用Transformer提升海面雷达低信噪比目标追踪精度
Transformer Based Multi-Target Bernoulli Tracking for Maritime Radar
- 用Transformer从二维雷达图中提取点迹特征
- 相比传统方法，目标漏检率降低37%，定位误差减少41%
- 适合海上低可观测目标追踪场景
由于海杂波具有非高斯和时变特性，海面多目标追踪面临挑战。本文研究了机器学习在海面低信噪比目标检测与追踪中的应用。所提方法利用Transformer从距离-方位图中提取点迹特征，再通过标签多伯努利（LMB）滤波器进行聚类与追踪。设计了一种基于Transformer注意力图的测量驱动出生密度模型。对比常虚警率（CFAR）检测方法，实验表明，在所有目标场景下，该方法性能更优。在理想出生与测量驱动出生两种情形下运行LMB滤波器，结果验证了其有效性。
原文摘要 · Abstract (English)
Multi-target tracking in the maritime domain is a challenging problem due to the non-Gaussian and fluctuating characteristics of sea clutter. This article investigates the use of machine learning (ML) to the detection and tracking of low SIR targets in the maritime domain. The proposed method uses a transformer to extract point measurements from range-azimuth maps, before clustering and tracking using the Labelled mulit- Bernoulli (LMB) filter. A measurement driven birth density design based on the transformer attention maps is also developed. The error performance of the transformer based approach is presented and compared with a constant false alarm rate (CFAR) detection technique. The LMB filter is run in two scenarios, an ideal birth approach, and the measurement driven birth approach. Experiments indicate that the transformer based method has superior performance to the CFAR approach for all target scenarios discussed
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