Experimental results in bearings-only tracking using the sequential Monte-Carlo probability hypothesis density filter

2019 ◽  
Author(s):  
Jorge G. Jimenez ◽  
Artur Wolek ◽  
Daniel Stilwell ◽  
James McMahon ◽  
Benjamin Dzikowicz
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Experimental Results ◽  
Probability Hypothesis Density ◽  
Probability Hypothesis Density Filter ◽  
Density Filter

A Novel Sequential Monte Carlo-Probability Hypothesis Density Filter for Particle Impoverishment Problem

2016 ◽  
Vol 13 (10) ◽  
pp. 6872-6877
Author(s):  
Xu Cong-An ◽  
Xu Congqi ◽  
Dong Yunlong ◽  
Xiong Wei ◽  
Chai Yong ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Probability Hypothesis Density ◽  
Practical Applications ◽  
Probability Hypothesis Density Filter ◽  
Highly Nonlinear ◽  
Density Filter ◽  
Simulation Results ◽  
Application Prospects ◽  
Particle Impoverishment

As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, diversity loss of particles introduced by the resampling step, which can be called particle impoverishment problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this paper, a novel SMC-PHD filter based on particle compensation is proposed to solve the problem. Firstly, based on an analysis of the particle impoverishment problem, a new particle compensatory method is developed to improve the particle diversity. Then, all the particles are integrated into the SMC-PHD filter framework. Compared with the SMC-PHD filter, simulation results demonstrate that the proposed particle compensatory SMC-PHD filter is capable of overcoming the particle impoverishment problem, which indicate good application prospects.

scholarly journals Dolphin Whistle Track Estimation Using Sequential Monte Carlo Probability Hypothesis Density Filter

2015 ◽  
Vol 62 (1) ◽  
pp. 17-20
Author(s):  
Imtiaz Ahmed
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Ground Truth ◽  
Tracking Performance ◽  
Estimation Accuracy ◽  
Common Dolphin ◽  
Stenella Coeruleoalba ◽  
Probability Hypothesis Density ◽  
Delphinus Delphis ◽  
Density Filter

This article focuses on possible automation of dolphin whistle track estimation process within the context of Multiple Target Tracking (MTT). It provides automatic whistle track estimation from raw hydrophone measurements using the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) filter. Hydrophone measurements for three different types of species namely bottlenose dolphin (Tursiops truncates), common dolphin (Delphinus delphis) and striped dolphin (Stenella coeruleoalba) have been used to benchmark the tracking performance of the SMC-PHD filter against three major challenges- the presence of multiple whistles, spontaneous death/birth of whistles and multiple whistles crossing each other. Quantitative analysis of the whistle track estimation accuracy is not possible since there is no ground truth type track for the dolphin whistles. Hence visual inspection of estimated tracks has been used corroborate the satisfactory tracking performance in the presence of all three challenges. DOI: http://dx.doi.org/10.3329/dujs.v62i1.21954 Dhaka Univ. J. Sci. 62(1): 17-20, 2014 (January)

scholarly journals State Estimation and  Smoothing for the  Probability Hypothesis  Density Filter

2021 ◽  
Author(s):  
◽  
Sergio I. Hernandez
Keyword(s):  
Monte Carlo ◽  
State Estimation ◽  
Sequential Monte Carlo ◽  
Signal To Noise Ratio ◽  
Recursive Algorithm ◽  
Difficult Problem ◽  
Automated System ◽  
Order Moment ◽  
Probability Hypothesis Density ◽  
Density Filter

<p>Tracking multiple objects is a challenging problem for an automated system, with applications in many domains. Typically the system must be able to represent the posterior distribution of the state of the targets, using a recursive algorithm that takes information from noisy measurements. However, in many important cases the number of targets is also unknown, and has also to be estimated from data. The Probability Hypothesis Density (PHD) filter is an effective approach for this problem. The method uses a first-order moment approximation to develop a recursive algorithm for the optimal Bayesian filter. The PHD recursion can implemented in closed form in some restricted cases, and more generally using Sequential Monte Carlo (SMC) methods. The assumptions made in the PHD filter are appealing for computational reasons in real-time tracking implementations. These are only justifiable when the signal to noise ratio (SNR) of a single target is high enough that remediates the loss of information from the approximation. Although the original derivation of the PHD filter is based on functional expansions of belief-mass functions, it can also be developed by exploiting elementary constructions of Poisson processes. This thesis presents novel strategies for improving the Sequential Monte Carlo implementation of PHD filter using the point process approach. Firstly, we propose a post-processing state estimation step for the PHD filter, using Markov Chain Monte Carlo methods for mixture models. Secondly, we develop recursive Bayesian smoothing algorithms using the approximations of the filter backwards in time. The purpose of both strategies is to overcome the problems arising from the PHD filter assumptions. As a motivating example, we analyze the performance of the methods for the difficult problem of person tracking in crowded environments</p>

scholarly journals State Estimation and  Smoothing for the  Probability Hypothesis  Density Filter

2021 ◽  
Author(s):  
◽  
Sergio I. Hernandez
Keyword(s):  
Monte Carlo ◽  
State Estimation ◽  
Sequential Monte Carlo ◽  
Signal To Noise Ratio ◽  
Recursive Algorithm ◽  
Difficult Problem ◽  
Automated System ◽  
Order Moment ◽  
Probability Hypothesis Density ◽  
Density Filter

<p>Tracking multiple objects is a challenging problem for an automated system, with applications in many domains. Typically the system must be able to represent the posterior distribution of the state of the targets, using a recursive algorithm that takes information from noisy measurements. However, in many important cases the number of targets is also unknown, and has also to be estimated from data. The Probability Hypothesis Density (PHD) filter is an effective approach for this problem. The method uses a first-order moment approximation to develop a recursive algorithm for the optimal Bayesian filter. The PHD recursion can implemented in closed form in some restricted cases, and more generally using Sequential Monte Carlo (SMC) methods. The assumptions made in the PHD filter are appealing for computational reasons in real-time tracking implementations. These are only justifiable when the signal to noise ratio (SNR) of a single target is high enough that remediates the loss of information from the approximation. Although the original derivation of the PHD filter is based on functional expansions of belief-mass functions, it can also be developed by exploiting elementary constructions of Poisson processes. This thesis presents novel strategies for improving the Sequential Monte Carlo implementation of PHD filter using the point process approach. Firstly, we propose a post-processing state estimation step for the PHD filter, using Markov Chain Monte Carlo methods for mixture models. Secondly, we develop recursive Bayesian smoothing algorithms using the approximations of the filter backwards in time. The purpose of both strategies is to overcome the problems arising from the PHD filter assumptions. As a motivating example, we analyze the performance of the methods for the difficult problem of person tracking in crowded environments</p>

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