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Passive guidance schemes that employ measurement of relative bearing to the target via an angle-or-arrival mechanism (such as optical telescope or radar antenna) offers several strategic benefits but suffer from the unavailability of measurement of range or range-rate. Passive ranging, i.e., estimation of range information from available measurements, is fraught with many technical challenges, and particularly in an air-to-air missile guidance context is complicated by a stubborn observability problem. As a missile maneuvers for an optimal intercept solutions, range and range-rate observability are degraded and, in the presence of measurement noise and target acceleration, become completely unobservable. Available schemes that typically employ extended Kalman filter solutions perform well against non-maneuvering targets but suffer estimation bias and divergence as intercept is approached. Interactive Multiple Model solutions promoted in prior works show promise in removing estimation bias due to target maneuver but have so far been restricted to active ranging problems. In this paper we shall present a novel Multiple Maneuver Model Filter (termed M3F in the following) that employs a suite of constant acceleration models in order to reliably estimate any target maneuver executed in the vertical as well as the horizontal plane. To quantitatively demonstrate the tracking performance of this filter, a set of benchmark tracking scenarios which present a broad range of problems relevant to passive ranging in an air-to-air missile context is also developed in this work. It should be emphasized that while several benchmark tracking problems in a surveillance radar context are recently developed, especially for testing the beam steering efficiency of a phased array system, these are not particularly useful for evaluating the performance of an air-to-air missile guidance scheme, and hence the benchmark scenarios developed in this work are of independent interest. Simulations of the M3F against the benchmark cases are also included to demonstrate the superior performance offered by the present algorithm in reducing estimation bias compared to existing techniques.
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