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MIMO radar moving target detector with model-aided learning
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AI
DOI：10.1016/j.dsp.2025.105130.png)
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Abstract
En 中文
The moving target detection (MTD) problem in multiple-input multiple-output (MIMO) radar usually involves a large number of unknown parameters, such as clutter-plus-noise parameters, target reflection coefficients, and target velocity. The presence of these numerous unknowns poses significant challenges to traditional model- based detector such as generalized likelihood ratio test (GLRT), whose performance quickly deteriorates when the number of unknown parameters increases. Recently, learning-based method has been recognized as an effective strategy to deal with MTD. However, it requires a large number of training data to ensure accuracy, which is not easily acquired in practical scenarios. Statistical model is derived from historical data, and therefore incorporating this prior knowledge into learning network intuitively reduces the number of training data. In this work, we propose a model-aided learning moving target detector that combines the prior knowledge provided by the statistical model with the purely learning-based approach. Through simulations, we compare the model-aided learning moving target detector with model-based GLRT and purely learning-based detector to demonstrate its effectiveness.
Keywords:
Moving target detection (MTD)
Multiple-input multiple-output (MIMO) radar
Model-based detector
Purely learning-based detector
Training data
Model-aided learning moving target detector
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