Journal ArticleDOI
Sonar tracking of multiple targets using joint probabilistic data association
Reads0
Chats0
TLDR
A new theoretical result is presented: the joint probabilistic data association (JPDA) algorithm, in which joint posterior association probabilities are computed for multiple targets (or multiple discrete interfering sources) in Poisson clutter.Abstract:
The problem of associating data with targets in a cluttered multi-target environment is discussed and applied to passive sonar tracking. The probabilistic data association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, a new theoretical result is presented: the joint probabilistic data association (JPDA) algorithm, in which joint posterior association probabilities are computed for multiple targets (or multiple discrete interfering sources) in Poisson clutter. The algorithm is applied to a passive sonar tracking problem with multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, two or more acoustic states, and realistic (i.e., low) probabilities of detection at each sample time. A simulation result is presented for two heavily interfering targets illustrating the dramatic tracking improvements obtained by estimating the targets' states using joint association probabilities.read more
Citations
More filters
Proceedings ArticleDOI
Simple online and realtime tracking with a deep association metric
TL;DR: This paper integrates appearance information to improve the performance of SORT and reduces the number of identity switches, achieving overall competitive performance at high frame rates.
Journal ArticleDOI
The Gaussian Mixture Probability Hypothesis Density Filter
Ba-Ngu Vo,Wing-Kin Ma +1 more
TL;DR: Under linear, Gaussian assumptions on the target dynamics and birth process, the posterior intensity at any time step is a Gaussian mixture and closed-form recursions for propagating the means, covariances, and weights of the constituent Gaussian components of the posteriorintensity are derived.
Posted Content
Simple Online and Realtime Tracking with a Deep Association Metric
TL;DR: In this paper, the authors integrate appearance information to improve the performance of Simple Online and Real-time Tracking (SORT) by tracking objects through longer periods of occlusions, effectively reducing the number of identity switches.
Proceedings ArticleDOI
Globally-optimal greedy algorithms for tracking a variable number of objects
TL;DR: A near-optimal algorithm based on dynamic programming which runs in time linear in the number of objects andlinear in the sequence length is given which results in state-of-the-art performance.
Journal ArticleDOI
MCMC-based particle filtering for tracking a variable number of interacting targets
TL;DR: A particle filter that effectively deals with interacting targets, targets that are influenced by the proximity and/or behavior of other targets, is described and a novel Markov chain Monte Carlo (MCMC) sampling step is replaced to obtain a more efficient MCMC-based multitarget filter.
References
More filters
Book
Stochastic Processes and Filtering Theory
TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.
Journal ArticleDOI
An algorithm for tracking multiple targets
TL;DR: An algorithm for tracking multiple targets in a cluttered environment is developed, capable of initiating tracks, accounting for false or missing reports, and processing sets of dependent reports.
Journal ArticleDOI
Tracking in a cluttered environment with probabilistic data association
Yaakov Bar-Shalom,Edison Tse +1 more
TL;DR: Simulation results obtained for tracking an object in a cluttered environment show the PDAF to give significantly better results than the standard filter currently in use for this type of problem.
Journal ArticleDOI
Tracking methods in a multitarget environment
TL;DR: This compact and unified presentation of the state-of-art in multitarget tracking was motivated by the recent surge of interest in this problem and is hoped to be useful in view of the need to adapt and modify existing techniques before using them for specific problems.
Proceedings ArticleDOI
Multi-target tracking using joint probabilistic data association
TL;DR: New theoretical results are presented on the JPDA algorithm, in which joint posterior probabilities are computed for multiple targets in Poisson clutter, applied to a passive sonar tracking problem wlth multiple sensors and targets.