MIMO radar target tracking using the probability hypothesis density filter

Glass, John D.; Lanterman, Aaron D.

doi:10.1109/aero.2012.6187208

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…Nevertheless, the Gaussian mixture (GM) combined with the probability hypothesis density (PHD) as an algorithm GM-PHD [8] provides a favorable framework for treating the measurements of several sensors.…”

Section: • Relatedworkmentioning

confidence: 99%

Improved multi-target tracking crossing paths in MIMO FMCW 8 × 16 radar system using a new hybrid AMC-JPDAF algorithm

Zarai

Abdallah

Cherif

2023

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This paper addresses the multi-target tracking problem in a multiple-input multiple-output radar system. Different tracking sensor measurements often encounter complex data as well correlation problems. Our goal is to solve this dilemma, which appears to be a new technical challenge; fix object-tracking scenarios while avoiding divergence. Once the cross-path phenomenon occurs, the target measurement assignment process in MIMO radar systems becomes more complicated. Therefore, this interference phenomenon can disturb the received signal and miss the state estimation process. To avoid all the mentioned problems, we have improved a new hybrid algorithm based on the particle filter; Adaptive Monte Carlo has been used in conjunction with the Joint Probabilistic Data Association Filter to substitute the classical extended KALMAN filter combined with JPDAF known as EKF-JPDAF. Experimental results of our MIMO-FMCW radar system with AMC-JPDAF converge to a more accurate state estimate while avoiding divergence. Using MATLAB software development framework, the designed system meets the goals originally set by AMC-JPDAF by referring to the experimental database obtained from the MIMO-FMCW 8x16 radar system.

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Section: • Relatedworkmentioning

confidence: 99%

Improved multi-target tracking crossing paths in MIMO FMCW 8 × 16 radar system using a new hybrid AMC-JPDAF algorithm

Zarai

Abdallah

Cherif

2023

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“…In order to deal with the MTT data association issues, we found in literature several methods are classified into Bayesian and other non-Bayesian filters, has been applied to address different scenarios, such as, Markov Chain Monte Carlo Data Association (MCMCDA) was proposed in [7] as a solution to replace the conventional method as known by The Multiple Hypothesis Tracking (MHT), to handle the low Signal-to-Noise Ratio (SNR) in the pre-processing phase. On the other hand, the Gaussian mixture (GM) combined with Probability Hypothesis Density (PHD), then the full GM-PHD algorithm [8] provides a promising framework to process the several measurements from multi sensors.…”

Section: Related Workmentioning

confidence: 99%

Improved Multi Target Tracking in MIMO Radar System Using New Hybrid Monte Carlo–PDAF Algorithm

Zarai¹,

Cherif²

2022

IoT Applications Computing

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This article deals with the multi-target tracking problem (MTT) in MIMO radar systems. As a result, this problem is now seen as a new technological challenge. Thus, in different tracking scenarios, measurements from sensors are usually subject to a complex data association issue. The MTT data association problem of assigning measurements-to-target or target-state-estimates becomes more complex in MIMO radar system, once the crossing target tracking scenario arises, hence the interference phenomenon may interrupt the received signal and miss the state estimation process. To avoid most of these problems, we have improved a new hybrid algorithm based on particle filter called “Monte Carlo” associated to Joint Probabilistic data Association filter (JPDAF), the whole approach named MC-JPDAF algorithm has been proposed to replace the traditional method as is known by the Extended KALMAN filter (EKF) combined with JPDAF method, such as EKF-JPDAF algorithm. The obtained experimental results showed a challenging remediation. Where, the MC-JPDAF converges towards the accurate state estimation. Thus, more efficient than EKF-JPDAF. The simulation results prove that the designed system meets the objectives set for MC-JPDA by referring to an experimental database using the MATLAB Software Development Framework.

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“…In recent years, the GM-PHD filter has been widely applied to various fields such as tracking visual targets [13][14][15], Doppler-only targets [16,17], radar targets [18,19] and extended targets [20,21]. Tracking of multi-sensor multi-target based on the GM-PHD filter is also studied in [22] and tracking of manoeuvring targets using this filter is considered in [23,24].…”

Section: Introductionmentioning

confidence: 99%

Novel N ‐scan GM‐PHD‐based approach for multi‐target tracking

Yazdian-Dehkordi

Azimifar

2016

IET signal process.

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The GM-PHD-based filter has been proposed as an alternative of the PHD filter to estimate the first-order moment of the multi-target posterior density. The GM-PHD filter utilises a weighted summation of Gaussian components to estimate the target states. This filter and its recent variants perform state extraction of the targets based on the target weights. However, due to different uncertainties such as noisy observation, miss-detection, clutter or occlusion, the weight of a target is decreased and the estimation of the target is lost in some steps. In this study, the authors develop a simple and effective N-scan approach which employs the weight history of targets to improve the performance of the GM-PHD-based methods. They propose to assign a label, a weight history and a binary confidence indicator to each Gaussian component and propagate them in time. Then, they explain a novel N-scan state extraction algorithm to estimate the target states based on their histories in the N last steps. To study the efficiency of the proposed N-scan approach, it is applied on the GM-PHD filter as well as its several recent variants. The experimental results provided for various uncertainties show the effectiveness of the method.

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MIMO radar target tracking using the probability hypothesis density filter

Cited by 4 publications

References 18 publications

Improved multi-target tracking crossing paths in MIMO FMCW 8 × 16 radar system using a new hybrid AMC-JPDAF algorithm

Improved multi-target tracking crossing paths in MIMO FMCW 8 × 16 radar system using a new hybrid AMC-JPDAF algorithm

Improved Multi Target Tracking in MIMO Radar System Using New Hybrid Monte Carlo–PDAF Algorithm

Novel N ‐scan GM‐PHD‐based approach for multi‐target tracking

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