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

Chai, Lei; Kong, Lingjiang; Li, Suqi; Yi, Wei

doi:10.1016/j.sigpro.2020.107501

Cited by 22 publications

(23 citation statements)

References 36 publications

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“…We exploit partially overlapping time windows, each containing points of W scans; the points of the most recent window are given by (13). Tracklets are detected by the points in each time window independently.…”

Section: E Tracklets Generationmentioning

confidence: 99%

“…Therefore, a target may generate multiple measurements as it can in principle be detected in several resolution cells, leading to an extended target problem [5], [7]. Although a number of tracking methods have been developed to track extended targets, including methods based on the probability hypothesis density (PHD) filter [8]- [12] and methods based on the Bernoulli filter [13], it is known that, without a sufficient amount of a priori information about the target, the presence of multiple measurements deteriorates tracking performance. We also point out that targets in UWB RSNs are often human beings, whose motion may be characterized by some degree of maneuverability, with course and speed changing within one or two scans.…”

Section: Introductionmentioning

confidence: 99%

“…Particle filter (PF) based TBD [22]- [24], [34], [35]: Particle filters can sequentially approximate the a posteriori probability density function with any dynamic and measurement models, making PF-TBD superior to other methods in the presence of nonlinear or non-Gaussian noise. Some PHD filters [11], [12] and Bernoulli filters [13] within TBD strategies are also implemented as particle filters.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: E Tracklets Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yan

Giorgetti

2021

Preprint

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“…On the other hand, multimodel (MM) algorithms such as interactive MM (IMM) [7], fixed structure MM [8] and variable structure MM [9] have been proposed for manoeuvring target tracking, in which all tracking results of different models are combined with appropriate weights improve tracking results. Several researchers have constructed more advanced MM algorithms, such as interactive MM algorithms fusing input estimation and best linear unbiased estimation filter (MIE-BLUE-IMM) [10] and multi-Bernoulli filter based track-before-detect [11], in which more information is used to provide better manoeuvring model approximations and further improve tracking performance. However, MM algorithms still need to accumulate enough observation data to form a correct estimation of the dynamic model, which creates the problem of model estimation delay [12].…”

Section: Introductionmentioning

confidence: 99%

DeepGTT: A general trajectory tracking deep learning algorithm based on dynamic law learning

et al. 2021

IET Radar Sonar & Navi

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Deep learning technology provides novel solutions for increasingly complex target tracking requirements. For traditional target tracking models, the movement of the target need to be simulated by a predefined mathematical model. However, it is extremely difficult to obtain sufficient information in advance, which makes it challenging to track changeable and noisy trajectories in a timely and precise manner. A deep learning framework is constructed for automatic trajectory tracking based on learning the dynamic laws of motion, called DeepGTT. Specifically, a trajectory generator and a trajectory mapper were designed to standardise trajectory data and construct trajectory mapping, which enable the long short-term memory-based tracking network to learn general dynamic laws. Then, to discuss the interpretability of the model, the mechanism of the deep learning framework is considered and a memory factor matrix is defined. Finally, extensive experiments are conducted on various weak manoeuvring and manoeuvring scenarios to evaluate the algorithm. Experimental results demonstrate that the DeepGTT algorithm remarkably improves accuracy and efficiency compared with most conventional algorithms and state-of-the-art methods. In addition, interpretability experiments qualitatively prove that the tracking network can perceive dynamic laws when estimating the target state.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…The larger the number of particles, the greater the complexity of the algorithm and the higher the demand for equipment. The measurement-driven approach is an effective way to work out this problem, and is widely used in different filters [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Robust Measurement-Driven Cardinality Balance Multi-Target Multi-Bernoulli Filter

Yang

Zhu

et al. 2021

Sensors

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The multi-target tracking filter under the Bayesian framework has strict requirements on the prior information of the target, such as detection probability density, clutter density, and target initial position information. This paper proposes a novel robust measurement-driven cardinality balance multi-target multi-Bernoulli filter (RMD-CBMeMBer) for solving the multiple targets tracking problem when the detection probability density is unknown, the background clutter density is unknown, and the target’s prior position information is lacking. In RMD-CBMeMBer filtering, the target state is first extended, so that the extended target state includes detection probability, kernel state, and indicators of target and clutter. Secondly, the detection probability is modeled as a Beta distribution, and the clutter is modeled as a clutter generator that is independent of each other and obeys the Poisson distribution. Then, the detection probability, kernel state, and clutter density are jointly estimated through filtering. In addition, the correlation function (CF) is proposed for creating new Bernoulli component (BC) by using the measurement information at the previous moment. Numerical experiments have verified that the RMD-CBMeMBer filter can solve the multi-target tracking problem under the condition of unknown target detection probability, unknown background clutter density and inadequate prior position information of the target. It can effectively estimate the target detection probability and the clutter density.

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

Cited by 22 publications

References 36 publications

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

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

DeepGTT: A general trajectory tracking deep learning algorithm based on dynamic law learning

Robust Measurement-Driven Cardinality Balance Multi-Target Multi-Bernoulli Filter

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