beam response is lower than the expected response, the interference source intensity is reduced at the next iteration. A maximum allowable value may be set for preventing the adaptation process from diverging.
The method can design the beam forming weight vector which meets the expectations。
The application is actually an improved Olen method, in the azimuth space, a virtual interference source obtains a steady-state beam pattern under the conditions of the intensity and the direction of a current interference source according to a beam forming weight vector, and the obtained beam pattern is compared with an expected beam pattern to adjust the intensity of the interference source, so that the beam weight vector with low sidelobe which accords with the expected is designed iteratively.
The beamforming weight vector is designed by using the ollen method, and in practical application, the MVDR beamformer can be used for processing the virtual array received signals containing the virtual interference sources, and the design of the beamforming weight vector is realized by adjusting the power of the virtual interference sources.
S203, measuring the target azimuth.
Scanning in azimuth space by utilizing beams corresponding to the designed beam forming weight vectors, and calculating azimuth spectrum output as follows:
(5)
wherein,representing azimuth spectrum output,/->Represents the scan angle +.>Representing the array received signal sample covariance matrix.
As scan angleUniformly varying between 0 and 360 DEG, calculating +.>The spatial azimuth spectrum can be obtained.
The peak value corresponding to the azimuth spectrum output iskCoarse estimation measurement of target azimuth at moment, recorded asWherein, the method comprises the steps of, wherein,Nis thatkEstimated target number of time,/->Corresponding toNThe orientation of the individual targets. Total in trackingKThe rough estimated measurement of the target azimuth obtained at each moment is recorded as +.>。
Scanning in azimuth space by using the designed beam forming weight vector, calculating azimuth spectrum output, and extracting a peak value corresponding to the azimuth spectrum peak value as a target azimuth coarse estimation measurement.
S204, setting initial parameters of target azimuth tracking.
Before starting tracking positioning (regarded as) Setting initial parameters of GM model component +.>Wherein, the method comprises the steps of, wherein,/>，/>and->Initial weights, initial states and initial mean square error matrices for GM component, respectively, +.>Is->Number of target components at a time. Setting initial set potential distribution +.>. Setting process noise variance->Measuring noise variance->Tracking total time of dayK。
S205, predicting target azimuth association parameters.
S205-1, predicting the set potential distribution.
At a given pointk-1 moment set potential distributionIn the case of (a) calculating a predicted potential set distribution (i.e., a potential set distribution at the next moment):
（6）
wherein,representation->Time prediction->Predicted set potential distribution of time of day->Representation->Moment of time new generation target set potential distribution +.>Representing from->The individual elements are->Number of combinations of elements, < >>Representation->Time of day distribution, ->Representing the distribution of survival target set potential,/->Representing the target number quadratic accumulation parameter, +.>Representing the target number accumulation parameter,/->Representing the target number argument +_>Is the target survival probability. />。
S205-2, predicting a target component.
Calculating a predicted surviving target component weight (i.e., the surviving target component weight at the next time):
（7）
wherein,representation->Time prediction->Time-of-day survival target component weights, +.>Indicating that the target sequence number is to be displayed,representing the number of surviving target components,/-, and>representation->Surviving target component weights at time.
Calculating a predicted survival target component mean (i.e., a survival target component mean at a next time):
（8）
wherein,representation->Time prediction->Time-of-day survival target component mean,/-)>State transition matrix representing a target dynamics model, +.>Representation->The mean value of the surviving target components at the moment.
Calculating a predicted surviving target component mean square error matrix (i.e., the surviving target component mean square error matrix at the next time):
（9）
wherein the method comprises the steps ofRepresentation->Time prediction->Time-of-day survival target component mean square error matrix, +.>Representing noise driving matrix>Representing process noise variance>Representation ofk-1 moment mean square error matrix,>representing the time interval between adjacent moments in the tracking process.
Based on the above calculation, a new target component is added:
（10）
wherein,for new target component weights, +.>For the new target component state, +.>For the new target component mean square error matrix, +.>Is thatk-1 moment target component number, < >>Is the number of new target components.
Order theThe predicted component is expressed as:
（11）
wherein,for predicting the target component weights +.>For predicting the target component state +.>To predict a target component mean square error matrix.
S206, updating the target azimuth correlation parameters.
S206-1, updating the set potential distribution.
Updating the set potential distribution as follows:
（12）
wherein,indicating +.>Distribution of the moment of concentration->Representation->The 0 th order set potential cumulative parameter at the moment,representing a predicted target component weight vector, +.>Denoted as->Measurement of time of day->Representing a predicted set potential distribution.
