Joint Inverse Learning of Cognitive Radar Perception and Perception-Action Policy

TL;DR

This paper introduces a novel Bayesian inverse learning framework for cognitive radars, jointly estimating perception and perception-action policies from observed actions, with improved accuracy and uncertainty quantification.

Contribution

It develops the IPFDDP algorithm, a nonparametric Bayesian method that jointly infers perception and action policies in adversarial settings, addressing limitations of existing approaches.

Findings

IPFDDP outperforms existing inverse learning methods in accuracy.

Active probing with IPFDDP reduces KL divergence faster.

The framework quantifies uncertainty, enabling active probing strategies.

Abstract

Cognitive Radars (CRs) employ perception-action cycle to adapt their sensing and transmission strategies based on its' perception of the target kinematic states and mission objectives. This paper considers an inverse learning Electronic Counter Measure (ECM) that infers both the perception and perception-driven action policy of the adversarial CR's from the actions of the CR, i.e. the sensing and transmission actions taken by the CR. Existing frameworks, in the literature, assume the knowledge of either the perception or the perception-action policy and infer the other. However, this assumption is unrealistic in an adversarial setting. We address this gap by proposing an online, nonparametric Bayesian machine learning framework and developing the Inverse Particle Filter with Dependent Dirichlet Process (IPFDDP) algorithm, which characterizes the perception-dependent action policy using a Dependent Dirichlet Process (DDP) and embeds kernel-based DDP inference within a Bayesian inverse particle filtering framework to jointly estimate the CR's perception and perception-action policy. Extensive numerical simulations demonstrate that IPFDDP outperforms existing inverse learning methods in terms of mean squared error, Kullback-Leibler divergence between the estimated and true policy, and accuracy in identifying relative action preferences. Unlike the existing techniques, the proposed Bayesian formulation naturally quantifies uncertainty in inferred perception and perception-action policy, enabling active probing strategies for sample efficient inverse learning. Simulation results show that active probing integrated with IPFDDP achieves, on average, a 40% faster reduction in KL divergence compared to randomized probing.