Paper
7 August 2002 Efficient multisensor resource management using Cramer-Rao lower bounds
Author Affiliations +
Abstract
This paper describes the development of a general framework for the efficient management of multiple sensors in target tracking. The basis of the technique is to quantify, and subsequently control, the accuracy of target state estimation. The Posterior Cramer-Rao lower bound provides the means of achieving this aim by enabling us to determine a bound on the performance of all unbiased estimators of the unknown target state. The general approach is then to use optimization techniques to control the measurement process in order to achieve accurate target state estimation. We are concerned primarily with the deployment and utilization of a limited sensor resource. We also allow for measurement origin uncertainty, with sensor measurements either target generated or false alarms. We exploit previous work to determine a general expression for the Fisher Information Matrix in this case. We show that by making certain assumptions we can express the measurement uncertainty as a constant information reduction factor. This enables the Fisher Information Matrix to be calculated quickly, allowing Cramer-Rao bounds to be utilized for real-time, online sensor management.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Marcel L. Hernandez, Thiagalingam Kirubarajan, and Yaakov Bar-Shalom "Efficient multisensor resource management using Cramer-Rao lower bounds", Proc. SPIE 4728, Signal and Data Processing of Small Targets 2002, (7 August 2002); https://doi.org/10.1117/12.478520
Lens.org Logo
CITATIONS
Cited by 8 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Sensors

Error analysis

Detection and tracking algorithms

Particle filters

Process control

Monte Carlo methods

Target detection

Back to Top