Bistatic Measurement Fusion from Multistatic Configurations for Air Collision Warning

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1 December 2015
Wenbo Dou, Yaakov Bar-Shalom, Peter Willett

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A requisite for unmanned aircraft systems (UAS) to operate within a controlled airspace is a capability to sense and avoid collisions with non-cooperative aircraft. Ground-based transmitters and UAS-mounted receivers are preferred due to limitations on UAS. This paper assumes a constant velocity motion of an intruder (tar-get) aircraft and presents a method to estimate the position and velocity of the target so as to predict the closest point of approach. Bistatic range and range rate are assumed the only measurements available. Several configurations are investigated from a parameter observability point of view. It turns out that one needs three transmitters in a general three-dimensional scenario to achieve decent observability of the target motion parameter. With the assumption that the target is at the same altitude as the ownship, one has a two-dimensional scenario in which two transmitters are required in order to have good observability. Simulation results show that the maximum likelihood (ML) estimate of the target parameter using an iterated least squares search can be considered as statistically efficient in both multistatic configurations with good observability for the scenarios considered in this paper. The collision warning can be carried out based on the ML estimate in two different ways. The first approach is to formulate the collision as a hypothesis testing problem using a generalized likelihood function. A second, Bayesian, approach is also presented. The performance of the likelihood based collision warning shows that the multistatic configuration with three transmitters is reliable for collision warning but that the multistatic configuration with two transmitters under the same target and ownship altitude assumption is prone to false alarms. In the configuration with three transmitters, the Bayesian approach yields the similarly reliable collision warning performance as the likelihood-based approach when they use threshold values of the same magnitude.