To address the problem of underwater multi-sensor multi-target passive tracking in clutter,a distributed kernel mean embedding-based Gaussian belief propagation(DKME-GaBP)algorithm is proposed.First,a joint posterior ...To address the problem of underwater multi-sensor multi-target passive tracking in clutter,a distributed kernel mean embedding-based Gaussian belief propagation(DKME-GaBP)algorithm is proposed.First,a joint posterior probability density function(PDF)is established and factorized,and it is represented by the corresponding factor graph.Then,the GaBP algorithm is executed on this factor graph to reduce the computational complexity of data association.The factor graph of the GaBP consists of inner and outer loops.The inner loop is responsible for local track estimation and data association.The outer loop fuses information from different sensors.For the inner loop,the kernel mean embedding(KME)with a Gaussian kernel is designed to transform the strong nonlinear problem of local estimation into a linear problem in a high-dimensional reproducing kernel Hilbert space(RKHS).For the outer loop,a multi-sensor distributed fusion method based on KME is proposed to improve fusion accuracy by accounting for the distance among different PDFs in RKHS.The effectiveness and robustness of the DKME-GaBP are validated in the simulations.展开更多
The special sections of volume target are observed with acoustic vector intensity according to the difference among their radiated-noise characteristics, then three sections are tracked with Kalman filtering, and targ...The special sections of volume target are observed with acoustic vector intensity according to the difference among their radiated-noise characteristics, then three sections are tracked with Kalman filtering, and target size is estimated. Simulation results indicate that in ideal condition three sections of a ship can be tracked and ship's size can be estimated even though one of three sections can not be observed.展开更多
Single passive sensor tracking algorithms have four disadvantages: bad stability, longdynamic time, big bias and sensitive to initial conditions. So the corresponding fusion algorithm results in bad performance. A new...Single passive sensor tracking algorithms have four disadvantages: bad stability, longdynamic time, big bias and sensitive to initial conditions. So the corresponding fusion algorithm results in bad performance. A new error analysis method for two passive sensor tracking system is presented and the error equations are deduced in detail. Based on the equations, we carry out theoretical computation and Monte Carlo computer simulation. The results show the correctness of our error computation equations. With the error equations, we present multiple 'two station'fusion algorithm using adaptive pseudo measurement equations. This greatly enhances the tracking performance and makes the algorithm convergent very fast and not sensitive to initial conditions.Simulation results prove the correctness of our new algorithm.展开更多
Bearing-only passive tracking is regarded as a nonlinear hard tracking problem. There are still no completely good solutions to this problem until now. Based on current statistical model, the novel solution to this pr...Bearing-only passive tracking is regarded as a nonlinear hard tracking problem. There are still no completely good solutions to this problem until now. Based on current statistical model, the novel solution to this problem utilizing particle filter (PF) and the unscented Kalman filter (UKF) is proposed. The new solution adopts data fusion from two observers to increase the observability of passive tracking. It applies the residual resampling step to reduce the degeneracy of PF and it introduces the Markov Chain Monte Carlo methods (MCMC) to reduce the effect of the “sample impoverish”. Based on current statistical model, the EKF, the UKF and particle filter with various proposal distributions are compared in the passive tracking experiments with two observers. The simulation results demonstrate the good performance of the proposed new filtering methods with the novel techniques.展开更多
In the tracking problem for the maritime radiation source by a passive sensor,there are three main difficulties,i.e.,the poor observability of the radiation source,the detection uncertainty(false and missed detections...In the tracking problem for the maritime radiation source by a passive sensor,there are three main difficulties,i.e.,the poor observability of the radiation source,the detection uncertainty(false and missed detections)and the uncertainty of the target appearing/disappearing in the field of view.These difficulties can make the establishment or maintenance of the radiation source target track invalid.By incorporating the elevation information of the passive sensor into the automatic bearings-only tracking(BOT)and consolidating these uncertainties under the framework of random finite set(RFS),a novel approach for tracking maritime radiation source target with intermittent measurement was proposed.Under the RFS framework,the target state was represented as a set that can take on either an empty set or a singleton; meanwhile,the measurement uncertainty was modeled as a Bernoulli random finite set.Moreover,the elevation information of the sensor platform was introduced to ensure observability of passive measurements and obtain the unique target localization.Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source and demonstrate the superiority of the proposed approach in comparison with the traditional integrated probabilistic data association(IPDA)method.The tracking performance under different conditions,particularly involving different existence probabilities and different appearance durations of the target,indicates that the method to solve our problem is robust and effective.展开更多
For the problem of deterministic parameter estimate, the theoretical lower bound of esti- mate error is the Cramér-Rao bound; while for random parameter, the lower bound of estimate error is generally termed by P...For the problem of deterministic parameter estimate, the theoretical lower bound of esti- mate error is the Cramér-Rao bound; while for random parameter, the lower bound of estimate error is generally termed by Posterior Cramér-Rao Bound (PCRB). Under the background of passive tracking where the target's state can be seen as a time-varying random parameter, PCRB of the state estimate error is analyzed in this paper, and the relation between PCRB and varied condition is also fully in- vestigated using different simulation examples. The presented analytical method provides a theoretical base for performance assessment of all kinds of suboptimal estimate algorithms used in practice.展开更多
A marginalized particle filtering （MPF） approach is proposed for target tracking under the background of passive measurement. Essentially, the MPF is a combination of particle filtering technique and Kalman filter. ...A marginalized particle filtering （MPF） approach is proposed for target tracking under the background of passive measurement. Essentially, the MPF is a combination of particle filtering technique and Kalman filter. By making full use of marginalization, the distributions of the tractable linear part of the total state variables are updated analytically using Kalman filter, and only the lower-dimensional nonlinear state variable needs to be dealt with using particle filter. Simulation studies are performed on an illustrative example, and the results show that the MPF method leads to a significant reduction of the tracking errors when compared with the direct particle implementation. Real data test results also validate the effectiveness of the presented method.展开更多
In the state estimation of passive tracking systems, the traditional approximate expression for the Cramero-Rao lower bound （CRLB） does not take two factors into consideration, that is, measurement origin uncertaint...In the state estimation of passive tracking systems, the traditional approximate expression for the Cramero-Rao lower bound （CRLB） does not take two factors into consideration, that is, measurement origin uncertainty aad state noise. Such treatment is only valid in ideal situation but it is not feasible in actual situation. In this article, considering the two factors, the posterior Cramer-Rao lower bound （PCRLB） recursion expression for the error of bearing-only tracking is derived. Then, further analysis is carried out on the PCRLB. According to the final result, there are four main parameters that play a role in the performance of the PCRLB, that is, measurement noise, detection probability, state noise and clutter density, amongst which the first two have greater impact on the performance of the PCRLB than the others.展开更多
For maritime radiation source target tracking in particular electronic counter measures（ECM）environment,there exists two main problems which can deteriorate the tracking performance of traditional approaches.The frs...For maritime radiation source target tracking in particular electronic counter measures（ECM）environment,there exists two main problems which can deteriorate the tracking performance of traditional approaches.The frst problem is the poor observability of the radiation source.The second one is the measurement uncertainty which includes the uncertainty of the target appearing/disappearing and the detection uncertainty（false and missed detections）.A novel approach is proposed in this paper for tracking maritime radiation source in the presence of measurement uncertainty.To solve the poor observability of maritime radiation source target,using the radiation source motion restriction,the observer altitude information is incorporated into the bearings-only tracking（BOT）method to obtain the unique target localization.Then the two uncertainties in the ECM environment are modeled by the random fnite set（RFS）theory and the Bernoulli fltering method with the observer altitude is adopted to solve the tracking problem of maritime radiation source in such context.Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source,and also demonstrate the superiority of the method compared with the traditional integrated probabilistic data association（IPDA）method.The tracking performance under different conditions,particularly those involving different duration of radiation source opening and switching-off,indicates that the method to solve our problem is robust and effective.展开更多
