Bistatic Target Detection by Exploiting Both Deterministic Pilots and Unknown Random Data Payloads

TL;DR

This paper develops a GLRT-based target detection method for hybrid ISAC signals in 6G, effectively utilizing deterministic pilots and unknown random data payloads despite their coupled effects on the received signal.

Contribution

It introduces a novel detection algorithm that leverages both known pilots and statistical properties of unknown data, addressing challenges in bistatic ISAC target detection.

Findings

The proposed detector outperforms existing methods in simulations.

Trade-off identified between detection reliability and statistical uncertainty.

Asymptotic analysis provides insights into false alarm and detection probabilities.

Abstract

Integrated sensing and communication (ISAC) plays a crucial role in 6G, to enable innovative applications such as drone surveillance, urban air mobility, and low-altitude logistics. However, the hybrid ISAC signal, which comprises deterministic pilot and random data payload components, poses challenges for target detection due to two reasons: 1) these two components cause coupled shifts in both the mean and variance of the received signal, and 2) the random data payloads are typically unknown to the sensing receiver in the bistatic setting. Unfortunately, these challenges could not be tackled by existing target detection algorithms. In this paper, a generalized likelihood ratio test (GLRT)-based detector is derived, by leveraging the known deterministic pilots and the statistical characteristics of the unknown random data payloads. Due to the analytical intractability of exact performance characterization, we perform an asymptotic analysis for the false alarm probability and detection probability of the proposed detector. The results highlight a critical trade-off: both deterministic and random components improve detection reliability, but the latter also brings statistical uncertainty that hinders detection performance. Simulations validate the theoretical findings and demonstrate the effectiveness of the proposed detector, which highlights the necessity of designing a dedicated detector to fully exploited the signaling resources assigned to random data payloads.