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
Policy rollout is a method for the online computation of future costs in approximate dynamic programming, and has been utilized for various problems including sensor management. In previous work, it has predominately been applied to the selection of actions from discrete sets. In this paper we present methods for action selection from continuous sets and analyze their trade-offs. The methods are evaluated on the problem of sensor path planning, with the intent of minimizing the time to localize an emitter using bearing measurements.
Reinforcement learning is the problem of autonomously learning a policy guided only by a reward function. We evaluate the performance of the Proximal Policy Optimization (PPO) reinforcement learning algorithm on a sensor management task and study the influence of several design choices about the network structure and reward function. The chosen sensor management task is optimizing the sensor path to speed up the localization of an emitter using only bearing measurements. Furthermore, we discuss generic advantages and challenges when using reinforcement learning for sensor management.
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