A new methodology for interference/jamming suppression employing a spatial filter measurement matrix (SFMM) is presented. Different from the previous Capon beamforming method, the proposed method is not dependent on accurate prior information of targets' directions-of-arrival (DOAs). The proposed SFMM suppresses the noise and interferences outside the searching area, thus apparently improves detection performance. The experimental results demonstrate the effectiveness of the proposed SFMM.

A new method based on joint sparse representation is developed to recover the peak information from high‐noise Raman signal. This method used the sparsity of Raman spectrum to recover the signals and preserve its useful peak information. The peak information is then reconstructed by using an orthogonal matching pursuit algorithm. The joint sparse representation method is found to be an effective approach to analyze the Raman spectrum, especially Raman spectrum that have high noise, thus improving the detection limit of Raman spectroscopy. Experimental results demonstrate that this approach is better than other approaches in case of low signal‐to‐noise ratios.

Low SNR condition has been a big challenge in the face of distributed compressive sensing MIMO radar (DCS-MIMO radar) and noise in measurements would decrease performance of radar system. In this paper, we first devise the scheme of DCS-MIMO radar including the joint sparse basis and the joint measurement matrix. Joint orthogonal matching pursuit (JOMP) algorithm is proposed to recover sparse targets scene. We then derive a recovery stability guarantee by employing the average coherence of the sensing matrix, further reducing the least amount of measurements which are necessary for stable recovery of the sparse scene in the presence of noise. Numerical results show that this scheme of DCS-MIMO radar could estimate targets’ parameters accurately and demonstrate that the proposed stability guarantee could further reduce the amount of data to be transferred and processed. We also show the phase transitions diagram of the DCS-MIMO radar system in simulations, pointing out the problem to be further solved in our future work.

A new methodology for sparse signal acquisition using a multi-comparator-based integrate-and-fire sampler is presented. By employing the randomness of comparator voltages, the original analogue signal is converted into a series of binaries and is guaranteed to be precisely recovered from these measurements. The proposed scheme operates with a sub-Nyquist rate, which requires neither a high-speed linear feedback shift register nor an accurate analogue-to-digital converter. The experimental results demonstrate the effectiveness of the proposed scheme.

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