We present a 2-D Direction of arrival algorithm whose resolution is superior to that of the subspace class of algorithms and sidelobes are reduced compared to most algorithms. The algorithm is based on the 2-D AR Power Spectral Density (2-D ARPSD) applied to a uniformly spaced data set (space-time) which transforms the space-time data to spatial frequency (wavenumber, which is a function of the direction of arrival) and temporal frequency in a high resolution context. This is done by modeling the sensor array data with a 2-D AR model. The 2-D AR parameters are then used in a specialized form of a 2-D FFT to create an enhanced wavenumber-frequency image. A wavenumber vector for a specific narrowband temporal frequency is extracted and compared to other high resolution algorithm such as MUSIC. Our results exhibit superior performance in low SNR and short sample sized scenarios and when mismatch occurs in the subspace techniques. Our technique also exhibits reduced sidelobes as compared with traditional methods.
KEYWORDS: Time-frequency analysis, Radar, Doppler effect, Signal analysis, Radar signal processing, Fourier transforms, High dynamic range imaging, Autoregressive models, Sensors, Imaging systems
Despite the enhanced time-frequency analysis (TFA) detailing capability of quadratic TFAs like the Wigner and Cohen representations, their performance with signals of large dynamic range (DNR in excess of 40 dB) is not acceptable due to the inability to totally suppress the cross-term artifacts which typically are much stronger than the weakest signal components that they obscure. AMTI and GMTI radar targets exhibit such high dynamic range when microDoppler is present, with the aspects of interest being the weakest components. This paper presents one of two modifications of linear TFA to provide the enhanced detailing behavior of quadratic TFAs without introducing cross terms, making it possible to see the time-frequency detail of extremely weak signal components. The technique described here is based on subspace-enhanced linear predictive extrapolation of the data within each analysis window to create a longer data sequence for conventional STFT TFA. The other technique, based on formation of a special two-dimensional transformed data matrix analyzed by high-definition two-dimensional spectral analysis methods such as 2-D AR or 2-D minimum variance, is compared to the new technique using actual AMTI and GMTI radar data.
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