Multiframe Detection via Graph Neural Networks: A Link Prediction Approach

TL;DR

This paper introduces a novel graph neural network-based link prediction method for multi-frame detection, unifying track search and detection, which enhances weak target detection and reduces false alarms compared to traditional algorithms.

Contribution

It reformulates multi-frame detection as a link prediction problem using GNNs, enabling direct target track output and improved detection performance.

Findings

Improves detection of weak targets

Reduces false alarms

Unifies track search and detection

Abstract

Multi-frame detection algorithms can effectively utilize the correlation between consecutive echoes to improve the detection performance of weak targets. Existing efficient multi-frame detection algorithms are typically based on three sequential steps: plot extraction via a relative low primary threshold, track search and track detection. However, these three-stage processing algorithms may result in a notable loss of detection performance and do not fully leverage the available echo information across frames. As to applying graph neural networks in multi-frame detection, the algorithms are primarily based on node classification tasks, which cannot directly output target tracks. In this paper, we reformulate the multi-frame detection problem as a link prediction task in graphs. First, we perform a rough association of multi-frame observations that exceed the low threshold to construct observation association graphs. Subsequently, a multi-feature link prediction network is designed based on graph neural networks, which integrates multi-dimensional information, including echo structure, Doppler information, and spatio-temporal coupling of plots. By leveraging the principle of link prediction, we unifies the processes of track search and track detection into one step to reduce performance loss and directly output target tracks. Experimental results indicate that, compared with traditional single-frame and multi-frame detection algorithms, the proposed algorithm improves the detection performance of weak targets while suppressing false alarms. Additionally, interpretable analysis shows that the designed network effectively integrates the utilized features, allowing for accurate associations between targets and false alarms.