Representing the inner product, i.e.)>（/>，/>），/>For predicting the weight vector of the target component there is +.>Definitions->Is thatuThe expression of the order set potential multiplication parameter is:
（13）
wherein,is false alarm probability->Representation ofjFirst order symmetric function, have +>，，/>Is the slavejFetching from different elementslThe number of arrangement of the individual elements is +.>，/>For the target component weight, +.>For the cumulative parameter order of the set potential, +.>For the measurement set, define->To consider the gaussian distribution parameters of false alarms and detection, the expression is:
（14）
wherein,is the strength of false alarm poisson>The expression measurement is->Intensity of false alarm at time, < >>The probability of detection is indicated and,representing a predicted target-si distribution, +.>The expression of (2) is:
（15）
wherein,is->Predicted gaussian distribution for each target.
Wherein,（/>) The expression of (2) is:
（16）
wherein,for predictive measurement, ->Is a residual matrix, and the expression is:
（17）
（18）
wherein,for predicting the target component state +.>For predicting the target component mean square error matrix,/v>For measuring matrix, < >>To measure noise.
S206-2, updating the target component.
Calculating the weight of the target component:
（19）
wherein,indicating +.>Time-of-day survival target component weights, +.>Representation ofkProbability of detection of time,/-time>Representing predictive weights->Representing a predicted target-s distribution,/->Represents a 1 st order set potential cumulative parameter, +.>Indicating measurement of->Indicating the strength of false alarm poisson->The expression measurement is->Intensity of false alarm at time, < >>Representing discard element->Posterior Collection->。
Calculating the mean value of the target componentAnd a target component mean square error matrix +.>The expression is:
（20）
（21）
wherein,indicating +.>Time-of-day survival target component mean,/-)>Representing the state of the predicted target,representing Kalman filtering gain, < >>Representing predictive measures,/->Indicating +.>Time-of-day survival target component mean square error matrix, +.>Representing the Jacobian matrix of the measurement function, +.>Representing an identity matrix>And (3) representing.
The expression of (2) is:
（22）
wherein,representing the residual matrix.
kUpdated probability hypothesis density for time of dayExpressed as a gaussian mixture model:
（23）
wherein the method comprises the steps ofIs thatkTime-of-day survival target component weights, +.>And->Respectively iskThe mean value of the survival target component and the mean square error matrix of the survival target component at the moment.
S207, estimating the target number and the target state.
Potential distributionCorresponding to maximum valuenEstimate value +.>Survival target component weight maximum +.>The corresponding +.>The first item of the state estimation value vector is the target azimuth tracking result.
Will collect potential distributionAnd the target component is substituted into the next time, i.e., let time +.>Circulating inThe loop sequentially executes the steps S202 to S207, so that the underwater target azimuth can be continuously tracked until the underwater target azimuth is +.>The cycle is stopped.
This embodiment is a robust multi-target azimuth tracking method based on an improved Olen method and a unified hypothesis probability density filtering. Aiming at the reduction of azimuth tracking performance caused by complex underwater environmental noise, an improved Olen method is utilized to adaptively design wave beams in real time in the target azimuth tracking process, the target azimuth is roughly estimated to be a measurement value, the target azimuth is tracked by utilizing potential-gathering hypothesis probability density filtering, the influence of the underwater environmental noise on the performance of the target azimuth tracking method is eliminated, and the high-precision and high-stability underwater target azimuth tracking is realized.
The following is one example of a method for high-robustness multi-target DOA tracking using the present application:
the initial azimuth angles of the 3 underwater targets relative to the sensor array are 120 degrees, 180 degrees and 240 degrees respectively, the target azimuth angles change in a uniform model, the total tracking time step number is 5000, the time interval between adjacent time steps is 1 s, and the total tracking time is 5000 s. As indicated by the dashed line in fig. 3, the trajectory of the change in azimuth of the simulation target is shown. The sound velocity under water is 1500 m/s, the target radiates a narrow-band signal with the frequency of 200Hz, and the signal amplitude is 1 when reaching the array. And (3) using a 12-element uniform circular array to obtain measurement, and adding zero-mean additive Gaussian white noise into the measurement.
And obtaining measurement data according to the condition simulation. And testing the high-robustness multi-target DOA tracking method provided by the application by using the measurement data obtained through simulation. As shown in fig. 3, the solid line represents the target azimuth tracking trajectory. As can be seen from FIG. 3, by adopting the high-robustness multi-target DOA tracking method, the target azimuth tracking error is extremely small, and the high-precision and robust target azimuth tracking is realized.
