Abstract
Conventional beamforming and classical spectral estimation methods are widely used in array signal processing to estimate the angular and frequency spectrum, respectively. But the spectra provided by these algorithms appear as low resolutions and high side lobes because of the limited array aperture and the number of snapshots. Then a multi-dimensional window function is defined, which is composed of the snapshot number and array aperture. Based on the theories of space-time joint spectral estimation, the multi-dimensional power spectrum about the azimuthal angle and frequency of array signal can be deduced as the convolution of the multi-dimensional power spectrum of the true signal and the power spectrum of the multi-dimensional window function. Therefore, the deconvolution algorithm can be introduced to remove the influence from the window function to recover the power spectrum of the true signal which is considered to be received from the infinite time and space. It is illustrated from simulations and experimental data processing that the deconvolution method can greatly improve the angular and frequency resolutions of the multi-dimensional spectrum, suppress the side lobes and provide extra signal-to-noise ratio, which can be applied for multi-target identification and weak signal detection.
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