High-Resolution Power Spectral Estimation Method Using Deconvolution

Spectral analysis is a significant technique applied in many fields to infer the signal spectral contents. However, the frequency resolution of a signal spectrum estimation result is limited by its finite data length, especially when using a Fourier-based method. Extra processing gain [i.e., signal-to-noise ratio (SNR) improvement] is always required for weak target detection. In this paper, a high-resolution spectrum estimation method using a deconvolution algorithm is proposed. According to classical spectral analysis, a power spectrum derived from a finite data length is related to the convolution of the true power spectrum from an infinite length data set with the power spectrum from a window function. Therefore, using a deconvolution algorithm on the power spectrum estimated by classical spectral analysis can remove the influence from the window function, such as spectral leakage. The deconvolved power spectrum can robustly obtain a sufficiently high-frequency resolution and low sidelobes with relatively few calculations. The proposed method can also provide a deconvolution gain, which plays an important role in weak signal detection as it is capable of enhancing the signal and reducing background noise. Its performance is analyzed in simulations as well as with measured experimental data.