Parameter Optimization of Sparse Fourier Transform for Radar Target Detection

The sparse Fourier transform (SFT) can dramatically accelerate the spectral analyses by leveraging the inherit sparsity in radar echoes. However, a satisfactory accuracy-complexity trade-off commonly requires sophisticated empirical parameter tuning. In this context, this work attempts to enhance SFT by optimizing the parameter selection mechanism. We first derive closed-form expressions of two performance metrics with respect to the detection and false-alarm rates. On top of this, a parameter optimization algorithm is designed. The proposed scheme is able to automatically arrive at a optimized parameter settings considering the a priori knowledge and the performance requirements, which is confirmed by numerical simulations.