CRLB and ML for parametric estimate: new results
Signal Processing
Robust sequential view planning for object recognition using multiple cameras
Image and Vision Computing
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
IEEE Transactions on Signal Processing
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The paper presents a comparative study of two recently reported Crame´r-Rao lower bounds (CRLBs) for nonlinear filtering, both applicable when the probability of detection is less than unity. The first bound is the information reduction factor CRLB; the second is the enumeration method CRLB. The enumeration method is accurate but computationally expensive. We prove in the paper that the information reduction factor bound is overoptimistic, being always less than the enumeration CRLB. The theory is illustrated by two target tracking applications: ballistic object tracking and bearings-only tracking. The simulations studies confirm the theory and reveal that the information reduction factor CRLB rapidly approaches the enumeration CRLB as the scan number increases.