Sigma-point multiple particle filtering (original) (raw)

Particle Filters for Multiple Target Tracking

Procedia Technology, 2016

Multiple target tracking has immense application in areas such as surveillance, air traffic control, defense and computer vision. The aim of a target tracking algorithm is to estimate the target position precisely from the partial noisy observations available. The real challenges of multiple target tracking are to accomplish the same in the presence of measurement origin uncertainty and clutter. Optimal solutions are available by way of Kalman filters for the special case of linear dynamical systems with Gaussian noise. For a more general scenario, we resort to the suboptimal solutions like Particle filters which implement stochastic filtering through a sequential Monte Carlo approach. Measurement origin uncertainty is resolved by using a suitable data association technique prior to the filtering. This paper explores the possibilities of applying a variant of Ensemble Square Root Filters (EnSRF) in a multiple target tracking scenario and its tracking performance is compared with those of conventional Bootstrap and Auxiliary Bootstrap particle filters. The filtering scheme proposed here incorporates Sample based Joint Probabilistic Data Association (SJPDA) in the EnSRF framework for dealing with measurement origin uncertainty.

The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems

Parametric filters, such as the Extended Kalman Filter and the Unscented Kalman Filter, typically scale well with the dimensionality of the problem, but they are known to fail if the posterior state distribution cannot be closely approximated by a density of the assumed parametric form. For nonparametric filters, such as the Particle Filter, the converse holds. Such methods are able to approximate any posterior, but the computational requirements scale exponentially with the number of dimensions of the state space. In this paper, we present the Coordinate Particle Filter which alleviates this problem. We propose to compute the particle weights recursively, dimension by dimension. This allows us to explore one dimension at a time, and resample after each dimension if necessary. Experimental results on simulated as well as real data confirm that the proposed method has a substantial performance advantage over the Particle Filter in high-dimensional systems where not all dimensions are...

Particle filter for joint estimation of multi-object dynamic state and multi-sensor bias

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012

The paper formulates the problem of sequential Bayesian estimation of a compound state consisting of a multi-object dynamic state and a multi-sensor bias. The compound state is modelled by a doubly stochastic point process, where the multi-object bias is a parent, whereas the multi-object state is the offspring point process. The prediction and the update steps for the first-order moment of the posterior density of the doubly-stochastic point process can be expressed analytically. The implementation, however, in general has to be done numerically. The paper presents a particle filter implementation illustrated in the context of multi-target tracking using rangeazimuth measuring sensors with unknown biases.

Improving Accuracy by Iterated Multiple Particle Filtering

IEEE Signal Processing Letters, 2000

This paper analyzes and validates an enhanced implementation of the multiple particle filter that improves its accuracy when applied to high dimensional problems. The algorithm combines the divide et impera philosophy of the multiple particle filter, which avoids the collapse of traditional particle filters, with game theory strategies that provide with a powerful tool to improve the performance. The problem of multiple target tracking with received signal strength measurements is addressed and the results show remarkable improvement over both standard particle filtering and multiple particle filtering.

A Closer Look to Probabilistic State Estimation – Case: Particle Filtering (2014)

OPTOELECTRONICS AND ADVANCED MATERIALS-RAPID COMMUNICATIONS

Particle Filter is a significant member of the group of methods aiming to provide reasonable solutions to the real-world problems by approximating the value of the posterior density function using probabilistic sampling. Particle filtering has been increasingly used by researchers for the last two decades with the advancements occurred in computational resources in order to solve such problems. This paper focuses on Particle Filtering in a way to be a complete tutorial for the beginner researchers by means of providing a quick theoretical framework of Particle Filtering in a step-by-step progressive manner starting with Bayesian Inference as well as providing a stimulating multi-target tracking example problem with solution.

Bandwidth-Efficient Target Tracking In Distributed Sensor Networks Using Particle Filters

2006 9th International Conference on Information Fusion, 2006

This paper considers the problem of tracking a moving target in a multisensor environment using distributed particle filters (DPFs). Particle filters have a great potential for solving highly nonlinear and non-Gaussian estimation problems, in which the traditional Kalman Filter(KF) and Extended Kalman Filter(EKF) generally fail. However, in a sensor network, the implementation of distributed particle filters requires huge communications between local sensor nodes and the fusion center. To make the DPF approach feasible for real time processing and to reduce communication requirements, we approximate a posteriori distribution obtained from the local particle filters by a Gaussian Mixture Model(GMM). We propose a modified EM algorithm to estimate the parameters of GMMs obtained locally. These parameters are transmitted to the fusion center where the Best Linear Unbiased Estimator(BLUE) is used for fusion. Simulation results are presented to illustrate the performance of the proposed algorithm.

Joint particle filtering of multiple maneuvering targets from unassociated measurements

Journal of Advances in Information Fusion, 2006

The problem of maintaining tracks of multiple maneuvering targets from unassociated measurements is formulated as a problem of estimating the hybrid state of a Markov jump linear system from measurements made by a descriptor system with independent, identically distributed (i.i.d.) stochastic coefficients. This characterization is exploited to derive the exact equation for the Bayesian recursive filter, to develop two novel Sampling Importance Resampling (SIR) type particle filters, and to derive approximate Bayesian filters which use for each target one Gaussian per maneuver mode. The two approximate Bayesian filters are a compact and a trackcoalescence avoiding version of Interacting Multiple Model Joint Probabilistic Data Association (IMMJPDA). The relation of each of the four novel filter algorithms to the literature is well explained. Through Monte Carlo simulations for a two target example, these four filters are compared to each other and to the approach of using one IMMPDA filter per target track. The Monte Carlo simulation results show that each of the four novel filters clearly outperforms the IMMPDA approach. The results also show under which conditions the IMMJPDA type filters perform close to exact Bayesian filtering, and under which conditions not.

Box-particle PHD filter for multi-target tracking

2012

This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable to deal with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-PHD filter reduces the number of particles significantly, which improves the runtime considerably. The small particle number makes this approach attractive for distributed computing. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes methods from the field of interval analysis. The theoretical derivation of the box-PHD filter is presented followed by a comparative analysis with a standard sequential Monte Carlo (SMC) version of the PHD filter. To measure the performance objectively three measures are used: inclusion, volume and the optimum subpattern assignment metric. Our studies suggest that the box-PHD filter reaches similar accuracy results, like a SMC-PHD filter but with much considerably less computational costs. Furthermore, we can show that in the presence of strongly biased measurement the box-PHD filter even outperforms the classical SMC-PHD filter.

A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

Sensors (Basel, Switzerland), 2017

We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tra...