An Adaptive Estimator for Passive Range and Depth Determination of a Maneuvering Target
NAVAL UNDERWATER SYSTEMS CENTER NEWPORT RI
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The report describes an adaptive state estimator that can significantly improve the passive range and depth determination of a randomly maneuvering target. The target in this study is a submarine, which, while being tracked, performs large-magnitude depth changes at times unknown to the tracking submarine. Present passive tracking techniques usually utilize a Kalman filter to process the azimuth andor elevation observations. A Kalman filter will theoretically give the best estimates of target range, depth, and velocity when the system and measurement errors can be modeled as Gaussian processes. The main difficulty in using a Kalman filter in passive tracking applications is that large bias errors invariably develop as the target makes large alterations in velocity or depth. A technique for including a feedback-type learning processor in conjunction with the Kalman filter has been found to greatly reduce bias errors produced by the maneuvering target.
- Submarine Engineering
- Acoustic Detection and Detectors