Target Motion Analysis With Passive Measurements and Partial Prior Information

Abstract

This work demonstrates a simple but effective method by which prior information on the target range can be included in the likelihood function (i.e., in a non-Bayesian framework) for bearings-only as well as Doppler-bearings target motion analysis (tracking with passive measurements). The prior information is treated as a pseudomeasurement on the initial target range, i.e., the target range relative to the observer during the first time instance of the tracking period. The pseudomeasurement may be modeled using a Gaussian distribution or a Gaussian mixture model distribution. An estimation technique is derived as an extension to the well-known maximum likelihood estimator. The performance bounds naturally follow as an extension to the Cramer–Rao lower bound. The use of a range pseudomeasurement adds additional design parameters to the estimation process. Practical methodology and illustrative examples are provided for parameter set design. The statistical efficiency of the estimator is confirmed using Monte-Carlo trials on several experimental configurations. The simulated scenarios include bearings-only measurements as well as Doppler-bearings measurements.