Multiple Maneuvering Target Tracking Research Articles

Multiple maneuvering target tracking based on hierarchical Dirichlet process and hidden Markov model

This paper is concerned with multiple maneuvering target tracking problems. Although the interacting multiple model (IMM) algorithm can be used for maneuvering targets tracking, the performance is limited as the motion mode set of the target is fixed in advance. In this paper, we address the problem of multiple maneuvering targets with unknown maneuvering models in cluttered environments. A finite hidden Markov model (HMM) is developed with truncated Hierarchical Dirichlet process (HDP) to recognize the unknown and changing motion modes. Further, the HDP-HMM is introduced into multiple hypothesis tracking (MHT) and probabilistic multiple hypothesis tracking (PMHT) approaches for multiple maneuvering targets tracking with data association uncertainty. To joint estimate the HDP-HMM parameters and target states, the particle learning and variational inference approaches are used, and the solutions of HDP-HMM-MHT and HDP-HMM-PMHT algorithms are obtained based on Rao-Blackwellized particle filter and variational Bayesian methods, respectively. As the changes of maneuver models can be learned using HDP, the multi-target tracking accuracy of the proposed algorithm is improved. Simulation results show that the proposed algorithms outperform the traditional IMM-MHT method with fixed model sets.

LPI-constrained collaborative transmit beampattern optimization and resource allocation for maneuvering targets tracking in colocated MIMO radar network

Considering tracking multiple maneuvering targets with the presence of a hostile interceptor in the modern electronic warfare (EW) environment, how to maximize targets tracking performance while ensuring system’s anti-interception capability is of essential significance for the radar network consisting of multiple colocated MIMO radar (CMR) nodes. In this paper, we propose a novel low probability of intercept (LPI) constrained collaborative beampattern optimization and resource allocation (LPI-CBORA) strategy for multiple maneuvering targets tracking (MMTT) in the CMR network (CMRN). More precisely, the LPI-CBORA strategy is formulated as a nonconvex optimization problem, where the node-target assignment, beampattern design and bandwidth allocation are collaboratively considered for the first time. To tackle the nonconvex problem effectively, an efficient two-stage solution algorithm incorporating with the successive convex approximation (SCA) and the cyclic minimization algorithm (CMA) is developed. Numerical simulations are provided to verify the effectiveness and superiority of the proposed LPI-CBORA strategy.

Joint Strategy of Power and Bandwidth Allocation for Multiple Maneuvering Target Tracking in Cognitive MIMO Radar With Collocated Antennas

In this article, we propose a joint strategy of power and bandwidth allocation (JSPBA) for multiple maneuvering target tracking (MMTT) in the multi-input multi-output (MIMO) radar with collocated antennas. The basis of our strategy is to optimally allocate the transmitted resources of power and effective bandwidth by the prior information in the closed-loop system of cognitive tracking. On account of the predicted conditional Cramér-Rao lower bound (PC-CRLB) offering a more accurate and time-sensitive lower bound than the standard posterior CRLB (PCRLB), the PC-CRLB of the range, Doppler frequency, and direction-of-arrival (DOA) is derived, normalized and adopted as the optimization criterion. Moreover, in order to solve the nonconvex problem, the initial nonconvex problem is converted into a series of convex problems, which are further formulated as the standard semi-definite programming (SDP) problems and then be solved, by introducing the convex relaxation technique and the two-step solution technique. Simulations confirm the superiority of the proposed JSPBA algorithm, in terms of the overall tracking accuracy in the MMTT scenario.

Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter

This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms.

A message passing approach for multiple maneuvering target tracking

This paper considers the problem of detecting and tracking multiple maneuvering targets, which suffers from the intractable inference of high-dimensional latent variables that include target kinematic state, target visibility state, motion mode-model association, and data association. A unified message passing algorithm that combines belief propagation (BP) and mean-field (MF) approximation is proposed for simplifying the intractable inference. By assuming conjugate-exponential priors for target kinematic state, target visibility state, and motion mode-model association, the MF approximation decouples the joint inference of target kinematic state, target visibility state, motion mode-model association into individual low-dimensional inference, yielding simple message passing update equations. The BP is exploited to approximate the probabilities of data association events since it is compatible with hard constraints. Finally, the approximate posterior probability distributions are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue between the estimations of target kinematic state and target visibility state and decisions on motion mode-model association and data association. The performance of the proposed algorithm is demonstrated by comparing with the well-known multiple maneuvering target tracking algorithms, including interacting multiple model joint probabilistic data association, interacting multiple model hypothesis-oriented multiple hypothesis tracker and multiple model generalized labeled multi-Bernoulli.