Based on the high-robustness multi-target DOA tracking method, the application also provides a high-robustness multi-target DOA tracking system. As shown in fig. 4, which is a schematic diagram of the high-robustness multi-target DOA tracking system of the present application, may include:
the self-adaptive module is used for determining a beam forming weight vector, so that the difference between a steady-state beam pattern obtained according to the beam forming weight vector and an expected beam pattern meets the preset requirement.
And the rough estimation module is used for scanning in the azimuth space by using the beams corresponding to the beam forming weight vectors, calculating azimuth spectrum output, and obtaining target azimuth rough estimation measurement according to the azimuth spectrum output.
And the prediction module is used for predicting the target azimuth associated parameters at the next moment through the GM model in the tracking process.
And the correction module is used for correcting the target azimuth associated parameter at the next moment through the beam forming weight vector and the target azimuth rough estimation measurement to obtain a corrected target azimuth associated parameter.
And the estimation module is used for determining the target number, the target state and the target azimuth tracking result according to the corrected target azimuth association parameters.
In yet another embodiment of the present invention, a terminal device is provided, the terminal device comprising a processor and a memory, the memory for storing a computer program, the computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular to load and execute one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiments of the present invention may be used for the operation of a highly robust multi-target DOA tracking method.
The terminal device may be a computer device, and the computer device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, which when executed by the processor, implements the high-robustness multi-objective DOA tracking method in embodiments, and is not described in detail herein to avoid repetition. Alternatively, the computer program, when executed by the processor, implements the functions of each module in the high-robustness multi-objective DOA tracking system of the embodiment, and in order to avoid repetition, it is not described in detail herein.
The computer device may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. Computer devices may include, but are not limited to, processors and memory.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The memory may also be an external storage device of the computer device, such as a plug-in hard disk provided on the computer device, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like.
Further, the memory may also include both internal storage units and external storage devices of the computer device. The memory is used to store computer programs and other programs and data required by the computer device. The memory may also be used to temporarily store data that has been output or is to be output.
The terminal device may also be a chip, the chip of this embodiment comprising a processor, which may be one or more in number, and a memory for storing a computer program executable by the processor. A computer program stored in memory may include one or more modules each corresponding to a set of instructions. Furthermore, the processor may be configured to execute the computer program to perform the high-robustness multi-target DOA tracking method described above.
In addition, the chip may further include a power supply component that may be configured to perform power management of the chip, and a communication component that may be configured to enable communication of the chip, e.g., wired or wireless communication. In addition, the chip may also include an input/output (I/O) interface. The chip may operate based on an operating system stored in memory.
The embodiment of the application also provides electronic equipment, which comprises a chip, wherein the chip realizes the steps of the high-robustness multi-target DOA tracking method when executing.
The foregoing is merely a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and variations may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (10)
1. A high-robustness multi-target DOA tracking method, comprising:
determining a beam forming weight vector, so that the difference between a steady-state beam pattern obtained according to the beam forming weight vector and an expected beam pattern meets a preset requirement;
scanning beams corresponding to the beam forming weight vectors in an azimuth space, calculating azimuth spectrum output, and obtaining coarse estimation measurement of the target azimuth according to the azimuth spectrum output;
in the tracking process, predicting target azimuth associated parameters at the next moment through a GM model;
correcting the target azimuth associated parameter at the next moment through the beam forming weight vector and the target azimuth rough estimation measurement to obtain a corrected target azimuth associated parameter;
and determining the target number, the target state and the target azimuth tracking result according to the corrected target azimuth association parameters.
2. The method of claim 1, wherein determining the beamforming weight vector to cause a difference between a steady-state beam pattern obtained from the beamforming weight vector and an expected beam pattern to satisfy a preset requirement comprises:
s1-1, calculating a beam forming weight vector:
wherein,representing beam forming weight vectors, ">Represents a constant factor, ">Steady-state interference covariance matrix representing passive sonar array output>Representation->A passive sonar array response vector corresponding to the direction;
s1-2, marking a steady-state beam diagram corresponding to the beam forming weight vector as a result diagram, and marking a beam diagram expected to be obtained as an expected diagram;
s1-3, comparing a side lobe level on the result graph with an expected value corresponding to the expected graph in each direction, and if the side lobe level is higher than the expected value, increasing the interference source intensity in the corresponding direction; if the side lobe level is lower than the expected value, reducing the interference source intensity in the corresponding direction;
s1-4, repeating the steps S1-1 to S1-3 until the difference between the sidelobe level on the result diagram and the expected value corresponding to the expected diagram meets the preset requirement in each direction.
