An Adaptive Estimator for Passive Range and Depth Determination of a Maneuvering Target

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 sub...

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Bibliographic Details
Main Author: Moose, Richard L
Format: Report
Language:English
Subjects:
Acoustic Detection and Detectors
ANTISUBMARINE WARFARE
computer aided analysis
computerized simulation
EVASION
kalman filters
MATHEMATICAL MODELS
Most Project-3
RANDOM VARIABLES
Submarine Engineering
SUBMARINES
TARGET ACQUISITION
UNDERWATER TRACKING
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Summary: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 and/or 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.