Target Tracking In Clutter Research Articles

Radar Waveform Selection for Maneuvering Target Tracking in Clutter with PDA-RBPF and Max-Q-Based Criterion

Radar Waveform Selection for Maneuvering Target Tracking in Clutter with PDA-RBPF and Max-Q-Based Criterion

Variational Approximation for Adaptive Extended Target Tracking in Clutter With Random Matrix

As a computationally efficient framework, the random matrix approach can simultaneously estimate the kinematic state and extent of the extended target. For the extended target tracking in clutter, the measurement origin uncertainty, the unknown detection probability and measurement rate challenge the existing methods. In this paper, we propose the Beta Gamma Gaussian inverse Wishart filter based on the variational approximation. The proposed method takes the association event as an unknown parameter with a prior distribution. Following a more rigorous path, we derive an approximate posterior distribution of the unknowns using the analytical techniques of variational Bayesian inference. The joint estimations of the kinematic state, extent, detection probability, measurement rate and association events are obtained in this work. The simulation results illustrate the effectiveness and robustness of the proposed approach.

Adaptive mixture approximation for target tracking in clutter

Target tracking is a state estimation problem common in many practical scenarios like air traffic control, autonomous vehicles, marine radar surveillance and so on. In a Bayesian perspective, when phenomena like clutter are present, most existing tracking algorithms must deal with association hypotheses which may grow in number over time. In that case, the posterior state distribution can quickly become computationally intractable, and approximations must necessarily be introduced. In this work, the impact of the number of hypotheses and of reduction procedures is investigated both in terms of computational resources and tracking performances. For this purpose, a recently developed adaptive mixture model reduction algorithm is considered in order to assess its performances when applied to the problem of single object tracking in the presence of clutter and to provide some interesting insights into the addressed problem.

An IPDA based target existence assisted Bayesian detector for target tracking in clutter

In this paper, an integrated probabilistic data association (IPDA) based target existence assisted Bayesian detector (TE-BD) is derived for target tracking in the clutter. The key idea of the proposed method is to feed back the predicted target position information and the existence probability from the tracker to the detector to facilitate target detection and tracking, while ensuring radar can terminate track quickly when target disappears. We first derive the prior information assisted target detection model, and then present the general steps of the TE-BD scheme based on the IPDA framework. Simulation results verify the effectiveness of the proposed IPDA-TE-BD w.r.t. the existing works.

Reinforcement Learning-Based Data Association for Multiple Target Tracking in Clutter.

Data association is a crucial component of multiple target tracking, in which each measurement obtained by the sensor can be determined whether it belongs to the target. However, many methods reported in the literature may not be able to ensure the accuracy and low computational complexity during the association process, especially in the presence of dense clutters. In this paper, a novel data association method based on reinforcement learning (RL), i.e., the so-called RL-JPDA method, has been proposed for solving the aforementioned problem. In the presented method, the RL is leveraged to acquire available information of measurements. In addition, the motion characteristics of the targets are utilized to ensure the accuracy of the association results. Experiments are performed to compare the proposed method with the global nearest neighbor data association method, the joint probabilistic data association method, the fuzzy optimal membership data association method and the intuitionistic fuzzy joint probabilistic data association method. The results show that the proposed method yields a shorter execution time compared to other methods. Furthermore, it can obtain an effective and feasible estimation in the environment with dense clutters.

Joint threshold optimization and power allocation of cognitive radar network for target tracking in clutter

Networked radar systems have the ability to enhance the tracking performance by working in a cognitive manner. In the cognitive radar network, resource allocation technique can be employed to improve the system performance. In this paper, a joint threshold optimization and power allocation (JTOPA) strategy for target tracking by radar network in clutter is proposed containing two main stages. At the detection stage, we introduce the Bayesian tracker-aware detector to adaptively adjust the threshold based on the predicted target state information. At the transmitting stage, the limited power resource is optimally assigned to each radar node. By incorporating the improved information reduction factor (IIRF), the Bayesian Cramér-Rao lower bound (BCRLB) is utilized as the optimization criterion for the JTOPA strategy. The JTOPA is a two-variable nonconvex optimization problem. We develop a spectral projected gradient based method to solve the problem. Simulations show that the proposed JTOPA strategy effectively improves the tracking performance compared with the uniform threshold setting and power allocation in the cognitive radar network.

