Multiple Maneuvering Targets Research Articles

Enhanced multi-model multi-scan data association and tracking algorithm via convex variational inference

Tracking multiple targets with unknown measurement-to-target association and uncertain target dynamics is a significant problem that arises in various applications such as surveillance monitoring and intelligent transportation systems. In this paper, we propose an enhanced multi-model multi-scan data association algorithm to address the problem of tracking multiple maneuvering targets. First, we use a probabilistic graphical model to represent the joint distribution of the dynamic model indices, target state, and multi-scan data association variables. This formulation transforms the inference of marginal distributions into a Bethe free energy (BFE) problem. Next, to transform the BFE problem into a convex one, we demonstrate that the BFE function can be made convex through re-weighting. Additionally, we decompose the re-weighted BFE function into a block-wise sum form. We prove that under certain regularization conditions, each block of the re-weighted BFE is convex, ensuring convergence of the primal–dual coordinate ascent algorithm to the minimum of the overall re-weighted BFE. Finally, we provide a particle implementation of the proposed algorithm, accompanied by an analysis of its complexity. Simulation results indicate that the proposed algorithm exhibits favorable performance when compared to both the single-model multi-scan algorithm and the multi-model single-scan algorithm.

Multiple-model GLMB filter based on track-before-detect for tracking multiple maneuvering targets

Multiple-model GLMB filter based on track-before-detect for tracking multiple maneuvering targets

The trajectory motion model based TPHD and TCPHD filters for maneuvering targets

In this article, we present the TMM-TPHD and TMM-TCPHD filters, which are the alternative trajectory probability hypothesis density (TPHD) and the alternative trajectory cardinality probability hypothesis density (TCPHD) filters for tracking maneuvering targets. To describe the trajectory maneuver, we define the motion model history sequence of a trajectory as the trajectory motion model (TMM) variable. By incorporating the TMM, we augment the general trajectory model to accommodate the maneuver. Based on this augmented model, we propose the TMM extensions of the standard TPHD and TCPHD filters, namely TMM-TPHD and TMM-TCPHD filters. These filters can directly estimate the number, trajectory state, and TMM of maneuvering targets. In addition to the derivation of the filtering formulas, we also develop linear Gaussian mixture (LGM) implementations for TMM-TPHD and TMM-TCPHD filters. We verify the tracking performance and parameter stability of the proposed filters through tracking simulations involving multiple maneuvering targets.

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.

Fixed-time anti-saturation grouped cooperative guidance law with state estimations of multiple maneuvering targets

The problem of a grouped multiple missiles cooperative attack on multiple high maneuvering targets with a limited driving force is achieved by an anti-saturation fixed-time grouped cooperative guidance (FxTCG) law based on a sliding mode fixed-time disturbance observer (SM-FxTDO) in this study. First, the state estimation of each high maneuvering target within a fixed time is achieved by designing a sliding mode fixed-time disturbance observer. Second, the group cooperative guidance law is designed by using fixed-time theory, which can ensure the group consensus of multiple missiles strike times within a fixed time under the condition of input saturation. Then, the fixed time stability of the multi-missiles system is proven by using the bi-limit homogeneous theory and the Lyapunov function. Finally, the simulation results show the superiority of the designed observer and cooperative guidance law. The proposed observer can more effectively and accurately estimate the state of the high maneuvering target than the ESO. The proposed cooperative guidance law expands the number of attack targets and makes each group of multiple missiles attack the corresponding high maneuvering target under the conditions of an input saturation within a fixed time compared to the single-target cooperative law.

Multiple‐model generalised labelled multi‐Bernoulli filter with distributed sensors for tracking manoeuvring targets using belief propagation

AbstractThe multi‐sensor multiple‐model generalised labelled multi‐Bernoulli filter (MS‐MM‐GLMB) is presented for tracking multiple manoeuvring targets. And we develop efficient implementation for computing multi‐target posterior. The proposed implementation of the MS‐MM‐GLMB filter is composed of two main parts including centralised fusion with distributed sensors and posterior calculation using belief propagation. Specifically, the centralised fusion with distributed sensors is performed based on common predicted information to enable a full parallelisation. This fusion strategy yields the exact solution along with independence of the sensor ordering. More importantly, this fusion allows multiple sensor fusion with the MM‐GLMB filters for iterative update step for synchronous measurements. Subsequently, the belief propagation algorithm is adopted to implement the MS‐MM‐GLMB filter by efficiently calculating the marginal posterior distributions of GLMB form with superior tracking performance. The belief propagation solution to solving data association is presented for the computationally efficient centralised tracking of the manoeuvring targets. Compared with Gibbs‐based implementation, the proposed belief‐propagation‐based method performs well or better, especially in more challenging scenario.

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.