In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too...In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.展开更多
Two target motion analysis (TMA) methods using multi-dimension information are studied, one is TMA with bearing-frequency and the other is TMA with multiple arrays. The optimization algorithm combining Gauss-Newton (G...Two target motion analysis (TMA) methods using multi-dimension information are studied, one is TMA with bearing-frequency and the other is TMA with multiple arrays. The optimization algorithm combining Gauss-Newton (G-N) method with Levenberg-Marquardt (L- M) method is applied to analyze the performance of target tracking with maximum likelihood estimation(MLE), and Monte Carlo experiments are presented. The results show that although the TMA with multi-dimension information have eliminated the maneuvers needed by conven- tional bearing-only TMA, but the application are not of universality展开更多
基金supported by the National Natural Science Foundation of China(Nos.62371173,U22A2044,and U22A2047)the Stable Supporting Fund of Acoustic Science and Technology Laboratory(NO.JCKYS2024604SSJS009)。
文摘To address the problem of underwater multi-sensor multi-target passive tracking in clutter,a distributed kernel mean embedding-based Gaussian belief propagation(DKME-GaBP)algorithm is proposed.First,a joint posterior probability density function(PDF)is established and factorized,and it is represented by the corresponding factor graph.Then,the GaBP algorithm is executed on this factor graph to reduce the computational complexity of data association.The factor graph of the GaBP consists of inner and outer loops.The inner loop is responsible for local track estimation and data association.The outer loop fuses information from different sensors.For the inner loop,the kernel mean embedding(KME)with a Gaussian kernel is designed to transform the strong nonlinear problem of local estimation into a linear problem in a high-dimensional reproducing kernel Hilbert space(RKHS).For the outer loop,a multi-sensor distributed fusion method based on KME is proposed to improve fusion accuracy by accounting for the distance among different PDFs in RKHS.The effectiveness and robustness of the DKME-GaBP are validated in the simulations.
基金This work was supported by the fund of special doctoral site fund of National education ministry.
文摘The special sections of volume target are observed with acoustic vector intensity according to the difference among their radiated-noise characteristics, then three sections are tracked with Kalman filtering, and target size is estimated. Simulation results indicate that in ideal condition three sections of a ship can be tracked and ship's size can be estimated even though one of three sections can not be observed.
文摘Single passive sensor tracking algorithms have four disadvantages: bad stability, longdynamic time, big bias and sensitive to initial conditions. So the corresponding fusion algorithm results in bad performance. A new error analysis method for two passive sensor tracking system is presented and the error equations are deduced in detail. Based on the equations, we carry out theoretical computation and Monte Carlo computer simulation. The results show the correctness of our error computation equations. With the error equations, we present multiple 'two station'fusion algorithm using adaptive pseudo measurement equations. This greatly enhances the tracking performance and makes the algorithm convergent very fast and not sensitive to initial conditions.Simulation results prove the correctness of our new algorithm.
基金This workis supported by national863project :No.2001AA422420 02
文摘Bearing-only passive tracking is regarded as a nonlinear hard tracking problem. There are still no completely good solutions to this problem until now. Based on current statistical model, the novel solution to this problem utilizing particle filter (PF) and the unscented Kalman filter (UKF) is proposed. The new solution adopts data fusion from two observers to increase the observability of passive tracking. It applies the residual resampling step to reduce the degeneracy of PF and it introduces the Markov Chain Monte Carlo methods (MCMC) to reduce the effect of the “sample impoverish”. Based on current statistical model, the EKF, the UKF and particle filter with various proposal distributions are compared in the passive tracking experiments with two observers. The simulation results demonstrate the good performance of the proposed new filtering methods with the novel techniques.
