On the Cramér-Rao lower bound for biased bearings-only maneuvering target tracking

Authors:
Benlian Xu;Zhengyi Wu;Zhiquan Wang
Affiliations:
Department of Information and Control Engineering, Changshu Institute of Technology, 215500 Changshu, PR China and School of Automation, Nanjing University of Science & Technology, 210094 Nanjing, ...;Department of Information and Control Engineering, Changshu Institute of Technology, 215500 Changshu, PR China;School of Automation, Nanjing University of Science & Technology, 210094 Nanjing, PR China
Venue:
Signal Processing
Year:
2007

Citing 8
Cited 1

Estimation with Applications to Tracking and Navigation

Estimation with Applications to Tracking and Navigation
Tracking a manoeuvring target using angle-only measurements: algorithms and performance

Signal Processing
On the bias of linear least squares algorithms for passive target localization

Signal Processing
Bearings-only target localization using total least squares

Signal Processing
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering

IEEE Transactions on Signal Processing
Simple approximations to the Cramer-Rao lower bound on direction ofarrival for closely spaced sources

IEEE Transactions on Signal Processing
Tracking a maneuvering target using neural fuzzy network

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Filtering, predictive, and smoothing Cramér-Rao bounds for discrete-time nonlinear dynamic systems

Automatica (Journal of IFAC)

Cramer-Rao lower bounds for bearings-only maneuvering target tracking with incomplete measurements

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference

Quantified Score

Hi-index	0.08

Visualization

Abstract

This paper aims to investigate the theoretical performance of bearings-only maneuvering target tracking when fixed measurement biases exist. Firstly, the general formulation of Cramér-Rao lower bound (CRLB) is presented when both target's maneuver and fixed measurement biases work simultaneously in a bistatic system, and the corresponding recursive CRLB formulation is then derived. Finally, a maneuver detection method is developed to calculate the approximation of the theoretical CRLB. Numerical simulation results indicate either that the proposed or the fuzzy-neural-network-based maneuver detection method can yield a satisfying CRLB compared with the theoretical CRLB, but the proposed method is easier to implement owing to directly utilizing the target's bearing information.