Non-Parametric and Geometric Multi-Target Data Association for Distributed MIMO Radars

Abstract

Distributed MIMO radar systems offer tremendous advantage in the detection of airborne platforms employing stealth and are resilient to single point failure. However, when multiple targets are present over the surveillance region, the reflected signals at various receivers from these targets cannot be uniquely associated to the targets easily. Incorrect associations of the received data lead to the creation of ghost targets, and hence, de-ghosting is an inherent problem in distributed radar systems. Exploiting the geometry of the measurement model into the association process, we devise algorithms that are practically implementable and computationally feasible. In this work, a novel, efficient and fast data association scheme followed by a localization algorithm is proposed that utilizes Time-of-Arrival and Doppler frequency measurements of the targets with respect to the transmitter-receiver pairs to accurately determine 3D position and velocities of the targets. The proposed approach is non-parametric as it does not need the assumption of initial states, number of targets and their motion models. It simultaneously associates up to four targets present within a minimum horizontal separation of $100m\times 100m$ for signals of bandwidth 20MHz and any number of targets that are flying far away from this minimum separation in the observation region. It can also associate and track up to nine targets that have sequential birth and random death, flying with random realizable velocities.