Improved Detection by Peak Shape Recognition Using Artificial Neural Networks

Conventional peak detection algorithms are not designed to include information on the expected peak shape. Therefore, commonly used detectors discard this valuable information and do not perform optimally in regard to the given possibilities. Designed and evaluated is a detector based on an artificial neural network, which is employed for pattern recognition in order to exploit the peak shape information. The detector outperforms the best detector using no peak shape information with a detection rate increase of up to 10\% at a constant false alarm rate. The proposed detection method is compared with a threshold detector and an ordered statistics constant false alarm rate (OS-CFAR) detector commonly used in radar. The introduced detector provides useful information on the reliability of the peak detection. Furthermore, it is shown that the neural network based detection mechanism is easily employable on hardware because no knowledge about the signal-to-noise ratio of the training data is needed.