Trigonometric bearings-only tracking for a single stationary observer

Terry Rea; Y. T. Chan

doi:10.1117/12.477604

31 July 2002 Trigonometric bearings-only tracking for a single stationary observer

Terry Rea, Y. T. Chan

Proceedings Volume 4729, Signal Processing, Sensor Fusion, and Target Recognition XI; (2002) https://doi.org/10.1117/12.477604
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

A single, stationary observer cannot determine a unique target track with bearings-only measurements. In the land environment, for tactical reasons, the observer typically remains stationary but can measure the target range by a laser rangefinder (LRF). Bearings-only tracking of a non-maneuvering target is a non-linear problem. Solutions by iteration or the extended Kalman filter suffer from a high computation load and possible filter divergence. In contrast, the pseudo-linear formulation permits the application of a linear Kalman filter but the range estimate has a bias, which eliminates through instrumental variables. The development in showed that even though a target track is indeterminate due to a stationary observer, a unique target heading is still available from the bearings-only measurements. Then after an LRF range measurement, Rl, future estimates of target position and velocity become determinant. This paper gives a new tracking scheme for a stationary observer that gives the range estimate as a function of Rl, the target heading and bearings. The estimation equation comes from the trigonometric Law of Sines and is simple to implement. The estimator is unbiased and simulation experiments have shown that the estimates are close to the Cramer-Rao Lower Bound.

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Terry Rea and Y. T. Chan "Trigonometric bearings-only tracking for a single stationary observer", Proc. SPIE 4729, Signal Processing, Sensor Fusion, and Target Recognition XI, (31 July 2002); https://doi.org/10.1117/12.477604

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KEYWORDS

Laser range finders

Filtering (signal processing)

Infrared search and track

Infrared sensors

Sensors

Electronic filtering

Environmental sensing

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