scholarly journals Robust CPHD Fusion for Distributed Multitarget Tracking Using Asynchronous Sensors

2021 ◽  
Author(s):  
Benru Yu ◽  
Tiancheng Li ◽  
Hong Gu
Keyword(s):  
Time Sequence ◽  
Sampling Time ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Arithmetic Average ◽  
Bootstrap Approach ◽  
Asynchronous Network ◽  
Time Requirements ◽  
Intensity Functions ◽  
Fusion Approach

This paper concentrates on tracking multiple targets using an asynchronous network of sensors with different sampling rates. First, a timely fusion approach is proposed for handling measurements from asynchronous sensors. In the proposed approach, the arithmetic average fusion of the estimates provided by local cardinalized probability hypothesis density filters is recursively carried out according to the network-wide sampling time sequence. The corresponding intersensor communication is conducted by a partial flooding protocol, in which either cardinality distributions or intensity functions pertinent to local posteriors are disseminated among sensors. Moreover, both feedback and non-feedback fusion-filtering modes are provided to meet the performance and real-time requirements, respectively. Second, an extension of the timely fusion approach referred to as robust bootstrap approach is presented, which can deal with unknown clutter and detection parameters by exploiting a local bootstrap filtering scheme. Finally, numerical simulations are performed to test the proposed approaches. <br>

scholarly journals Robust CPHD Fusion for Distributed Multitarget Tracking Using Asynchronous Sensors

2021 ◽  
Author(s):  
Benru Yu ◽  
Tiancheng Li ◽  
Hong Gu
Keyword(s):  
Time Sequence ◽  
Sampling Time ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Arithmetic Average ◽  
Bootstrap Approach ◽  
Asynchronous Network ◽  
Time Requirements ◽  
Intensity Functions ◽  
Fusion Approach

This paper concentrates on tracking multiple targets using an asynchronous network of sensors with different sampling rates. First, a timely fusion approach is proposed for handling measurements from asynchronous sensors. In the proposed approach, the arithmetic average fusion of the estimates provided by local cardinalized probability hypothesis density filters is recursively carried out according to the network-wide sampling time sequence. The corresponding intersensor communication is conducted by a partial flooding protocol, in which either cardinality distributions or intensity functions pertinent to local posteriors are disseminated among sensors. Moreover, both feedback and non-feedback fusion-filtering modes are provided to meet the performance and real-time requirements, respectively. Second, an extension of the timely fusion approach referred to as robust bootstrap approach is presented, which can deal with unknown clutter and detection parameters by exploiting a local bootstrap filtering scheme. Finally, numerical simulations are performed to test the proposed approaches. <br>

scholarly journals Robust CPHD Fusion for Distributed Multitarget Tracking Using Asynchronous Sensors

2021 ◽  
Author(s):  
Benru Yu ◽  
Tiancheng Li ◽  
Hong Gu
Keyword(s):  
Time Sequence ◽  
Sampling Time ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Arithmetic Average ◽  
Bootstrap Approach ◽  
Asynchronous Network ◽  
Time Requirements ◽  
Intensity Functions ◽  
Fusion Approach

This paper concentrates on tracking multiple targets using an asynchronous network of sensors with different sampling rates. First, a timely fusion approach is proposed for handling measurements from asynchronous sensors. In the proposed approach, the arithmetic average fusion of the estimates provided by local cardinalized probability hypothesis density filters is recursively carried out according to the network-wide sampling time sequence. The corresponding intersensor communication is conducted by a partial flooding protocol, in which either cardinality distributions or intensity functions pertinent to local posteriors are disseminated among sensors. Moreover, both feedback and non-feedback fusion-filtering modes are provided to meet the performance and real-time requirements, respectively. Second, an extension of the timely fusion approach referred to as robust bootstrap approach is presented, which can deal with unknown clutter and detection parameters by exploiting a local bootstrap filtering scheme. Finally, numerical simulations are performed to test the proposed approaches. <br>

185. Short-Term Sampling Time Requirements in Rooms

2003 ◽  
Author(s):  
E. Lee ◽  
C. Feigley ◽  
J. Hussey ◽  
J. Khan ◽  
M. Ahmed
Keyword(s):  
Sampling Time ◽  
Short Term ◽  
Time Requirements

scholarly journals On Arithmetic Average Fusion and Its Application for Distributed Multi-Bernoulli Multitarget Tracking

2020 ◽  
Author(s):  
Tiancheng Li ◽  
Xiaoxu Wang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Literature Review ◽  
Information Fusion ◽  
Large Scale ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Computationally Efficient ◽  
Simple Arithmetic ◽  
Arithmetic Average ◽  
The Core ◽  
Comprehensive Literature Review

<div>Recently, the simple arithmetic averages (AA) fusion has demonstrated promising, even surprising, performance for multitarget information fusion. In this paper, we first analyze the conservativeness and Frechet mean properties of it, presenting new empirical analysis based on a comprehensive literature review. Then, we propose a target-wise fusion principle for tailoring the AA fusion to accommodate the multi-Bernoulli (MB) process, in which only significant Bernoulli components, each represented by an individual Gaussian mixture, are disseminated and fused in a Bernoulli-to-Bernoulli (B2B) manner. For internode communication, both the consensus and flooding schemes are investigated, respectively. At the core of the proposed fusion algorithms, Bernoulli components obtained at different sensors are associated via either clustering or pairwise assignment so that the MB fusion problem is decomposed to parallel B2B fusion subproblems, each resolved via exact Bernoulli-AA fusion. Two communicatively and computationally efficient cardinality consensus approaches are also presented which merely disseminate and fuse target existence probabilities among local MB filters. The accuracy and computing and communication cost of these four approaches are tested in two large scale scenarios with different sensor networks and target trajectories. </div>

Multitarget Tracking using Probability Hypothesis Density Smoothing

2011 ◽  
Vol 47 (4) ◽  
pp. 2344-2360 ◽  
Author(s):  
N. Nadarajah ◽  
T. Kirubarajan ◽  
T. Lang ◽  
M. Mcdonald ◽  
K. Punithakumar
Keyword(s):  
Multitarget Tracking ◽  
Probability Hypothesis Density

scholarly journals Multisensor RFS Filters for Unknown and Changing Detection Probability

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 741
Author(s):  
Zhang ◽  
Li ◽  
Sun
Keyword(s):  
Detection Probability ◽  
A Priori ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Inverse Gamma ◽  
Motion State ◽  
Performance Deterioration ◽  
Cphd Filter ◽  
Monitoring Area

The detection probability is an important parameter in multisensor multitarget tracking. The existing multisensor multi-Bernoulli (MS-MeMBer) filter and multisensor cardinalized probability hypothesis density (MS-CPHD) filter require that detection probability is a priori. However, in reality, the value of the detection probability is constantly changing due to the influence of sensors, targets, and other environmental characteristics. Therefore, to alleviate the performance deterioration caused by the mismatch of the detection probability, this paper applies the inverse gamma Gaussian mixture (IGGM) distribution to both the MS-MeMBer filter and the MS-CPHD filter. Specifically, the feature used for detection is assumed to obey the inverse gamma distribution and is statistically independent of the target’s spatial position. The feature is then integrated into the target state to iteratively estimate the target detection probability as well as the motion state. The experimental results demonstrate that the proposed methods can achieve a better filtering performance in scenarios with unknown and changing detection probability. It is also shown that the distribution of the sensors has a vital influence on the filtering accuracy, and the filters perform better when sensors are dispersed in the monitoring area.

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