3. The method of claim 2, wherein in step S1-3, the increasing the intensity of the source of interference in the corresponding direction and the decreasing the intensity of the source of interference in the corresponding direction comprise:
wherein,indicate->Interference source intensity after secondary adjustment, +.>Indicate->Post-secondary adjustment->Iterative gain factor of individual interferers, +.>Indicate->Interference source intensity after secondary adjustment, +.>Indicating the number of interference sources existing in the side lobe area of the fundamental wave beam, < >>Indicate->Direction of the individual interference sources->Represents the lower limit of the region in which the main lobe is located, +.>Representing the upper limit of the region where the main lobe is located;
the interference source is a virtual interference source existing in a fundamental wave beam sidelobe area.
4. A high-robustness multi-target DOA tracking method according to claim 3 wherein the calculating azimuth spectrum output comprises:
wherein,representing azimuth spectrum output,/->Represents the scan angle +.>Represents the conjugate transpose->Representing the array received signal sample covariance matrix.
5. The method of claim 4, wherein the target bearing correlation parameters comprise: the set potential distribution, the weight value of the survival target component, the average value of the survival target component and the mean square error matrix of the survival target component;
the predicting, by the GM model, the target azimuth correlation parameter at the next moment includes:
predicting the potential set distribution at the next moment:
wherein,representation->Time prediction->Predicted set potential distribution of time of day->Representation->Moment of time new generation target set potential distribution +.>Representing from->The individual elements are->Number of combinations of elements, < >>For target survival probability, ++>Representation->Time of day distribution, ->Representing the distribution of survival target set potential,/->Representing the target number quadratic accumulation parameter, +.>Representing the target number accumulation parameter,/->Representing a target number argument;
predicting the weight of the survival target component at the next moment:
wherein,representation->Time prediction->Time-of-day survival target component weights, +.>Indicates the target sequence number +.>Representing the number of surviving target components,/-, and>representation->The weight of the survival target component at the moment;
predicting the survival target component mean value at the next moment:
wherein,representation->Time prediction->Time-of-day survival target component mean,/-)>State transition matrix representing a target dynamics model, +.>Representation->The mean value of the survival target components at the moment;
predicting a survival target component mean square error matrix at the next moment:
wherein,representation->Time prediction->Time-of-day survival target component mean square error matrix, +.>Representing noise driving matrix>Representing process noise variance>Representation ofk-1 moment mean square error matrix,>representing the time interval between adjacent moments in the tracking process.
6. The method of claim 5, wherein correcting the target azimuth correlation parameter at the next time by the beamforming weight vector and the target azimuth rough estimate measurement comprises:
correcting the potential collection distribution at the next moment:
wherein,indicating +.>Distribution of the moment of concentration->Representation->Time 0 th order set potential cumulative parameter, < ->Representing a predicted target component weight vector, +.>Denoted as->Measurement of time of day->Representing a predicted set potential distribution;
correcting the weight value of the survival target component at the next moment:
wherein,indicating +.>Time-of-day survival target component weights, +.>Representation ofkThe probability of detection of the moment in time,representing predictive weights->Representing a predicted target-s distribution,/->Represents a 1 st order set potential cumulative parameter, +.>Indicating measurement of->Indicating the strength of false alarm poisson->The expression measurement is->Intensity of false alarm at time, < >>Representing discard element->Posterior Collection->；
Correcting the survival target component mean value at the next moment:
wherein,indicating +.>Time-of-day survival target component mean,/-)>Representing predicted target state,/->Representing Kalman filtering gain, < >>Representing a predictive measure;
correcting the mean square error matrix of the survival target component at the next moment:
wherein,indicating +.>Time-of-day survival target component mean square error matrix, +.>Representing the Jacobian matrix of the measurement function, +.>Representing an identity matrix>Representing a predicted mean square error matrix of the target component.
7. The method of claim 6, wherein determining the number of targets, the target state, and the target azimuth tracking result according to the corrected target azimuth correlation parameters comprises:
taking the corresponding maximum value of the corrected potential distributionAs target number estimation value +.>；
Taking the weight value of the survival target component with the largest weight valueSurvival orders corresponding to the target componentsThe target component mean value is used as a target state estimated value, and the first item of the target state estimated value is used as a target azimuth tracking result.
8. A high-robustness multi-target DOA tracking system, comprising:
the self-adaptive module is used for determining a beam forming weight vector, so that the difference between a steady-state beam pattern obtained according to the beam forming weight vector and an expected beam pattern meets the preset requirement;
the rough estimation module is used for scanning beams corresponding to the beam forming weight vectors in an azimuth space, calculating azimuth spectrum output, and obtaining target azimuth rough estimation measurement according to the azimuth spectrum output;
the prediction module is used for predicting target azimuth association parameters at the next moment through the GM model in the tracking process;
the correction module is used for correcting the target azimuth associated parameter at the next moment through the beam forming weight vector and the target azimuth rough estimation measurement to obtain a corrected target azimuth associated parameter;
and the estimation module is used for determining the target number, the target state and the target azimuth tracking result according to the corrected target azimuth association parameters.