Tracking Multiple Maneuvering Targets Hidden in the DBZ Based on the MM-GLMB Filter

Tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ) is a challenging problem. To overcome the complicated problem, we proposed a tracker based on the multiple model probability hypothesis density (MM-PHD) filter. However, the PHD filter is only the first-order moment approximation of the multi-target Bayesian filter, and it cannot output track labels in principle. In order to improve the tracking performance, another novel tracker is proposed in this paper. To track multiple maneuvering targets, the proposed tracker is built on the latest multiple model generalized labeled multi-Bernoulli (MM-GLMB) filter. Moreover, it incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Finally, a measurement-driven adaptive track initiation is introduced to address the fixed track initiation problem of the standard MM-GLMB filter. It is demonstrated through numerical examples that the proposed tracker outperforms the existing work significantly, especially in terms of both accuracy and robustness of cardinality estimation.

Augmented input estimation in multiple maneuvering target tracking

This paper presents augmented input estimation (AIE) for multiple maneuvering target tracking. Multi-target tracking (MTT) is based on two main parts, data association and estimation. In data association (DA), the best observations are assigned to the considered tracks. In real conditions, the number of observations is more than targets and also locations of observations are often so scattered that the association between targets and observations cannot be done simply. In this case, for general MTT problems with unknown numbers of targets, we present a Markov chain MonteCarlo DA (MCMCDA) algorithm that approximates the optimal Bayesian filter with low complexity in computations. After DA, estimation and tracking should be done. Since in general cases, many targets can have maneuvering motions, then AIE is proposed to cover both the non-maneuvering and maneuvering parts of motion and the maneuver detection procedure is eliminated. This model with an input estimation (IE) approach is a special augmentation in the state space model which considers both the state vector and the unknown input vector as a new augmented state vector. Some comparisons based on the Monte-Carlo simulations are also made to evaluate the performances of the proposed method and other older methods in MTT.

Maneuvering multi-target tracking based on variable structure multiple model GMCPHD filter

The traditional multiple model cardinalized probability hypothesis density (MMCPHD) filter uses a fixed model set for all targets. It is clearly inefficient and may cause the decrease of performance in the situations where the size of the model set is large and the scene is complicated. This paper presents a variable structure multiple model (VSMM) version of the Gaussian mixture cardinalized probability hypothesis density (GMCPHD) filter based on the expected mode augmentation (EMA) for the multiple maneuvering target tracking. The GMCPHD filter which is suitable for the VSMM is derived, thus different model sets can be used for different targets. Then EMA algorithm which is an efficient model set adaptation method is introduced and applied to the VSMM version of the GMCPHD (VSMM-GMCPHD) filter. This method costs less computational time and has better estimate precision than that of the traditional multiple model version of GMCPHD (MM-GMCPHD) filter. The gating technique is utilized to further improve the computational efficiency. Simulation results verify the performance of the proposed methods. The estimate precision of VSMM-GMCPHD filters with and without the gate are almost the same, however the former one is more cost-effective. Both of the two VSMM-GMCPHD filters outperform the MM-GMCPHD filter.

Tracking multiple maneuvering targets using a sequential multiple target Bayes filter with jump Markov system models

Tracking multiple maneuvering (MM) targets is a well-known and challenging problem because of clutter and several uncertainties existing in target motion mode, target detection, and data association. An efficient solution to this problem is the Gaussian mixture probability hypothesis density (GM-PHD) filter for jump Markov system (JMS) models. However, this solution is inapplicable to circumstances where detection probability is low because the GM-PHD filter for JMS models requires a high detection probability. To address this problem, we propose a sequential multiple target (MT) Bayes filter for JMS models. To track MM targets that are switching among a set of linear Gaussian models, an implementation process of this filter for linear Gaussian jump Markov MT models is also developed. The conclusion that the novel filter is more efficient for tracking MM targets than the existing filter for JMS models in circumstances of low detection probability is validated by simulation results.

Extensions of the CBMeMBer filter for joint detection, tracking, and classification of multiple maneuvering targets

This paper addresses the problem of joint detection, tracking and classification (JDTC) of multiple maneuvering targets in clutter. The multiple model cardinality balanced multi-target multi-Bernoulli (MM-CBMeMBer) filter is a promising algorithm for tracking an unknown and time-varying number of multiple maneuvering targets by utilizing a fixed set of models to match the possible motions of targets, while it exploits only the kinematic information. In this paper, the MM-CBMeMBer filter is extended to incorporate the class information and the class-dependent kinematic model sets. By following the rules of Bayesian theory and Random Finite Set (RFS), the extended multi-Bernoulli distribution is propagated recursively through prediction and update. The Sequential Monte Carlo (SMC) method is adopted to implement the proposed filter. At last, the performance of the proposed filter is examined via simulations.

