Abstract

While there are many techniques for bearings-only tracking (BOT) in the ocean environment, they do not apply directly to the land situation. Generally, for tactical reasons, the land observer platform is stationary; but, it has two sensors, visible and infrared, for measuring bearings and a laser range finder (LRF) for measuring range. There is a requirement to develop a new BOT data fusion scheme that fuses the two sets of bearing readings, and together with a single LRF measurement, produces a unique track. This paper first develops a parameterized solution for the target speeds, and then heading, prior to the occurrence of the LRF measurement, when the track is unobservable. At, and after the LRF measurement, a BOT, formulated as a least squares (LS) estimator, then produces a unique LS estimate of the target states. Bearing readings from the other sensor serve as instrumental variables in a data fusion setting to eliminate the bias in the BOT estimator. The result is an unbiased and decentralized data fusion scheme. Results from two simulation experiments have corroborated the theoretical development and show also that the scheme is optimal.

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