9. A chip, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the high-robustness multi-target DOA tracking method as claimed in any one of claims 1-7.
10. An electronic device, characterized in that,
comprising a chip as claimed in claim 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410249595.8A CN117849705A (en) | 2024-03-05 | 2024-03-05 | High-robustness multi-target DOA tracking method, system, chip and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410249595.8A CN117849705A (en) | 2024-03-05 | 2024-03-05 | High-robustness multi-target DOA tracking method, system, chip and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117849705A true CN117849705A (en) | 2024-04-09 |
Family
ID=90544294
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410249595.8A Pending CN117849705A (en) | 2024-03-05 | 2024-03-05 | High-robustness multi-target DOA tracking method, system, chip and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117849705A (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9869752B1 (en) * | 2016-04-25 | 2018-01-16 | Ocean Acoustical Services And Instrumentation Systems, Inc. | System and method for autonomous joint detection-classification and tracking of acoustic signals of interest |
| CN109307855A (en) * | 2018-08-02 | 2019-02-05 | 西北工业大学 | The sparse approximate minimum variance DOA estimation method of mesh free based on mesh error model |
-
2024
- 2024-03-05 CN CN202410249595.8A patent/CN117849705A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9869752B1 (en) * | 2016-04-25 | 2018-01-16 | Ocean Acoustical Services And Instrumentation Systems, Inc. | System and method for autonomous joint detection-classification and tracking of acoustic signals of interest |
| CN109307855A (en) * | 2018-08-02 | 2019-02-05 | 西北工业大学 | The sparse approximate minimum variance DOA estimation method of mesh free based on mesh error model |
Non-Patent Citations (2)
| Title |
|---|
| 张博轩等: "量测噪声不确定情况下的水下多目标稳健方位跟踪", 声学学报, vol. 48, no. 04, 11 July 2023 (2023-07-11), pages 605 - 617 * |
| 杨益新: "声呐波束形成与波束域高分辨方位估计技术研究", 中国优秀博士学位论文全文数据库, 15 March 2003 (2003-03-15), pages 3 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ries et al. | Geometry-induced range-dependence compensation for bistatic STAP with conformal arrays | |
| CN106855622B (en) | A kind of angle-measuring method of phased array at subarray level radar | |
| CN108845325B (en) | Towed line array sonar subarray error mismatch estimation method | |
| CN108387876B (en) | External radiation source radar network double-base-distance error registration method based on CTLS | |
| CN105182311B (en) | Omnidirectional's radar data processing method and system | |
| CN105022050A (en) | Underwater-acoustic-channel discrete noise source suppression method of multi-sensor array | |
| Park et al. | Sequential sparse Bayesian learning for time-varying direction of arrival | |
| KR20200065827A (en) | Apparatus and Method for Tracking Object based on Radar Image Reconstruction | |
| CN101644760A (en) | Rapid and robust method for detecting information source number suitable for high-resolution array | |
| CN111220960A (en) | Target feature extraction method and device under multi-station radar | |
| Hadzagic et al. | Track-before detect methods in tracking low-observable targets: A survey | |
| CN102087357A (en) | Method for echo direction estimation of sensor array and for multibeam echo depth sounding and bottom detection | |
| US7319640B1 (en) | Noise suppression system | |
| Morelande et al. | Signal-to-noise ratio threshold effect in track before detect | |
| Jagadesh et al. | Modeling Target Detection and Performance Analysis of Electronic Countermeasures for Phased Radar. | |
| Kantor et al. | Mean-squared-error prediction for Bayesian direction-of-arrival estimation | |
| CN117849705A (en) | High-robustness multi-target DOA tracking method, system, chip and device | |
| CN115469314A (en) | Uniform circular array steady underwater target azimuth tracking method and system | |
| CN117849707A (en) | Self-adaptive anti-interference multi-target azimuth tracking method, system, chip and device | |
| CN109799477A (en) | A kind of sequential vehicle fingerprint localization method and device towards millimeter wave car networking | |
| O'Brien et al. | Single-snapshot robust direction finding | |
| Rajani et al. | Direction of arrival estimation by using artificial neural networks | |
| CN115932743A (en) | Cognitive radar method and device based on waveform adaptive selection | |
| Feng et al. | Constained adaptive monopulse algorithm based on sub-array | |
| RU2611556C1 (en) | Method for determination of multiposition sonar system efficiency |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |