CN112630783A - Passive sonar target tracking method - Google Patents
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- 238000010606 normalization Methods 0.000 claims abstract description 7
- 230000004083 survival effect Effects 0.000 claims abstract description 7
- 230000037323 metabolic rate Effects 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims description 9
- 230000006872 improvement Effects 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 6
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- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/66—Sonar tracking systems
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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
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Abstract
The invention relates to a sonar target tracking method, in particular to a passive sonar target tracking method, which comprises the following steps: initializing parameters, including determining the number of particles, the metabolic rate and the prior distribution of the particles, and setting the initial positions and states of the particles; calculating a particle space state set, a survival state set and a normalization weight value set; resampling is carried out; and outputting a state estimation result. The passive sonar target tracking method provided by the invention combines particle filtering and a pre-detection tracking algorithm and applies the combined algorithm to the target tracking of the passive sonar, thereby solving the problems of wrong tracking, lost tracking, false alarm and the like of the conventional algorithm in a complex low signal-to-noise ratio, strong interference and multi-target tracking environment, and improving the tracking performance of the equipment.
Description
Technical Field
The invention relates to a sonar target tracking method, in particular to a passive sonar target tracking method.
Background
The passive sonar mainly depends on target radiation signal noise to detect underwater targets on water, and compared with active sonar, the passive sonar has better secrecy and anti-interference performance. However, due to the complexity and specificity of the underwater environment and the specificity of the passive sonar, the sonar is difficult to detect weak targets, and target tracking of the passive sonar becomes a very challenging task.
The DBT method comprises the steps of firstly detecting and then tracking, wherein the DBT method is a traditional target tracking mode, but in an underwater environment with a low signal-to-noise ratio, the detection rate can be greatly reduced, and the traditional DBT method is greatly restricted. And the TBD effectively combines the detection and the tracking without being restricted by a detection result, directly processes the detected previous layer or even more original data, and avoids information loss caused by the data in the post-processing process. On the other hand, "track before detection, TBD" can jointly process multi-frame data, and the influence of signal-to-noise ratio is reduced by using the continuity of target motion in adjacent time periods, so that the tracking performance is improved.
Kalman Filtering (KF) is a tracking filtering method that is currently very widely used. However, this filter is limited by a linear system and is difficult to implement in practical engineering. Extended Kalman Filter (EKF) and traceless Kalman Filter (UKF) improved by Kalman Filter break through the restriction of linear system, but these methods are all premised on gaussian disturbance and do not necessarily meet the actual tracking condition. With the continuous development of computer technology, another well-known nonlinear Filter, Particle Filter (PF), has received high attention from researchers at home and abroad. The Monte Carlo Method (MCM) proposed by MERROPOLIS et al in the 40's of the 20 th century is an rudimentary form of the particle filtering algorithm, which was later modified by Hammersley et al, which has a particle degradation problem. Aiming at the problem of particle degradation, Gordon et al in the last 90 th century proposed a resampling method, which greatly improved the problem of particle degradation. The particle filter is not limited by Gaussian disturbance and a linear system, and is more widely applied.
Based on the background, the invention improves the pre-detection tracking algorithm based on particle filtering and uses the pre-detection tracking algorithm in the target tracking of the passive sonar. The conventional passive sonar target tracking algorithm can better track a low-interference and strong target. However, the conventional algorithm is weak in complex environments such as multiple targets, strong interference, low signal-to-noise ratio and the like. In general, a conventional passive sonar target tracking algorithm detects a target first and then tracks a detection result. However, part of the target information is lost in the detection process, and the subsequent tracking performance is reduced. And the tracking algorithm before detection does not carry out target detection before tracking, thereby reducing target information loss and improving target tracking performance.
Disclosure of Invention
In order to solve the problems, the invention provides a passive sonar target tracking method which combines particle filtering and a pre-detection tracking algorithm and applies the combined algorithm to the target tracking of passive sonar, solves the problems of complex low signal-to-noise ratio, strong interference, frequent false tracking, tracking loss, false alarm and the like of the conventional algorithm in a multi-target tracking environment and has high tracking performance, and the specific technical scheme is as follows:
a passive sonar target tracking method comprises the following steps: initializing parameters, including determining the number of particles, the metabolic rate and the prior distribution of the particles, and setting the initial positions and states of the particles; calculating a particle space state set, a survival state set and a normalization weight value set; resampling is carried out; and outputting a state estimation result.
Further, S1, initialization parameter: setting the number N of particles, a state transition matrix PI and the particle initial rate mu m; according to the priori knowledge, generating N x m particles, and setting the corresponding states as: e0 iTable 1 is survival; the remaining particle states are set as: e0 iDeath is indicated by 0; state transition matrix of particles: PI ═ 1-Pb,Pb；Pd,1-Pd](ii) a Wherein, PnRepresenting the birth rate, PdRepresenting mortality; dividing the observation region into n observation units each having a length of deltaL(ii) a S2, transferring the particle state surviving at the previous moment according to the state transfer matrix PI to form the particle state set at the current moment, aiming at the particle state set at the previous momentParticle state Ek-1 iGenerating a random number mu between (0-1); if E isk-1 i0 and mu<PbWhen E is greaterk i1 is ═ 1; otherwise E k i0; if E isk-1 i1 and mu<PdWhen E is greaterk i0; otherwise Ek i1 is ═ 1; s3, if E k-1 i0 and Ek iIf the particle number is 1, sampling to generate a new particle according to the prior distribution probability; if E isk-1 i1 and Ek iIf the value is 1, a new particle is generated by using a state transfer function F, then the weight value is taken as a weight value according to the likelihood function ratio, and normalization processing is carried out to generate the weight value of the particle: { omega [ [ omega ] ]k iI ═ 1 … N }; determining a likelihood function ratio model: before detection, the target tracking measurement value is the signal energy intensity of the sensor in each direction; let n resolution elements, which may be denoted as j, { j ═ 1 … n }, per observation region, and each resolution element has a length ΔLThen the measurement value received at time k is
Wherein
The signal strength of the resolution unit at the moment j is expressed by the expression:
in the formula
To target at thetakSignal strength of spotted object, noisekCorresponding observation noise; when a target appears in an observation unit, the signal energy of the observation unit is increased and the observation unit is close to the observation unitThe signal energy of the element is also improved, and the improvement degree of the element and the improvement degree of the observation unit present an inverse relation; structure of the device
Where epsilon represents the degree of scattering of the sensor energy. Obtaining a likelihood function:
s4, resampling: generating a new particle space position by taking the space position and the weight of each particle as input values, and setting the weight of all the particles as 1/N;
s5, outputting the result:
furthermore, a plurality of sonars are adopted during tracking, the sonars are arranged into a sonar array along a linear array, the sonar array performs signal preprocessing on the received radiation sound signals, and beam domain data after beam forming is output.
Furthermore, the tracking mode is a tracking mode before detection, and a particle filter nonlinear filter is adopted.
Compared with the prior art, the invention has the following beneficial effects:
the passive sonar target tracking method provided by the invention combines particle filtering and a pre-detection tracking algorithm and applies the combined algorithm to the target tracking of the passive sonar, thereby solving the problems of wrong tracking, lost tracking, false alarm and the like of the conventional algorithm in a complex low signal-to-noise ratio, strong interference and multi-target tracking environment, and improving the tracking performance of the equipment.
Drawings
FIG. 1 is a comparison graph of simulation data of a target real track and three algorithms under different algorithm conditions when SNR is 21;
FIG. 2 is a comparison graph of simulation data of a target real track and three algorithms under different algorithm conditions when the SNR is 9;
FIG. 3 is a comparison graph of simulation data of a target real track and three algorithms under different algorithm conditions when the SNR is 1.5;
FIG. 4 is a first chart of a sea trial bearing;
fig. 5 is a second chart of the azimuth history of a certain sea test.
Detailed Description
The invention will now be further described with reference to the accompanying drawings.
As shown in fig. 1 to 5, a passive sonar target tracking method includes the following steps:
initializing parameters, including determining the number of particles, the metabolic rate and the prior distribution of the particles, and setting the initial positions and states of the particles;
calculating a particle space state set, a survival state set and a normalization weight value set;
resampling is carried out;
and outputting a state estimation result.
S1, initialization parameters:
setting the number N of particles, a state transition matrix PI and the particle initial rate mu m;
according to the priori knowledge, generating N x m particles, and setting the corresponding states as:
E0 itable 1 is survival;
the remaining particle states are set as:
E0 ideath is indicated by 0;
state transition matrix of particles:
PI＝[1-Pb,Pb；Pd,1-Pd]；
wherein, PbRepresenting the birth rate, PdRepresenting mortality;
dividing the observation region into n observationsUnits each unit having a length of deltaL；
S2, transferring the particle state surviving at the previous moment according to the state transfer matrix PI to form a particle state set at the current moment, and aiming at the particle state E at the previous momentk-1 iGenerating a random number mu between (0-1);
if E isk-1 i0 and mu<PbWhen E is greaterk i1 is ═ 1; otherwise Ek i＝0；
If E isk-1 i1 and mu<PdWhen E is greaterk i0; otherwise Ek i＝1；
S3, if E k-1 i0 and Ek iIf the particle number is 1, sampling to generate a new particle according to the prior distribution probability;
if E isk-1 i1 and Ek iIf the value is 1, a new particle is generated by using a state transfer function F, then the weight value is taken as a weight value according to the likelihood function ratio, and normalization processing is carried out to generate the weight value of the particle:
{ωk i,i＝1…N}；
determining a likelihood function ratio model:
before detection, the target tracking measurement value is the signal energy intensity of the sensor in each direction; let n resolution elements, which may be denoted as j, { j ═ 1 … n }, per observation region, and each resolution element has a length ΔLThen the measurement value received at time k is
Wherein
The signal strength of the resolution unit at the moment j is expressed by the expression:
in the formula
To target at thetakSignal strength of spotted object, noisekCorresponding observation noise;
when a target appears in an observation unit, the signal energy of the observation unit is improved, and the signal energy close to the observation unit is also improved, wherein the improvement degree of the observation unit and the improvement degree of the observation unit are in an inverse relation; structure of the device
Where epsilon represents the degree of scattering of the sensor energy. Obtaining a likelihood function:
s4, resampling: generating a new particle space position by taking the space position and the weight of each particle as input values, and setting the weight of all the particles as 1/N;
s5, outputting the result:
the state model of the object motion is:
Xk＝F*(Xk-1,Vk-1)
wherein Xk＝(θ,Δθ,Ik) Theta is azimuth, Delta theta is azimuth rate of change, IkTarget power spectrum information, F ═ 1, T, 0; 0,1, 0; 0,0,1]T is the time interval, Vk-1Is the system process noise.
During tracking, a plurality of sonars are adopted and arranged into a sonar array along a linear array, and the sonar array performs signal preprocessing on received radiation acoustic signals and outputs beam domain data after beam forming.
The tracking mode is a tracking mode before detection, and a particle filter nonlinear filter is adopted.
1. And (3) algorithm simulation verification:
the simulation parameters are as follows: tracking total time duration Ts 60s, time interval T1 s, number of particles 2000, particle birth rate PbParticle mortality rate P ═ 0.05dThe target moving range angle is 0 to 180, the target initial azimuth is 20, the initial azimuth change speed is 0.45, the change range is 0.2 to 2, the target energy initial size is 0.5, the change range is 0.2 to 1, and the signal-to-noise ratios are respectively set to 1.5db, 9db and 21 db.
As shown in fig. 1 to fig. 3, the results of comparing the tracking performance of the particle filter algorithm and the tracking performance of the kalman filter algorithm and the particle filter algorithm before detection based on the particle filter under different signal-to-noise ratios are shown. When the signal-to-noise ratio is 21db, the three algorithms can track the target more effectively. But from an enlarged view around 30s, it can be seen that the particle filter based pre-detection tracking algorithm (PF-TBD) is closer to the true trajectory. As the signal-to-noise ratio decreases, the KF algorithm fails to track to the target in the initial stage until convergence to the target position around 25s and a large deviation occurs at 48s, as in the 9db condition shown in fig. 2. The PF is basically able to track the upper target, although the error is slightly larger within the initial 20 s. The PF-TBD algorithm accurately tracks the target at all times except when it fails to find the target within the first 3s, and the error of the algorithm is observed to be minimal. As shown in fig. 3, when the signal-to-noise ratio is reduced to 1.5db, neither the PF algorithm nor the KF algorithm can track the target, while the PF-TBD algorithm, although jumping at the initial stage, generally completes the task of target tracking. Therefore, it can be found from fig. 1 that the tracking performance of the three algorithms is reduced with the reduction of the signal-to-noise ratio, but under the condition of low signal-to-noise ratio, the PF-TBD algorithm is obviously superior to the PF algorithm and the KF algorithm. This demonstrates the superiority of PF-TBD at low signal to noise ratios.