JPBA of ARN for target tracking in clutter

Networked radar systems can be used to improve target tracking performance. In the asynchronous radar network (ARN) system, the radars may work at different sampling rates, bringing difficulties in resource allocation. In this study, the authors propose a joint power and bandwidth allocation (JPBA) strategy to track a target by ARN in clutter. First, they build an asynchronous resource allocation model, in which a periodic allocation strategy is suggested. Second, they derive the Bayesian Cramer–Rao lower bound by incorporating the information reduction factor as a performance metric for the JPBA strategy. Third, the asynchronous power and bandwidth allocation optimisation problem are formulated by minimising the tracking root-mean-square error with given power and bandwidth budgets, which is non-convex but monotonic. Finally, by introducing the branch-reduce-and-bound algorithm, a global solution to the optimisation problem is obtained. Numerical results verify the validity of the proposed JPBA strategy for target tracking by ARN.

Modified smoothing data association for target tracking in clutter

Tracking an unknown target in noisy environment is difficult especially when the target is maneuvering and has unknown trajectory. Smoother uses measurements from future scans to estimate the target state in past scan. This requires the fusion of forward and backward prediction. However, due to uncertain target motions and low detection probabilities, backward prediction could not associate with forward prediction which results in inequitable fusion pair and thus, smoothing performance could not be improved. To cope with these difficulties, the proposed algorithm modifies the fixed-lag smoothing data association based on the integrated probabilistic data association (IPDA) algorithm and a new algorithm called modified smoothing IPDA (MSIPDA) is developed. MSIPDA utilizes two IPDA filters to obtain forward IPDA (fIPDA) track and backward (bIPDA) track estimation in each scan. Each fIPDA prediction generates multiple fusion pairs in association with bIPDA multi-track prediction. These fusion pairs are created in the form of components. As a result, multiple smoothing components are formed with their smoothing component data association probabilities for computing MSIPDA track components state estimate. In addition, the smoothing data association probabilities upgrade the forward track which makes the forward track more powerful for target tracking in clutter. The numerical assessment of MSIPDA is verified using simulations. The result shows significant false track discrimination performance in comparison to existing algorithms.

Collaborative detection and power allocation framework for target tracking in multiple radar system

In reality, multiple radar system (MRS) may have some limited resource, such as the data computation capacity of the fusion center and the transmit energy of each radar. To better exploit its limited system resource, a collaborative detection and power allocation (CDPA) scheme is developed for the application of target tracking in clutter. The basis of the CDPA scheme is to use optimization technique to control the false alarm rate (FAR) and transmit power of each radar in view of the aforementioned resource constraints, while achieving better target state estimation accuracy. The Bayesian Cramér–Rao lower bound is derived, relaxed, and subsequently utilized, as the optimization criterion for the CDPA strategy. The resulting nonlinear and nonconvex optimization problem consists of two adaptable vectors, one for FAR selection and the other for power allocation. By introducing an auxiliary vector, a fast two-step solution technique is presented to jointly select the FAR and distribute the transmit power. Simulation results demonstrate that, with given data computation capability and system total power budget, the CDPA scheme can evidently expand the detection range, increase the resource utilization efficiency of the MRS, and improve the target tracking accuracy.

Target Tracking in Clutter with Extremum Seeking Control for Adaptive Detection Thresholding.

If the signal strength obtained from sonar is higher than the predefined detection threshold, it is considered as a candidate for target tracking. This detection threshold is a parameter that affects the detection probability of targets and the distribution of clutter measurements, so it is important to determine a proper threshold to improve target tracking performance. There are various techniques for adjusting the detection threshold. To apply these techniques, it is assumed that the probability density functions of the signal strength for clutter are known in advance. However, in a real environment, the probability density function of the signal strength is unknown in general. In this paper, we propose a detection threshold control method using extremum seeking control in realistic environments. The extremum seeking control is a method used in complex nonlinear systems. We propose a new structure for extremum seeking control that is applicable to detection processes with nonlinear characteristics. This structure is used to adjust the detection threshold of the received signal amplitude to make the estimated clutter measurement density converge to a designed clutter measurement density to achieve the best target tracking performance in the current environment. Simulation studies for the proposed extremum seeking control applied to target tracking in an unknown clutter signal distribution demonstrate the effectiveness and improved target tracking performance.

