Abstract-This paper presents an approach for detection and tracking a micro-UAV using the multistatic radar NetRAD. Experimental trials were performed using NetRAD allowing for analysis of real data to assess the difficulty of detection and tracking of a micro-UAV target. The UAV detection is based on both time domain and micro-Doppler signatures, in order to enhance the discrimination between ground clutter and UAV returns. This micro-Doppler based procedure is shown to improve the clutter/target discrimination, in comparison to a Doppler-shift based procedure. The tracking approach is able to compensate for the limited quality measurement generated by each bistatic pair by fusing the measurements available from multiple bistatic pairs.
Previous works have identified key characteristics of a cognitive radar, such as knowledge exploitation, perception, action, memory, intelligence and attention. In this work, it is argued that the cognitive characteristic of anticipation can also enhance radar performance. In this paper it is shown that radar management using a partially observable Markov decision process (POMDP) enables the radar to act with anticipation. A method using policy rollout is applied to approximate a POMDP for a target tracking control problem. Through a simulated example it is demonstrated how the anticipative method departs from a purely adaptive approach, and the subsequent improvement in performance is quantified
This paper addresses the problem of allocating resource and selecting operational parameters for multiple tasks for an electronically steered phased array radar system. The continuous double auction parameter selection (CDAPS) algorithm is presented, which controls the operational parameters such that a resource constraint is satisfied. The algorithm optimises the complete multiple task parameter selection problem, in contrast to the common approach of optimising the parameter selection for each single task separately. It is shown that CDAPS produces a near-optimal solution for the single resource discrete parameter selection problem. Simulated scenarios verify that this near-optimum solution significantly improves upon conventional rule based methods. The additional desirable characteristics of the CDAPS algorithm: computational efficiency, scalability, and rapid reaction, are also highlighted
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