Processing of MST Radar Data Using the Covariance Fitting Algorithm

This paper introduces a high-resolution spectral estimation algorithm that minimizes the Frobenius norm between the estimated covariance matrix (derived from data) and the true covariance matrix (based on the assumed data model). The algorithm is iterative and employs a gradient descent method. Unlike the Sparse Iterative Covariance-based Estimation (SPICE) algorithm, it does not require calculating the inverse of the covariance matrix. It performs well in estimating the spectrum of simulated data, even at low Signal-to-Noise Ratios (SNR), demonstrating reliable performance. The results of this Proposed Method (PM) are compared with those from the periodogram and SPICE algorithms. Subsequently, the algorithm is applied to MST radar data to estimate the Doppler spectrum and, thus, the wind speed. These results are also compared with the periodogram and SPICE outputs. The Proposed Method demonstrates superior performance compared to the periodogram and yields results nearly identical to those of SPICE. To validate these findings, the estimated wind speed is compared with data from Global Positioning System (GPS) radiosonde. Satisfactory results are observed for practical MST radar measurement data.