Joint power and beam allocation of opportunistic array radar for multiple target tracking in clutter

This paper proposes a joint power and beam allocation scheme based on chance-constraint programming (CCP) for multiple target tracking of opportunistic array radar (OAR) in cluttered domains. The objective of the scheme is to cope with complex environments, unknown target information, and insufficient resource of radar system installed in mobile platforms operating over prolonged time periods. In this scheme, the total power of transmitting beams used in estimation process is minimized by effective allocation over the targets so that the desired tracking performance is attained. Allowing for the randomness of target RCS, the CCP used for balancing tracking performance and power resource is introduced conditioned on a specified confidence level. To track the targets more efficiently, the beams are allocated to the targets with large prior Cramér–Rao lower bound (CRLB). Then, in conjunction with an information reduction factor (IRF), the tracking Bayesian CRLB (BCRLB) of the illuminated targets can be considered as a measurement criteria for resource allocation in clutter. Through Conditional Value at Risk (CVaR), the CCP is relaxed to a convex problem solved by Lagrange multipliers method with Kuhn–Tucker (KT). The simulation results verify the validity and practicability of the resource allocation scheme.

Power allocation scheme for target tracking in clutter with multiple radar system

In this paper, a power allocation (PA) scheme is developed for target tracking in clutter using multiple radar system (MRS). The key idea of the PA scheme is to maximize the tracking performance in clutter for a predetermined power budget of the MRS. We use the Bayesian Cramér-Rao lower bound as the criterion, and formulate the PA scheme as a mathematical optimization problem. We analytically show that this optimization problem is nonconvex, and adopt a spectral projected gradient algorithm to obtain a constrained stationary solution to this problem. Simulation results demonstrate that the PA process can evidently expand the detection range, increase the resource utilization efficiency of the MRS, and improve the target tracking accuracy with a given power budget.

Integrated particle smoothing for target tracking in clutter

The integrated particle filter (IPF) is an algorithm for single-target tracking in clutter, which incorporates the probability of target existence (PTE) into the traditional particle filter as a track quality measure for false track discrimination (FTD). This study investigates two IPF-based fixed-interval smoothing algorithms: the IP smoothing (IPS) algorithm and the IP-Rauch–Tung–Striebel backward smoothing (IP-RTSBS) algorithm, both of which are capable of trajectory estimation and FTD. The IPS algorithm fuses the propagations for each pair of forward IPF and backward IPF particles to obtain the smoothing propagation that is used to update the track state by applying all available measurements in the current scan. The IP-RTSBS algorithm employs the forward filtering backward smoothing approach to smooth the trajectory state, which is then applied to the RTS smoothing methodology to obtain the smoothing propagation used to update the PTE. As a result, both FTD and trajectory estimation are improved. The smoothing benefits of the two algorithms are validated in the simulations, where a sliding batch mode with overlapping measurements is utilised to limit the smoothing lag.

An SVSF-Based Generalized Robust Strategy for Target Tracking in Clutter

Autonomous self-drive requires intelligence and cognition that relies on observations and tracking of the state of motion of surrounding vehicles. This information can be acquired by using sensors; however, these are often affected by clutter and noise that, in turn, introduce the issues of estimation and data origin uncertainty into the tracking system. The most popular methods for estimation and tracking are based on the well-studied Kalman filter (KF). KF is optimal when noise is white and remains so despite uncertainties in the filter model; the robustness and stability of the KF is affected if this condition is not met. The smooth variable structure filter (SVSF) is a relatively new method that is more robust to disturbances and uncertainties. The SVSF ensures stability by using a discontinuous corrective term that maintains estimates to within a subspace of the true state trajectory. The discontinuous corrective term results in chattering that is removed by using a smoothing boundary layer. In this paper, a generalized covariance formulation of the SVSF and a generalized optimal time-varying smoothing boundary layer are proposed. The generalized optimal SVSF is then combined with a joint probabilistic data association technique for target tracking. The robustness and accuracy of the new form of filtering and data association is validated and comparatively analyzed by its application to an experimental traffic monitoring system based on Light Detection and Ranging.

