TBD ( Track-before-detect )

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<Track-before-detect procedures for detection of extended object>

In this article, we present a particle filter (PF)-based track-before-detect (PF TBD) procedure for detection of extended objects whose shape is modeled by an ellipse. By incorporating of an existence variable and the target shape parameters into the state vector, the proposed algorithm performs joint estimation of the target presence/absence, trajectory and shape parameters under unknown nuisance parameters (target power and noise variance). Simulation results show that the proposed algorithm has good detection and tracking capabilities for extended objects.

List of abbreviations

LOS

line-of-sight

ML

maximum likelihood

PF

particle filter

PF TBD

particle filter-based track-before-detect

RMSE

root mean square error

SNR

signal-to-noise ratio

TBD

track-before-detect

A Bayesian track-before-detect procedure for passive radars

This article presents a Bayesian algorithm for detection and tracking of a target using the track-before-detect framework. This strategy enables to detect weak targets and to circumvent the data association problem originating from the detection stage of classical radar systems. We first establish a Bayesian recursion, which propagates the target state probability density function. Since raw measurements are generally related to the target state through a nonlinear observation function, this recursion does not admit a closed form expression. Therefore, in order to obtain a tractable formulation, we propose a Gaussian mixture approximation. Our targeted application is passive radar, with civilian broadcasters used as illuminators of opportunity. Numerical simulations show the ability of the proposed algorithm to detect and track a target at very low signal-to-noise ratios.

Track-before-detect

From Wikipedia, the free encyclopedia

In radar technology and similar fields, track-before-detect (TBD) is a concept according to which a signal is tracked before declaring it a target. In this approach, the sensor data about a tentative target are integrated over time and may yield detection in cases when signals from any particular time instance are too weak against clutter (low signal-to-noise ratio) to register a detected target.