A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

Abstract

The problem of data association for target tracking in a cluttered environment is discussed. In order to improve the real-time processing and accuracy of target tracking, based on a probabilistic data association algorithm, a novel data association algorithm using distance weighting was proposed, which can enhance the association probability of measurement originated from target, and then using a Kalman filter to estimate the target state more accurately. Thus, the tracking performance of the proposed algorithm when tracking non-maneuvering targets in a densely cluttered environment has improved, and also does better when two targets are parallel to each other, or at a small-angle crossing in a densely cluttered environment. As for maneuvering target issues, usually with an interactive multi-model framework, combined with the improved probabilistic data association method, we propose an improved algorithm using a combined interactive multiple model probabilistic data association algorithm to track a maneuvering target in a densely cluttered environment. Through Monte Carlo simulation, the results show that the proposed algorithm can be more effective and reliable for different scenarios of target tracking in a densely cluttered environment.

Optimized Algorithm for Target Tracking using Classification Information based Association Filter

“…The tracking process always based on two models: the target motion model and measurement model. Motion model describes the target motion dynamics in terms of velocity, acceleration and turn rate whereas measurement model captures the target's state and covariance in terms of measurement matrix with 2-D or 3-D coordinates [3].…”

“…Tracking targets always involves association of set of mk approved estimations gotten by gating tests as portrayed in segment 2 with realized targets set T. The occasion ɸij is characterized as estimation j began from objective I. Relating estimation to target affiliation probabilities to these occasions are determined together crosswise over focuses with assistance of Probabilistic information affiliation channel [2] [3]. A mistake model Kalman channel (EKF) calculation is connected to every one of the approved estimations.…”

Collaborative Tracking in Distributed Multi-Sensors Video Surveillance Systems

“…Joint Probability Data Association (JPDA) [7] computes an estimate over the various possibilities of measurement-to-track associations. Assume that at time step k, there are N T targets in the scene and camera C i obtains measurements,…”

A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

“…Both the IMM and CPIMM include three models: one Constant Velocity (CV) model [44] and two Coordinated Turn (CT) models [44].…”

“…The process noise meets with The two models use the following transition and mode probabilities. The transition probability matrix is Both the IMM and CPIMM include three models: one Constant Velocity (CV) model [44] and two Coordinated Turn (CT) models [44].…”