Smoothing data association for target trajectory estimation in cluttered environments

For heavily cluttered environments with low target detection probabilities, tracking filters may fail to estimate the true number of targets and their trajectories. Smoothing may be needed to refine the estimates based on collected measurements. However, due to uncertainties in target motions, heavy clutter, and low target detection probabilities, the forward prediction and the backward prediction may not be properly matched in the smoothing algorithms, so that the smoothing algorithms may fail to detect the true target trajectories. In this paper, we propose a new smoothing algorithm to overcome such difficulties. This algorithm employs two independent integrated probabilistic data association (IPDA) tracking filters: one running forward in time (fIPDA) and the other running backward in time (bIPDA). The proposed algorithm utilizes bIPDA multi-tracks in each fIPDA path track for fusing through data association to obtain the smoothing innovation in a fixed-lag interval. The smoothing innovation is used to obtain the smoothing data association probabilities which update the target trajectory state and the probability of target existence. The fIPDA tracks are updated after smoothing using the smoothing data association probabilities, which makes the fIPDA path tracks robust for maneuvering target tracking in clutter. This significantly improves the target state estimation accuracy compared to the IPDA. The proposed algorithm is called fixed-lag smoothing data association based on IPDA (FLIPDA-S). A simulation study shows that the proposed algorithm improves false track discrimination performance for maneuvering target tracking in clutter.

A novel resource scheduling method of netted radars based on Markov decision process during target tracking in clutter

In order to improve the radio frequency stealth ability of phased array radars, a novel resource scheduling method of the radar network for target tracking in clutter is presented. Firstly, the relationship model between radar resource and tracking accuracy is built, and the sampling interval, power, and waveform will influence predicted error covariance matrix through transition matrix and measurement noise. Then, radar resource scheduling algorithm based on Markov decision process which is converted to be a binary optimization problem is proposed, and an improved binary wind-driven optimization method is presented to solve that problem. The radar and its radiation parameters will be selected for better radio frequency stealth performance and tracking accuracy. Simulation results show that the proposed algorithm not only has excellent tracking accuracy in clutter but also has better radio frequency stealth ability comparing with other methods.

Global sensor selection for maneuvering target tracking in clutter

Global sensor selection for maneuvering target tracking in clutter

Global sensor selection for maneuvering target tracking in clutter

This paper studies the problem of sensor selection for maneuvering target tracking in the cluttered environment. By modeling the target dynamics as jump Markov linear systems, a decentralized tracking algorithm is developed by applying the extended Kalman filter and the probabilistic data association technique. A cost function that minimizes the expected filtered mean square position error is utilized and a sensor selection scheme is proposed. A numerical example is provided to illustrate the effectiveness of the proposed approach.

Optimal coordination method of opportunistic array radars for multi‐target‐tracking‐based radio frequency stealth in clutter

AbstractOpportunistic array radar is a new radar system that can improve the modern radar performance effectively. In order to improve its radio frequency stealth ability, a novel coordination method of opportunistic array radars in the network for target tracking in clutter is presented. First, the database of radar cross section for targets is built, then the signal‐to‐noise ratio for netted radars is computed according to the radar cross section and range of target. Then the joint probabilistic data association algorithm of tracking is improved with consideration of emitted power of the opportunistic array radar, which has a main impact on detection probability for tracking in clutter. Finally, with the help of grey relational grade and covariance control, the opportunistic array radar with the minimum radiated power will be selected for better radio frequency stealth performance. Simulation results show that the proposed algorithm not only has excellent tracking accuracy in clutter but also saves much more radiated power comparing with other methods.

Joint Detection and Tracking Processing Algorithm for Target Tracking in Multiple Radar System

In this paper, a joint detection and tracking processing (JDTP) algorithm is proposed for target tracking in clutter using a multiple radar system. In this paper, the data association events are formed with a reasonable assumption that each radar can at most receive one measurement originated from a target. Moreover, we explore the idea of feeding the information from the tracker to the detector. In this scenario, the tracker can guide the detectors of multiple radars where to look for a target while keeping the constant false alarm rate property. From a practical point of view, the detection threshold is depressed near where a target is expected to be and elevated where it is unexpected. Simulation results demonstrate the efficiency of the proposed JDTP algorithm, in terms of the detection and the tracking performance, when compared with the existing works.