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In this paper, we propose a new type of array antenna, termed the Random Frequency Diverse Array (RFDA), for an uncoupled indication of target direction and range with low system complexity. In RFDA, each array element has a narrow bandwidth and a randomly assigned carrier frequency. The beampattern of the array is shown to be stochastic but thumbtack-like, and its stochastic characteristics, such as the mean, variance, and asymptotic distribution are derived analytically. Based on these two features, we propose two kinds of algorithms for signal processing. One is matched filtering, due to the beampatterns good characteristics. The other is compressive sensing, because the new approach can be regarded as a sparse and random sampling of target information in the spatial-frequency domain. Fundamental limits, such as the Cramer-Rao bound and the observing matrixs mutual coherence, are provided as performance guarantees of the new array structure. The features and performances of RFDA are verified with numerical results.
Frequency diverse (FD) radar waveforms are attractive in radar research and practice. By combining two typical FD waveforms, the frequency diverse array (FDA) and the stepped-frequency (SF) pulse train, we propose a general FD waveform model, termed
Compressed sensing (CS) or sparse signal reconstruction (SSR) is a signal processing technique that exploits the fact that acquired data can have a sparse representation in some basis. One popular technique to reconstruct or approximate the unknown s
Compressive subspace learning (CSL) with the exploitation of space diversity has found a potential performance improvement for wideband spectrum sensing (WBSS). However, previous works mainly focus on either exploiting antenna auto-correlations or ad
Compressive sensing has shown significant promise in biomedical fields. It reconstructs a signal from sub-Nyquist random linear measurements. Classical methods only exploit the sparsity in one domain. A lot of biomedical signals have additional struc
In this paper, a new cooperation structure for spectrum sensing in cognitive radio networks is proposed which outperforms the existing commonly-used ones in terms of energy efficiency. The efficiency is achieved in the proposed design by introducing