Joint Multitarget Probability Density Research Articles

Particle filtering based track-before-detect method for passive array sonar systems

This work considers the underwater tracking of an unknown and time-varying number of targets, i.e., acoustic emitters, using passive array sonar systems. This problem becomes more challenging if the signal-to-noise ratio (SNR) of the acoustic emitter is low. To address this problem, a complete particle filter track-before-detect (PF-TBD) signal processing procedure is especially developed for the passive array sonar systems. Specifically, in order to enhance the detection performance of the low SNR targets, the unthresholded spectrum measurements after the beamforming of the acoustic signals are directly used as the inputs of the PF-TBD method. To better model the statistical characteristics of the spectrum measurements, a data fitting based parameter estimation algorithm is proposed to build accurate likelihood functions. Then the joint multi-target probability density (JMPD) can be recursively propagated forward by particle filtering to estimate the multi-target states. To accommodate the time-varying number of targets, the trajectory initiation and termination strategies are also integrated into the filtering process by adaptively adjusting the state dimensions of the JMPD at each measurement time. Finally, the efficacy of the proposed PF-TBD method is demonstrated both in simulation and on collected real-world data.

Multi-target track-before-detect algorithm based on joint multi-target probability density model

Concerning the problem of Track-Before-Detect(TBD) in a multi-target environment,in this paper,a TBD algorithm based on Joint Multi-target Probability Density(JMPD) model was proposed.The JMPD was a single probabilistic entity that captured uncertainty about the number of targets present in the surveillance region as well as their individual states and a Particle Filter(PF) was used to recursively estimate the JMPD.The simulation results demonstrate that the birth and death of target can be estimated accurately as well as its trajectory by the proposed algorithm with smaller detection delays.

Distributed target tracking using signal strength measurements by a wireless sensor network

Wireless Sensor Networks are well suited for tracking targets carrying RFID tags in indoor environments. Tracking based on the received signal strength indication (RSSI) is by far the cheapest and simplest option, but suffers from secular biases due to effects of multi-path, occlusions and decalibration, as well as large unbiased errors due to measurement noise. We propose a novel algorithm that solves these problems in a distributed, scalable and power-efficient manner. Firstly, our proposal includes a tandem incremental estimator that learns and tracks the radio environment of the network, and provides this knowledge for the use of the tracking algorithm, which eliminates the secular biases due to radio occlusions etc. Secondly, we reduce the unbiased tracking error by exploiting the co-dependencies in the motion of several targets (as in crowds or herds) via a fully distributed and tractable particle filter. We thereby extract a significant 'diversity gain' while still allowing the network to scale seamlessly to a large tracking area. In particular, we avoid the pitfalls of network congestion and severely shortened battery lifetimes that plague procedures based on the joint multi-target probability density.

A Bayesian Approach to Multiple Target Detection and Tracking

This paper considers the problem of simultaneously detecting and tracking multiple targets. The problem can be formulated in a Bayesian framework and solved, in principle, by computation of the joint multitarget probability density (JMPD). In practice, exact computation of the JMPD is impossible, and the predominant challenge is to arrive at a computationally tractable approximation. A particle filtering scheme is developed for this purpose in which each particle is a hypothesis on the number of targets present and the states of those targets. The importance density for the particle filter is designed in such a way that the measurements can guide sampling of both the target number and the target states. Simulation results, with measurements generated from real target trajectories, demonstrate the ability of the proposed procedure to simultaneously detect and track ten targets with a reasonable sample size

Multitarget tracking using the joint multitarget probability density

This work addresses the problem of tracking multiple moving targets by recursively estimating the joint multitarget probability density (JMPD). Estimation of the JMPD is done in a Bayesian framework and provides a method for tracking multiple targets which allows nonlinear target motion and measurement to state coupling as well as nonGaussian target state densities. The JMPD technique simultaneously estimates both the target states and the number of targets in the surveillance region based on the set of measurements made. We give an implementation of the JMPD method based on particle filtering techniques and provide an adaptive sampling scheme which explicitly models the multitarget nature of the problem. We show that this implementation of the JMPD technique provides a natural way to track a collection of targets, is computationally tractable, and performs well under difficult conditions such as target crossing, convoy movement, and low measurement signal-to-noise ratio (SNR).