The metrication of LPI radar waveforms based on the asymptotic spectral distribution of wigner matrices

This paper presents an effective metric to evaluate different kinds of low probability of interception (LPI) waveforms. Based on the common view that white noise is the best LPI waveform, the method introduced in this paper first use the asymptotic spectral distribution of Wigner matrix as the property of white noise and use the spectral distribution of the normalized sample covariance matrix as the property of a specific waveform. Then, a numerical approximation of Kullback-Leibler divergence (NA-KLD) is deduced to measure the distance between the two distributions. The NA-KLD is regarded as the metrication to evaluate LPI waveforms. A lower value of NA-KLD represents a better LPI performance. Simulations show that the proposed NA-KLD is effective and robust to evaluate LPI radar waveforms.