Mitigation of Radar Range Deception Jamming Using Random Finite Sets

TL;DR

This paper introduces a radar tracking framework using random finite sets to counteract range deception jamming attacks, effectively estimating target position and detecting jamming without relying on extra signal features.

Contribution

It proposes a novel RFS-based approach with adaptive bias estimation for robust radar tracking under RGPO jamming attacks, enhancing detection and accuracy.

Findings

The adaptive method improves jamming bias estimation and detection.

The framework maintains tracking accuracy under multiple RGPO attacks.

Non-adaptive approach performs well with accurate prior knowledge.

Abstract

This paper presents a radar target tracking framework for addressing main-beam range deception jamming attacks using random finite sets (RFSs). Our system handles false alarms and detections with false range information through multiple hypothesis tracking (MHT) to resolve data association uncertainties. We focus on range gate pull-off (RGPO) attacks, where the attacker adds positive delays to the radar pulse, thereby mimicking the target trajectory while appearing at a larger distance from the radar. The proposed framework incorporates knowledge about the spatial behavior of the attack into the assumed RFS clutter model and uses only position information without relying on additional signal features. We present an adaptive solution that estimates the jammer-induced biases to improve tracking accuracy as well as a simpler non-adaptive version that performs well when accurate priors on the jamming range are available. Furthermore, an expression for RGPO attack detection is derived, where the adaptive solution offers superior performance. The presented strategies provide tracking resilience against multiple RGPO attacks in terms of position estimation accuracy and jamming detection without degrading tracking performance in the absence of jamming.