Adaptive parameter particle CBMeMBer tracker for multiple maneuvering target tracking

Cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter has been demonstrated as a promising algorithm for multi-target tracking, and the multi-model (MM) method has been incorporated into the CBMeMBer filter to solve the problem of multiple maneuvering target tracking. However, it is difficult to construct a proper set of models due to the unknown maneuvering parameters of the targets. Moreover, the number of models may increase exponentially if more unknown parameters have to be taken into account to match the target motion modes, which may lead to prohibitive computational complexity. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the framework of CBMeMBer filters, so that the model with unknown maneuvering parameter can be modified adaptively by using the selected parameter particles. Moreover, a particle labeling technique is introduced in the proposed algorithm in order to obtain the individual target track, which results in the adaptive parameter particle filter CBMeMBer (APPF-CBMeMBer) tracker. Simulation results show that the proposed algorithm can effectively track multiple maneuvering targets with abruptly changing parameters and exhibit better robustness than those of the well-known MM-based approaches.

NOVEL MULTI-TARGET TRACKING ALGORITHM FOR AUTOMOTIVE RADAR

Tracking multiple maneuvering targets for automotive radar is a vital issue. To this end, a novel DS-UKGMPHD algorithm which combines diagraph switching (DS), unscented Kalman (UK) filter and Gaussian mixture probability hypothesis density (GMPHD) filter is proposed in this paper. The algorithm is capable of tracking a varying number of target cars detected by automotive radar with nonlinear measurement models in a cluttered environment. In addition, variable structure is used to accommodate various target motions in real world. Simulation results demonstrate the superiority of the presented algorithm to IMM-UKGMPHD filter in terms of estimation accuracy of both number and states.

Joint range ambiguity resolving and multiple maneuvering targets tracking in clutter via MMPHDF-DA

It is difficult to track multiple maneuvering targets of which the number is unknown and time-varying, especially when there is range ambiguity. The random finite sets (RFS) based probability hypothesis density filter (PHDF) is an effective solution to the problem of multiple targets tracking. However, when tracking multiple targets via the range ambiguous radar, the problem of range ambiguity has to be solved. In this paper, a multiple model PHDF and data association (MMPHDF-DA) based method is proposed to address multiple maneuvering targets tracking with range ambiguous radar in clutter. Firstly, by introducing the turn rate of target and the discrete pulse interval number (PIN) as components of target state vector, and modeling the incremental variable of the PIN as a three-state Markov chain, the problem of multiple maneuvering targets tracking with range ambiguity is converted into a hybrid state filtering problem. Then, by implementing a novel “track-estimate” oriented association with the filtering results of the hybrid filter, target tracks are provided at each time step. Simulation results demonstrate that the MMPHDF-DA can estimate target state as well as the PIN simultaneously, and succeeds in multiple maneuvering target tracking with range ambiguity in clutter. Simulation results also demonstrate that the MMPHDF-DA can overcome the limitation of the Chinese Remainder Theorem for range ambiguity resolving.

Joint detection, tracking and classification of multiple maneuvering targets based on the linear Gaussian jump Markov probability hypothesis density filter

This paper is to account for joint detection, tracking, and classification (JDTC) of an unknown and time-varying number of maneuvering targets in clutter. Unlike most tracking algorithms that use only the kinematic measurements, this paper proposes a recursive JDTC algorithm to exploit the coupled information between target detection, tracking and classification based on the Gaussian mixture probability hypothesis density filter (GMPHDF) in the linear Gaussian jump Markov systems (LGJMS) multitarget model. The original LGJMS-GMPHDF, devoted to joint detect and track all potential targets utilizing an identical and fixed set of models, has been modified to incorporate target class information and the class-dependent kinematic model set. The mutual dependence between target kinematics and class is exploited twice: first to construct the combined model sets, then to compute the combined measurement likelihood. The proposed algorithm is illustrated via a simulation example involving tracking of two closely spaced parallel moving targets and two crossing moving targets from different classes, where targets can appear and disappear.

Multi-Sensor Interactive Multi-Model PHD Filter for Maneuvering Multi-Target Tracking

In maneuvering multiple targets tracking problem, Probability Hypothesis Density(PHD) filter can be used to estimate the multi-target state and the number at each time step, but single model method may not provide accurate estimates. In this paper, an interactive multiple model PHD filter is proposed, and then multiple sensor interactive multiple model PHD filter is proposed to improve the tracking of multiple maneuvering targets. PHD particle filter implementation is used to perform the proposed method consisting of multiple maneuvering targets.