基金Project(61101186)supported by the National Natural Science Foundation of China
文摘In the tracking problem for the maritime radiation source by a passive sensor,there are three main difficulties,i.e.,the poor observability of the radiation source,the detection uncertainty(false and missed detections)and the uncertainty of the target appearing/disappearing in the field of view.These difficulties can make the establishment or maintenance of the radiation source target track invalid.By incorporating the elevation information of the passive sensor into the automatic bearings-only tracking(BOT)and consolidating these uncertainties under the framework of random finite set(RFS),a novel approach for tracking maritime radiation source target with intermittent measurement was proposed.Under the RFS framework,the target state was represented as a set that can take on either an empty set or a singleton; meanwhile,the measurement uncertainty was modeled as a Bernoulli random finite set.Moreover,the elevation information of the sensor platform was introduced to ensure observability of passive measurements and obtain the unique target localization.Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source and demonstrate the superiority of the proposed approach in comparison with the traditional integrated probabilistic data association(IPDA)method.The tracking performance under different conditions,particularly involving different existence probabilities and different appearance durations of the target,indicates that the method to solve our problem is robust and effective.
文摘For the problem of deterministic parameter estimate, the theoretical lower bound of esti- mate error is the Cramér-Rao bound; while for random parameter, the lower bound of estimate error is generally termed by Posterior Cramér-Rao Bound (PCRB). Under the background of passive tracking where the target's state can be seen as a time-varying random parameter, PCRB of the state estimate error is analyzed in this paper, and the relation between PCRB and varied condition is also fully in- vestigated using different simulation examples. The presented analytical method provides a theoretical base for performance assessment of all kinds of suboptimal estimate algorithms used in practice.
文摘A marginalized particle filtering （MPF） approach is proposed for target tracking under the background of passive measurement. Essentially, the MPF is a combination of particle filtering technique and Kalman filter. By making full use of marginalization, the distributions of the tractable linear part of the total state variables are updated analytically using Kalman filter, and only the lower-dimensional nonlinear state variable needs to be dealt with using particle filter. Simulation studies are performed on an illustrative example, and the results show that the MPF method leads to a significant reduction of the tracking errors when compared with the direct particle implementation. Real data test results also validate the effectiveness of the presented method.
文摘In the state estimation of passive tracking systems, the traditional approximate expression for the Cramero-Rao lower bound （CRLB） does not take two factors into consideration, that is, measurement origin uncertainty aad state noise. Such treatment is only valid in ideal situation but it is not feasible in actual situation. In this article, considering the two factors, the posterior Cramer-Rao lower bound （PCRLB） recursion expression for the error of bearing-only tracking is derived. Then, further analysis is carried out on the PCRLB. According to the final result, there are four main parameters that play a role in the performance of the PCRLB, that is, measurement noise, detection probability, state noise and clutter density, amongst which the first two have greater impact on the performance of the PCRLB than the others.
基金supported by the National Natural Science Foundation of China(No.61101186)
文摘For maritime radiation source target tracking in particular electronic counter measures（ECM）environment,there exists two main problems which can deteriorate the tracking performance of traditional approaches.The frst problem is the poor observability of the radiation source.The second one is the measurement uncertainty which includes the uncertainty of the target appearing/disappearing and the detection uncertainty（false and missed detections）.A novel approach is proposed in this paper for tracking maritime radiation source in the presence of measurement uncertainty.To solve the poor observability of maritime radiation source target,using the radiation source motion restriction,the observer altitude information is incorporated into the bearings-only tracking（BOT）method to obtain the unique target localization.Then the two uncertainties in the ECM environment are modeled by the random fnite set（RFS）theory and the Bernoulli fltering method with the observer altitude is adopted to solve the tracking problem of maritime radiation source in such context.Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source,and also demonstrate the superiority of the method compared with the traditional integrated probabilistic data association（IPDA）method.The tracking performance under different conditions,particularly those involving different duration of radiation source opening and switching-off,indicates that the method to solve our problem is robust and effective.
基金the Youth Science and Technology Foundection of University of Electronic Science andTechnology of China (JX0622).
文摘In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.
文摘Two target motion analysis (TMA) methods using multi-dimension information are studied, one is TMA with bearing-frequency and the other is TMA with multiple arrays. The optimization algorithm combining Gauss-Newton (G-N) method with Levenberg-Marquardt (L- M) method is applied to analyze the performance of target tracking with maximum likelihood estimation(MLE), and Monte Carlo experiments are presented. The results show that although the TMA with multi-dimension information have eliminated the maneuvers needed by conven- tional bearing-only TMA, but the application are not of universality