Distributed robust cooperative tracking against multiple maneuvering targets based on random finite set

This paper considers the distributed multi-target tracking problem with mobile sensor network. Based on the random finite set and consensus methods, a distributed multi-target tracking algorithm is designed by local filtering stage and consensus fusion stage. In the local filtering stage, the state and measurement information of multi-target are described by random finite set. Each node in the sensor network obtains the state estimates of multi-target individually by applying the probability hypothesis density filtering algorithm. To reduce the computational complexity, the Jacobian matrix and Hessian matrix are used to calculate the priori probability density. With the local neighboring interactions, the estimate results of multi-target system are obtained after the consensus fusion stage. Besides, the stochastic boundedness of the estimation error of the distributed multi-target tracking algorithm is proved by constructing a stochastic process. In the final, a simulation example is given to verify the multi-target tracking performance of the proposed algorithm.

Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion

The effective detection of unmanned aerial vehicle (UAV) targets is of great significance to guarantee national military security and social stability. In recent years, with the development of communication and control technology, the movement of UAVs has become increasingly flexible and complex, presenting diverse trajectory forms and different motion models in different phases. The Gaussian mixture probability hypothesis density filter incorporating the linear Gaussian jump Markov system approach (LGJMS-GMPHD) provides an efficient method for tracking multiple maneuvering targets, as applied to the switching of motions between a set of models in a Markovian chain. However, in practice, the motion model parameters of targets are generally unknown and the model switching is uncertain. When the preset filtering model parameters are mismatched, the tracking performance is dramatically degraded. In this paper, within the framework of the LGJMS-GMPHD filter, a deep-learning-based multiple model tracking method is proposed. First, an adaptive turn rate estimation network is designed to solve the filtering model mismatch caused by unknown turn rate parameters in coordinate turn models. Second, a filter state modification network is designed to solve the large tracking errors in the maneuvering phase caused by uncertain motion model switching. Finally, based on simulations of multiple maneuvering targets in cluttered environments and experimental field data verification, it can be concluded that the proposed method has strong adaptability to multiple maneuvering forms and can effectively improve the tracking performance of targets with complex maneuvering motion.

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.

Multi-sensor Multiple Maneuvering Targets Tracking Algorithm under Greedy Measurement Partitioning Mechanism

A novel method Interacting Multiple Mode Multi-Sensor Multi-target Multi-Bernoulli (IMM-MS-MeMBer) filter to track multiple maneuvering targets in low detection probability scenario is proposed. At the prediction stage of the IMM-MS-MeMBer filter, model probability of the target is adaptively updated by utilizing the current measurement information, and then the mixed prediction of the target state is executed; At the update stage of the IMM-MS-MeMBer filter, the greedy multi-sensor measurement partitioning strategy is employed in measurement partition step, the posterior probability density of the target is updated by using the divided set of measurements and the IMM-MS-MeMBer filter; In addition, the IMM-MS-MeMBer filter utilizes the target angle and Doppler information to realize the simultaneous estimation of the position and speed of multiple maneuvering targets. Numerical experiments verify the superior performance of the IMM-MS-MeMBer filter.

A multiple-model generalized labeled multi-Bernoulli filter based on blocked Gibbs sampling for tracking maneuvering targets

In this paper, an efficient implementation of the multiple-model generalized labeled multi-Bernoulli filter (MM-GLMB) is presented for tracking multiple maneuvering targets. To alleviate the generation of the redundant components, the original two-staged implementations of MM-GLMB filter are integrated into a single step bringing the benefit that only one truncation procedure is required per iteration. In this study, the authors take the convergence behavior of the Gibbs sampling into full consideration to improve the convergence rate. The blocked Gibbs sampling over lattice Gaussian distribution based solution to the implementation of MM-GLMB filter is proposed to greatly relax the computational load. The numerical simulations demonstrate the efficacy of the proposed algorithm with low computational cost.

Data-Driven Simultaneous Multibeam Power Allocation: When Multiple Targets Tracking Meets Deep Reinforcement Learning

In this article, we aim to study radar power allocation problem in multiple maneuvering targets tracking from a new perspective by deep learning, to develop a data-driven approach, which enables radar to learn a good (hopefully the best) power allocation policy from data. In order to enhance the utilization effectiveness of radar power resource, we present a cognitive design based on control framework incorporating an actor-critic variation of deep reinforcement learning called deep deterministic policy gradient. Specifically, at each tracking interval, the transmit power is allocated with control framework based on prior information, which is represented by the Fisher information matrix (FIM) of prior information of target's state computed using target's dynamic models. However, as targets are maneuvering, the dynamic models are always unavailable. Thanks to the sequential manner of target tracking, we consider to integrate a memory based on recurrent neutral network into the control framework for the estimation of FIM of prior information by characterizing the output as parameters for a high-dimensional Gaussian distribution of target's state instead of a deterministic state. Theoretical analysis has established that the derived covariance matrix is essentially the inverse of the FIM of prior information. Simulation results have shown the effectiveness of the proposed power allocation method.

