High-performance Passive Eigen-model-based Detectors of Single Emitter Using Massive MIMO Receivers

TL;DR

This paper introduces three high-performance passive emitter detection methods based on eigen-space analysis of massive MIMO signals, outperforming traditional techniques in detection accuracy with low false alarm rates.

Contribution

The paper proposes three novel eigen-model-based detectors for passive emitter detection using massive MIMO, with closed-form expressions and improved performance over existing methods.

Findings

Proposed detectors outperform traditional GLRT in detection accuracy.

Two proposed methods achieve similar performance with lower false alarm rates.

Closed-form expressions enable practical implementation of detectors.

Abstract

For a passive direction of arrival (DoA) measurement system using massive multiple input multiple output (MIMO), it is mandatory to infer whether the emitter exists or not before performing DOA estimation operation. Inspired by the detection idea from radio detection and ranging (radar), three high-performance detectors are proposed to infer the existence of single passive emitter from the eigen-space of sample covariance matrix of receive signal vector. The test statistic (TS) of the first method is defined as the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) while that of the second one is defined as the ratio of Max-EV to noise variance (R-MaxEV-NV). The TS of the third method is the mean of maximum eigen-value (EV) and minimum EV(M-MaxEV-MinEV). Their closed-form expressions are presented and the corresponding detection performance is given. Simulation results show that the proposed M-MaxEV-MinEV and R-MaxEV-NV methods can approximately achieve the same detection performance that is better than the traditional generalized likelihood ratio test method with false alarm probability being less than 0.3.