Power Spectral Density Estimation of Radiated Noise With Sparse Spectral Fitting

When measuring the radiated noise from a noncooperative target by a small-aperture vertical array, the location of the noise source is usually unknown, so most measurement methods are ineffective. To solve this problem, we improve the sparse spectral fitting (SpSF) method so that it can be used to estimate the power spectral density (PSD) of radiated noise even though the source location is unknown. First, the experiment area is discretized and the sparse representation model of the received data is established. Then the PSD expression of the radiated noise is established by the covariance matrix in frequency-domain. Finally, the PSD and location of the radiated noise can be estimated simultaneously by the SpSF method. Simulation examples are given to demonstrate the performance of PSD estimation by SpSF.