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

Airborne Radar Multi-target Detection for Tracking in Clutter Using Non-Uniform Pulse Interval Transceivers Design

Airborne Radar Multi-target Detection for Tracking in Clutter Using Non-Uniform Pulse Interval Transceivers Design

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.

Passive Radar Tracking in Clutter Using Range and Range-Rate Measurements

Passive bistatic radar research is essential for accurate 3D target tracking, especially in the presence of missing or low-quality bearing information. Traditional extended Kalman filter (EKF) methods often introduce bias in such scenarios. To overcome this limitation, we propose employing the unscented Kalman filter (UKF) for handling the nonlinearities in 3D tracking, utilizing range and range-rate measurements. Additionally, we incorporate the probabilistic data association (PDA) algorithm with the UKF to handle cluttered environments. Through extensive simulations, we demonstrate a successful implementation of the UKF-PDA framework, showing that the proposed method effectively reduces bias and significantly advances tracking capabilities in passive bistatic radars.

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.

Performance Study of Distance-Weighting Approach with Loopy Sum-Product Algorithm for Multi-Object Tracking in Clutter.

In this paper, we explore the performance of the distance-weighting probabilistic data association (DWPDA) approach in conjunction with the loopy sum-product algorithm (LSPA) for tracking multiple objects in clutter. First, we discuss the problem of data association (DA), which is to infer the correspondence between targets and measurements. DA plays an important role when tracking multiple targets using measurements of uncertain origin. Second, we describe three methods of data association: probabilistic data association (PDA), joint probabilistic data association (JPDA), and LSPA. We then apply these three DA methods for tracking multiple crossing targets in cluttered environments, e.g., radar detection with false alarms and missed detections. We are interested in two performance metrics: tracking accuracy and computation time. LSPA is known to be superior to PDA in terms of the former and to dominate JPDA in terms of the latter. Last, we consider an additional DA method that is a modification of PDA by incorporating a weighting scheme based on distances between position estimates and measurements. This distance-weighting approach, when combined with PDA, has been shown to enhance the tracking accuracy of PDA without significant change in the computation burden. Since PDA constitutes a crucial building block of LSPA, we hypothesize that DWPDA, when integrated with LSPA, would perform better under the two performance metrics above. Contrary to expectations, the distance-weighting approach does not enhance the performance of LSPA, whether in terms of tracking accuracy or computation time.

Hybrid PDA/FIR Filtering for Preceding Vehicle Tracking Using Automotive Radars

This paper proposes a novel single vehicle tracking algorithm with enhanced reliability for automotive radar systems. The proposed algorithm overcomes the weaknesses of the probabilistic data association filter (PDAF) in single-target tracking in clutter. The PDAF is successful in normal situations, but may fail to track a target owing to various factors, such as the initialization errors and the sudden changes in the target motion. The proposed algorithm can recover the PDAF from failures using an assisting finite impulse response (FIR) filter. The FIR filter operates only when the PDAF cannot track a target properly, and additionally offers state estimate and estimation error covariance to reset the PDAF. The proposed algorithm, the hybrid PDAF/FIR filter (HPFF), combines the PDAF and FIR filter, and hence shows enhanced reliability. Simulations of preceding vehicle tracking using an automotive radar demonstrate the effect and performance of the proposed HPFF.

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.

Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT

Underwater bearing-only multitarget tracking in clutter environment is challenging because of the measurement nonlinearity, range unobservability, and data association uncertainty. In terms of the principle of expectation maximization, combining the extended Kalman filter (EKF) and unscented Kalman filter algorithm(UKF), a new bearing-only multi-sensor multitarget tracking via probabilistic multiple hypothesis tracking(PMHT) algorithm is proposed. The PMHT algorithm introduces an association variable to deal with the data association uncertainty problem between the measurements and the targets. Furthermore, the EKF-based PMHT for multi-sensor multitarget system is simplified, which obviate the need to "stack" the synthetic measurements and can reduce the computation cost. The estimation accuracy of the EKF based on PMHT approach and UKF based on PMHT approach in simulation experiments for underwater bearing-only cross-moving targets and closely spaced targets for the case of stationary multiple observations and maneuvering single observation under dense clutter environment is analyzed. The experimental results demonstrate that the present algorithm is very well in a highly clutter environment and its computational load is low, which confirms the effectiveness of the algorithm to the bearing-only multitarget tracking in dense clutter.

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.

