Online Bayesian Meta-Learning for Cognitive Tracking Radar

A key component of cognitive radar is the ability to generalize , or achieve consistent performance across a range sensing environments, since aspects physical scene may vary over time. This presents challenge for learning-based waveform selection approaches, transmission policies which are effective in one be highly suboptimal another. We address this problem by strategically xmlns:xlink="http://www.w3.org/1999/xlink">biasing learning algorithm exploiting high-level structure tracking instances, referred as xmlns:xlink="http://www.w3.org/1999/xlink">meta-learning . In work, we develop an online meta-learning approach waveform-agile tracking. uses information gained from previous target tracks speed up and enhance new instances. results sample-efficient class finite state channels inherent similarity scenes, attributed common elements such type clutter statistics. formulate within framework Bayesian learning, provide prior-dependent bounds using Probability Approximately Correct (PAC)-Bayes theory. present computationally feasible meta-posterior sampling study simulation consisting diverse scenes. Finally, examine potential benefits practical challenges associated with