Sequential Monte Carlo Cardinalized probability hypothesized density filter based on Track-Before-Detect for fluctuating targets in heavy-tailed clutter

Cao, Chenghu; Zhao, Yue; Pang, Xiaojiao; Xu, Baoqing; Suo, Zhiling

doi:10.1016/j.sigpro.2019.107367

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…The multi-target Bayes filter is typically complicated due to the considerable dimensionality of the multi-target state and observation space. 12 Traditional MTT algorithms, such as joint probabilistic data association (JPDA) filtering 13 and MHT, 14 assign measurements to targets via data association processing, and then perform multiple independent single-target Bayesian filtering in parallel. The problem of coupled explosion frequently happens when there is much clutter.…”

Section: Introductionmentioning

confidence: 99%

“…Most target motion and measurement models are nonlinear, such as missile re-entry velocity and target detection by passive radar and infrared sensors. In Cao et al, 12 a Gaussian mixture particle probability hypothesis density (GMP-PHD) algorithm is given to address the nonlinear problem. GMP-PHD utilizes a combination of particle filters, but it also introduces additional issues, such as the conflict between filter estimation accuracy and real-time tracking.…”

Section: Introductionmentioning

confidence: 99%

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Equal probability embedded cubature particle PHD filter algorithm in multi-target tracking

Xie

Huang

Wei

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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In target tracking, multi-target tracking is the focus of research. It is primarily concerned with the issue of collaboratively estimating the number, state, or trajectory of targets based on sensor measurements in the presence of data association uncertainty, detection uncertainty, false observation, and noise. The current research hotspot in multi-target tracking is nonlinear multi-target tracking based on the probability hypothesis density algorithm. Furthermore, there are several considerations with this algorithm’s multi-target tracking, such as low estimate accuracy, filter divergence, and poor real-time performance. Based on equivalent probability sampling and the embedded volume criterion, this article demonstrates an equal probability embedded cubature particle probability hypothesis density filter algorithm. In the sampling stage, the algorithm implements the equal probability sampling method, divides the entire sampling area into several areas with equal probability, extracts particles from each region using the established criteria, generates limited integral points using the third-order embedded volume criterion, filters each sampling particle, fits the important density function, and predicts and updates the probability hypothesis density of multi-target state. The simulation results demonstrate that the equal probability sampling strategy outperforms other multi-target position and number estimation methods. Simultaneously, it demonstrates that the equal probability embedded cubature particle probability hypothesis density filter algorithm can effectively track multiple targets. The equal probability embedded cubature particle probability hypothesis density filter algorithm performs better in real-time and has a more accurate target number and state estimate than other algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Equal probability embedded cubature particle PHD filter algorithm in multi-target tracking

Xie

Huang

Wei

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…As such, rather than declaring the presence of targets relying on the measurements collected in a single scan, measurements received in multiple scans are jointly processed, keeping record of a number of candidate trajectories, and confirming only a subset of them (hence detecting the associated targets). Several TBD approaches are available in the literature (e.g., [12]- [41]), each tailored to specific tracking problems. For example, in the TBD framework, the issue of extended target has been addressed through point voting in Hough transform [20], [30] or particle filter based probability density estimation of extended target state [24].…”

Section: Introductionmentioning

confidence: 99%

“…Dynamic programming based TBD [25]- [27], [38]: DP-TBD is a grid-based method that estimates target trajectories by searching all physically admissible paths in a discrete state space. Some grid-based TBD techniques [39]- [41] perform target detection via sliding time window and multi-frame tests.…”

Section: Introductionmentioning

confidence: 99%

A Track-Before-Detect Algorithm for UWB Radar Sensor Networks

Yan

Giorgetti

2021

Preprint

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Precise localization and tracking of moving non-collaborative persons and objects using a network of ultra-wideband (UWB) radar nodes has been shown to represent a practical and effective approach. In UWB radar sensor networks (RSNs), existence of strong clutter, weak target echoes, and closely spaced targets are obstacles to achieving a satisfactory tracking performance. Using a track-before-detect (TBD) approach, the waveform obtained by each node during a time period are jointly processed. Both spatial information and temporal relationship between measurements are exploited in generating all possible candidate trajectories and only the best trajectories are selected as the outcome. The effectiveness of the developed TBD technique for UWB RSNs is confirmed by numerical simulations and by two experimental results, both carried out with actual UWB signals. In the first experiment, a human target is tracked by a monostatic radar network with an average localization error of 41.9 cm with no false alarm trajectory in a cluttered outdoor environment. In the second experiment, two targets are detected by multistatic radar network with localization errors of 25.4 cm and 19.7 cm, and detection rate of the two targets is 88.75%, and no false alarm trajectory.

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“…In the PHD and CPHD filter, the clutter process is modeled as Poisson and IIDC RFS, respectively. But these two models are unable to describe some complicated clutter process [21,22]. Particularly when the target is submerged in the clutter background, the distribution of clutter process is more complicated [23].…”

mentioning

confidence: 99%

A general cardinalized probability hypothesis density filter

Shen

Song

Fan

et al. 2022

EURASIP J. Adv. Signal Process.

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Based on random finite set, the probability hypothesis density (PHD) filter and the cardinalized PHD (CPHD) filter have been proposed for multitarget tracking as they are computational tractable. But the classical PHD and CPHD filter is not applicable when a target generates multiple detections. The general PHD filter was proposed to apply the PHD filter to arbitrary clutter and target measurement process. However, as a filter with better performance than the PHD filter, the CPHD filter for multiple-detection tracking is not proposed except the extended target CPHD filter and the multisensor CPHD filter. In our work, the general CPHD filter, which allows the clutter process and target measurement process to be arbitrary and performs better than the general PHD filter, is proposed. The proposed general CPHD filter is the general form of all kinds of CPHD filters, as well as all kinds of PHD filters and Bernoulli filters. In the simulation, the multiple-detection model of the over-the-horizon radar is used to demonstrate the tracking performance. The simulation results show that the proposed filter improves the accuracy of the state estimates and reduces the variance of the estimated number of targets.

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Sequential Monte Carlo Cardinalized probability hypothesized density filter based on Track-Before-Detect for fluctuating targets in heavy-tailed clutter

Cited by 10 publications

References 23 publications

Equal probability embedded cubature particle PHD filter algorithm in multi-target tracking

Equal probability embedded cubature particle PHD filter algorithm in multi-target tracking

A Track-Before-Detect Algorithm for UWB Radar Sensor Networks

A general cardinalized probability hypothesis density filter

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