2. Verification of test data
As shown in fig. 4 and 5, the diagram is a direction history diagram of a certain sea test, and it can be seen from the diagram that the tracking algorithm before detection based on particle filtering can stably track weak targets and cross targets under strong interference.
The passive sonar target tracking algorithm before detection based on particle filtering is based on particle filtering, and the conventional thought of tracking after detection is abandoned, and beam domain data is directly processed, so that the problems of target information loss and the like in the detection process are avoided. In addition, particle filtering, which is a nonlinear filter that is receiving attention in recent years, breaks through the restriction of using kalman filtering under a linear system, and the application scenarios are wider.
The method is mainly used for target tracking of the passive sonar, and can greatly improve the target tracking performance compared with the conventional algorithm. This algorithm has three advantages:
1. multi-frame data joint processing can be realized, and the target tracking precision under the condition of low signal-to-noise ratio is improved;
2. the detection is not needed, the beam domain data is directly processed, and the information loss caused by multi-layer processing is reduced;
3. compared with Kalman filtering, particle filtering is more widely applied and is not limited by a linear system.
The technical principle of the present invention is described above in connection with specific embodiments. The description is made for the purpose of illustrating the principles of the invention and should not be construed in any way as limiting the scope of the invention. Based on the explanations herein, those skilled in the art will be able to conceive of other embodiments of the present invention without inventive step, which shall fall within the scope of the appended claims.
Claims (4)
1. A passive sonar target tracking method is characterized by comprising the following steps:
initializing parameters, including determining the number of particles, the metabolic rate and the prior distribution of the particles, and setting the initial positions and states of the particles;
calculating a particle space state set, a survival state set and a normalization weight value set;
resampling is carried out;
and outputting a state estimation result.
2. The passive sonar target tracking method according to claim 1,
s1, initialization parameters:
setting the number N of particles, a state transition matrix PI and the particle initial rate mu m;
according to the priori knowledge, generating N x m particles, and setting the corresponding states as:
E0 itable 1 is survival;
the remaining particle states are set as:
E0 ideath is indicated by 0;
state transition matrix of particles:
PI＝[1-Pb，Pb；Pd，1-Pd]；
wherein, PbRepresenting the birth rate, PdRepresenting mortality;
dividing the observation region into n observation units each having a length of deltaL；
S2, transferring the particle state surviving at the previous moment according to the state transfer matrix PI to form a particle state set at the current moment, and aiming at the particle state E at the previous momentk-1 iGenerating a random number mu between (0-1);
if E isk-1 i0 and mu < PbWhen E is greaterk i1 is ═ 1; otherwise Ek i＝0；
If E isk-1 i1 and mu < PdWhen E is greaterk i0; otherwise Ek i＝1；
S3, if Ek-1 i0 and Ek iIf the particle number is 1, sampling to generate a new particle according to the prior distribution probability;
if E isk-1 i1 and Ek iIf the value is 1, a new particle is generated by using a state transfer function F, then the weight value is taken as a weight value according to the likelihood function ratio, and normalization processing is carried out to generate the weight value of the particle:
{ωk i，i＝1…N}；
determining a likelihood function ratio model:
before detection, the target tracking measurement value is the signal energy intensity of the sensor in each direction; let n resolution elements, which may be denoted as j, { j ═ 1 … n }, per observation region, and each resolution element has a length ΔLThen the measurement value received at time k is Zk＝{Ik jJ ═ 1 … n }, where Ik jThe signal strength of the resolution unit at the moment j is expressed by the expression:
in the formula hk j(θk) To target at thetakSignal strength of spotted object, noisekCorresponding observation noise;
when a target appears in an observation unit, the signal energy of the observation unit is improved, and the signal energy close to the observation unit is also improved, wherein the improvement degree of the observation unit and the improvement degree of the observation unit are in an inverse relation; construction hk j(θk)：
Where epsilon represents the degree of scattering of the sensor energy. Obtaining a likelihood function:
s4, resampling: generating a new particle space position by taking the space position and the weight of each particle as input values, and setting the weight of all the particles as 1/N;
s5, outputting the result:
3. the passive sonar target tracking method according to claim 1,
during tracking, a plurality of sonars are adopted and arranged into a sonar array along a linear array, and the sonar array performs signal preprocessing on received radiation acoustic signals and outputs beam domain data after beam forming.
4. The passive sonar target tracking method according to claim 1,
the tracking mode is a tracking mode before detection, and a particle filter nonlinear filter is adopted.
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