Probability hypothesis density filter based on strong tracking MIE for multiple maneuvering target tracking

Taking into account the difficulties of multiple maneuvering target tracking due to the unknown target number and the uncertain acceleration, a novel multiple maneuvering target tracking algorithm based on the Probability Hypothesis Density (PHD) filter and Modified Input Estimation (MIE) technique is proposed in this paper. First, the unknown acceleration vector is added to the target state to form a new augmented state vector. Then, strong tracking filter multiple fading factors are introduced to the MIE method which can adjust the prediction covariance and the corresponding filter gain at different rates in real time, so that the MIE method can adaptively track high maneuvering targets well. Finally, we combine this adaptive MIE method with the PHD filter, which can effectively track multiple maneuvering targets without much prior information. Simulation results show that the proposed algorithm has a higher tracking precision and a better real-time performance than the conventional maneuvering target tracking algorithms.

Multiple Target Tracking Using Cheap Joint Probabilistic Data Association Multiple Model Particle Filter in Sensors Array

Joint multiple target tracking and classification is an important issue in many engineering applications. In recent years, multiple sensor data fusion has been extensively investigated by researchers in a variety of disciplines. Indeed, combining results issued from multiple sensors can provide more accurate information than using a single sensor. In the present paper we address the problem of data fusion for joint multiple maneuvering target tracking and classification in cluttered environment where centralized versus decentralized architectures are often opposed. The proposal advocates a hybrid approach combining a Particle Filter (PF) like method to deal with system nonlinearities and Fitgerald’s Cheap Joint Probabilistic Data Association Filter CJPDAF for the purpose of data association and target estimation problems, yielding CJPDA-PF algorithm. While the target maneuverability is tackled using a combination of a Multiple Model Filter (MMF) and CJPDAF, which yields CJPDA-MMPF algorithm. Consequently, at each particle level (of the particle filter), the state of the particle is evaluated using the suggested CJPDAMMF. In case of several sensors, the centralized fusion architecture and the distributed architecture in the sense of Federated Kalman Filtring are investigated and compared. The feasibility and the performances of the proposal have been demonstrated using a set of Monte Carlo simulations dealing with two maneuvering targets with two distinct operation modes and various clutter densities.

基于MMPHDF 的多机动目标联合检测、跟踪与分类算法

To account for joint detection, tracking and classification (JDTC) of multiple maneuvering targets in dense clutter environment, this paper introduces an algorithm based on the multiple-model probability hypothesis density filter (MMPHDF). The MMPHDF can be applied to jointly detect and track multiple maneuvering targets, but its filter performance deteriorates rapidly in dense clutter environment. The proposed JDTC algorithm (MMPHDF-JDTC) utilizes target classification information and target kinematic information to simultaneously estimate the time-varying number of targets, their kinematic states and types. The main idea is to augment the kinematic state vector with the target class vector, and then use their combined measurement likelihood to integrate the target classification information into the update process of MMPHDF. The combined target kinematic state and class measurement likelihood improves the discrimination of different class targets and clutter, so better detection and tracking performance can be expected compared with the original MMPHDF. Alternately, accurate detection and tracking results is the foundation for correct target classification. A particle implementation of the MMPHDF-JDTC has been given. Simulation results validate the above conclusions.

Multiple Maneuvering Target Tracking by Improved Particle Filter Based on Multiscan JPDA

The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent‐noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers. The equivalent‐noise approach converts the problem of maneuvering target tracking to that of state estimation in the presence of nonstationary process noise with unknown statistics. A novel method for identifying the nonstationary process noise is proposed in the particle filter framework. Furthermore, a particle filter based multiscan Joint Probability Data Association (JPDA) filter is proposed to deal with the data association problem in a multiple maneuvering target tracking. In the proposed multiscan JPDA algorithm, the distributions of interest are the marginal filtering distributions for each of the targets, and these distributions are approximated with particles. The multiscan JPDA algorithm examines the joint association events in a multiscan sliding window and calculates the marginal posterior probability based on the multiscan joint association events. The proposed algorithm is illustrated via an example involving the tracking of two highly maneuvering, at times closely spaced and crossed, targets, based on resolved measurements.

A novel algorithm for multiple maneuvering target tracking

It is difficult to track multiple maneuvering targets in clutter due to the uncertain acceleration and the disturbance of the clutter. Taking these into account, a strong tracking modified input estimation (STMIE) is presented in this paper, which is combined with maximum entropy fuzzy joint probabilistic data association for multiple maneuvering target tracking. Strong tracking multiple fading factors are introduced in order to enhance the tracking performance of MIE for high maneuvering targets. The prediction covariance can be adjusted in real time by the multiple time-varying fading factors and the different data channels are faded at different rates. Simulations show the effectiveness of the proposed method for the cross and high maneuvering target tracking. Compared with the conventional current statistical model (CS) combined with maximum entropy fuzzy joint probabilistic data association, the proposed algorithm has higher tracking accuracy and good engineering application prospect. Key words: Multiple maneuvering target tracking, data association, strong tracking filter, modified input estimation, multiple fading factor.