Paper
9 August 2004 Utilizing negative information to track ground vehicles through move-stop-move cycles
Craig S. Agate, Robert M. Wilkerson, Kevin J. Sullivan
Author Affiliations +
Abstract
Ground vehicles can be effectively tracked using a moving target indicator (MTI) radar. However, vehicles whose velocity along the line-of-sight to the radar falls below the minimum detectable velocity (MDV) are not detected. One way targets avoid detection, therefore, is to execute a series of move-stop-move motion cycles. While a target can be acquired after beginning to move again, it may not be recognized as a target previously in track. Particularly for the case of high-value targets, it is imperative that a vehicle be continuously tracked. We present an algorithm for determining the probability that a target has stopped and an estimate of its stopped state (which could be passed to a tasker to schedule a spot synthetic aperature radar (SAR) measurement. We treat a non-detection event as evidence that can be used to update the target state probability density function (PDF). Updating the target state PDF using a non-detection event pushes the probability mass into regions of the state space in which the vehicle is either stopped or traveling at a speed such that the range-rate fails the MDV. The target state PDF updated with the non-detection events is then used to derive an estimate of the stopped target’s location. Updating the target state PDF using a non-detection event is, in general, non-trivial and approximations are required to evaluate the updated PDF. When implemented with a particle filter, however, the updating formula is simple to evaluate and still captures the subtleties of the problem.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Craig S. Agate, Robert M. Wilkerson, and Kevin J. Sullivan "Utilizing negative information to track ground vehicles through move-stop-move cycles", Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); https://doi.org/10.1117/12.542575
Lens.org Logo
CITATIONS
Cited by 22 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Target detection

Particle filters

Sensors

Motion models

Particles

Detection and tracking algorithms

Radar

Back to Top