Improved Active Sonar Tracking in Clutter Using Integrated Feature Data

Target tracking has traditionally focused on the use of kinematic information, such as bearing, range, position, or speed. In some cases, it is possible to extract additional target information that may be useful in the maintenance of reliable tracks, or the establishment of target type or identity. Several methods have been proposed for the use of feature information in target tracking. In this paper, we discuss and evaluate its use as a component of the cumulative likelihood ratio used for track management and contact assignment in a track-oriented multihypothesis tracker (MHT) using a sorted branch and bound approach to contact assignment. The active sonar sea trial data used in this evaluation, Clutter09, include feature values based on the perceptual capabilities of the human auditory system. Three measurement scenarios are considered. Comparisons between the baseline case, which used only kinematic information, and several feature-aided cases showed that the performance of the tracker improved noticeably when feature information was used. The MHT, which was working at a depth of six scans, was also able to tolerate seven times as many false contacts in the feature-aided cases as in the baseline cases. This corresponds to improved detection and tracking of targets that are weaker, or at greater range, or in a more cluttered environment. Results were also significantly better when compared with cases where the feature information was used to prefilter the contacts entering the tracker or to postfilter the tracks produced by the tracker.

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.

Consensus-based distributed detection and tracking in clutter with random existence of target

Abstract We consider the distributed detection and tracking in the wireless sensor networks (WSNs) where the target may appear in or disappear from the surveillance region at random instances. We derive two consensus-based distributed detection and tracking algorithms. The first algorithm is for the distributed detection and tracking without clutter. With the cubature rule, we derive a centralized implementation with a fictitious fusion center (FC) and then a consensus-based distributed implementation without FC. The second algorithm is for the distributed detection and tracking in the clutter environment. Because of the clutter, it gives rise to more challenging scenarios. Based on the probabilistic data association filter, we develop a distributed algorithm for all nodes to cooperatively deal with the data association uncertainty. Numerical results illustrate that the distributed estimate at each node can be close to the global estimate with the FC which has the information of all nodes in the network.

Post-tracking range-rate estimation with up/down linear-frequency-modulated pulses

Range-rate (RR) estimation in active sonar is most often accomplished using continuous-wave (CW) pulses. An alternative that is more suited for use with slowly moving objects in reverberation-limited conditions is presented and evaluated. As described by A. Rihaczek [Principles of High Resolution Radar, McGraw Hill, 1969], the approach entails simultaneously transmitting up- and down-sweeping linear frequency modulated pulses (LFM-U&D). The key enabler of the LFM-U&D is the complementary range-bias information relative to using a single LFM sweep type (LFM-U|D). Novel in the proposed LFM-U&D approach is the use of multiple pings after tracking to form improved range and RR estimates. Cramer-Rao lower bounds (CRLBs) on estimator variance as a function of the number of pings illustrate that LFM-U&D is a compromise between the CW and the standard LFM-U|D. Estimates of the RR of clutter tracks from the Target and Reverberation Experiment (TREX13) were used to evaluate the approach. An order of magnitude reduction in the inter-quartile range (IQR) of the RR estimate was observed for LFM-U&D relative to LFM-U|D, which allows a significant reduction in the number of false tracks. The full reduction in IQR predicted by the CRLB was seen to be limited by the effects of surface motion.Range-rate (RR) estimation in active sonar is most often accomplished using continuous-wave (CW) pulses. An alternative that is more suited for use with slowly moving objects in reverberation-limited conditions is presented and evaluated. As described by A. Rihaczek [Principles of High Resolution Radar, McGraw Hill, 1969], the approach entails simultaneously transmitting up- and down-sweeping linear frequency modulated pulses (LFM-U&D). The key enabler of the LFM-U&D is the complementary range-bias information relative to using a single LFM sweep type (LFM-U|D). Novel in the proposed LFM-U&D approach is the use of multiple pings after tracking to form improved range and RR estimates. Cramer-Rao lower bounds (CRLBs) on estimator variance as a function of the number of pings illustrate that LFM-U&D is a compromise between the CW and the standard LFM-U|D. Estimates of the RR of clutter tracks from the Target and Reverberation Experiment (TREX13) were used to evaluate the approach. An order of magnitude reduc...

Improved Multitarget Tracking in Clutter Using Bearings-Only Measurements

Multitarget tracking in clutter using bearings-only measurements is a challenging problem. In this paper, a performance improved nonlinear filter is proposed on the basis of the Random Finite Set (RFS) theory and is named as Gaussian mixture measurements-based cardinality probability hypothesis density (GMMbCPHD) filter. The GMMbCPHD filter enables to address two main issues: measurement-origin-uncertainty and measurement nonlinearity, which constitutes the key problems in bearings-only multitarget tracking in clutter. For the measurement-origin-uncertainty issue, the proposed filter estimates the intensity of RFS of multiple targets as well as propagates the posterior cardinality distribution. For the measurement-origin-nonlinearity issue, the GMMbCPHD approximates the measurement likelihood function using a Gaussian mixture rather than a single Gaussian distribution as used in extended Kalman filter (EKF). The superiority of the proposed GMMbCPHD are validated by comparing with several state-of-the-art algorithms via intensive simulation studies.

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.