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A Cramér-Rao Lower Bound Derivation for Passive Sonar Track-Before-Detect Algorithms.
- Source :
-
IEEE Transactions on Information Theory . Oct2020, Vol. 66 Issue 10, p6449-6457. 9p. - Publication Year :
- 2020
-
Abstract
- Track-before-detect (TkBD) algorithms have been shown to greatly abate measurement-to-track association (MTA) challenges. These simplifications are aptly relevant for reducing operator workload in deployed sonar systems that require a human “in-the-loop.” In a prior manuscript a case study of a passive bearings-only target motion analysis TkBD algorithm was demonstrated in complex sonar scenarios relevant to advanced fielded sonar systems. In this manuscript, a Cramer-Rao Lower Bound (CRLB) is derived for the algorithm previously developed. The approximations used in developing the CRLB are validated with a real data set. The CRLB itself, as a predictor of state estimation error performance, is validated with single- and multi-contact simulated data scenarios. The prior algorithm and CRLB derived herein is applicable to passive sonar, active sonar, radar, and optical applications through a change of point spread functions and Jacobians. The CRLB derived is simple to implement, requires minimal statistical assumptions, and is applicable to similarly implemented TkBD algorithms. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SONAR
*ALGORITHMS
*OPTICAL radar
*JACOBIAN matrices
*APPROXIMATION algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 00189448
- Volume :
- 66
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Information Theory
- Publication Type :
- Academic Journal
- Accession number :
- 146079934
- Full Text :
- https://doi.org/10.1109/TIT.2020.3013991