Distributed multiple model joint probabilistic data association with Gibbs sampling-aided implementation

This paper proposes a new distributed multiple model multiple manoeuvring target tracking algorithm. The proposed tracker is derived by combining joint probabilistic data association (JPDA) with consensus-based distributed filtering. Exact implementation of the JPDA involves enumerating all possible joint association events and thus often becomes computationally intractable in practice. We propose a computationally tractable approximation of calculating the marginal association probabilities for measurement-target mappings based on stochastic Gibbs sampling. In order to achieve scalability for a large number of sensors and high tolerance to sensor failure, a simple average consensus algorithm-based information JPDA filter is proposed for distributed tracking of multiple manoeuvring targets. In the proposed framework, the state of each target is updated using consensus-based information fusion while the manoeuvre mode probability of each target is corrected with measurement probability fusion. Simulations clearly demonstrate the effectiveness and characteristics of the proposed algorithm. The results reveal that the proposed formulation is scalable and much more efficient than classical JPDA without sacrificing tracking accuracy.

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.

The multiple model multi-Bernoulli filter based track-before-detect using a likelihood based adaptive birth distribution

In this paper, a novel multi-Bernoulli filter based track-before-detect (MB-TBD) algorithm is proposed, to solve the tracking problem of multiple maneuvering targets. We incorporate the multiple motion models into the basic MB-TBD filtering, and then derive the closed-form recursive equations including both prediction and update steps based on optimal Bayesian filtering. Moreover, to accommodate the case of unknown prior knowledge for target births, a likelihood based adaptive birth distribution for the MB-TBD is proposed. The implementation of the proposed algorithm with adaptive birth distribution is presented using sequential Monte Carlo (SMC) technique. The performance of the proposed algorithm is demonstrated in challenging scenarios including multiple highly maneuvering objects.

Communication‐awareness joint beams and power allocation scheme of radar network for manoeuvring targets tracking

The coexistence of radar and communication system is a crucial task owing to the shortage of spectrum resources. In this study, the authors consider a multiple manoeuvring targets tracking problem via a radar network in the presence of several non-cooperative, spatially random, and spectral overlapped communication areas (CAs). Instead of costly inner pattern optimisation, a communication awareness joint beam and power allocation (CJBPA) scheme of radar network is proposed to achieve high flexibility in coexistence. In CJBPA, radars and CAs treat the signals from each other as interference, which are formulated and discussed from the perspective of spectrum and space. The CJBPA optimises the overall tracking performance, which is characterised by the posterior Cramer-Rao lower bound under the consideration of coexistence. The resultant non-convex problem is relaxed and decoupled as an integrated two-staged optimisation. To accelerate the numerical procedures of the optimisations, custom-built primal dual interior point methods are derived by the structure information of the problems. The effectiveness of the proposed CJBPA scheme is assessed through simulations, showing that by using spatial and spectral information of undesired signals, the tracking performance can be optimised while the impacts of interference on both radars and CAs are restricted.

Coherent Integration Algorithm for a Maneuvering Target Based on Frequency Spectrum Segment Processing

This paper considers the coherent integration problem for a low-observable maneuvering target, where the velocity and acceleration result in range migration (RM) and Doppler frequency migration (DFM) within the coherent pulse interval. A novel method based on the frequency spectrum segment processing (FSSP) and the segmental Lv's distribution (SLVD) is proposed to realize the long-time coherent integration for multiple maneuvering targets. In this method, FSSP is proposed to eliminate the RM effect by dividing the received signal into several subband signals and expanding the range resolution of the subband signals. Then SLVD is applied to achieve the coherent integration of the subband signals and accumulate the energy of all the subband signals coherently. The proposed method can realize the coherent integration for multiple maneuvering targets without any prior knowledge of the targets' motion. The simulation and experimental results demonstrate the effectiveness of the proposed algorithm.

MM-GLMB Filter-Based Sensor Control for Tracking Multiple Maneuvering Targets Hidden in the Doppler Blind Zone

To track multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we have proposed an MM-GLMB-DBZ tracker based on the latest multiple model generalized labeled multi-Bernoulli (MM-GLMB) filter. To further enhance the tracking performance, this paper combines the sensor control technique to the MM-GLMB-DBZ tracker. Macroscopically, the proposed algorithm consists of the MM-GLMB-DBZ tracker and a controller. Unlike conventional control approaches where separate prediction and update implementations are usually adopted, the proposed control algorithm constructs a systematic process flow for the joint prediction and update implementation of GLMB-like filters. Moreover, inside the core controller module, we apply the previously designed safety indicator and reward function for avoiding the DBZ, and derive the Cauchy-Schwarz divergence (CSD) compatible with the tracker. Hence, this control algorithm considers such factors as the safety of the sensor itself, the DBZ avoidance, and the improvement of the tracking accuracy. Numerical examples verify the effectiveness of the proposed control scheme, showing that it is significantly better than the random control strategy, the original MM-GLMB-DBZ tracker without the control technology, and the state-of-the-art control approach.