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Orthogonal frequency division multiplexing which is called OFDM for short is not only a popular modulation technique in communication systems but also a good method to generate radar signals. A joint radar and communication system could be realised by an OFDM system according to some off-the-shelf works. The radar functionality is mainly considered here, which requires the system to equip with the ability to suppress interference. The typical radar signal, frequency-modulated continuous wave, can be viewed as narrowband interference for a large bandwidth OFDM radar with comparably short duration of OFDM symbols. Here, an interference suppression algorithm suitable for any type of narrowband interference is proposed for OFDM radar. The atomic norm minimisation (ANM) method involved in compressed sensing is introduced to obviate the interference. Then, the data with little interference can be reconstructed by reformulating the ANM as a semi-definite program. Meanwhile, the level of noise is quelled effectively in terms of the atomic norm soft-thresholding method and the gridless version of SPICE. Finally, the numerical simulation is performed to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, the authors consider the problem of direction of arrival (DOA) estimation in compressive sensing multiple-input and multiple-output (CS-MIMO) radars with co-prime receive arrays. A sparse aperture completion scheme is proposed to fill the 'holes' that in the difference co-array, achieving the full virtual receive aperture. Structured measurement matrices are devised in order that the output data of match filters can be seen as space–time compressed signal of a virtual uniform linear array. By employing the space–time recovery scheme, the sparse target scene can be accurately recovered while the amount of samples is further reduced. Numerical results demonstrate that the proposed scheme can achieve accurate DOA estimation with space–time compressed data and outperform the CS-based difference co-array methods. [ABSTRACT FROM AUTHOR]

Distributed compressive sensing (DCS) has been used in multiple-input multiple-output (MIMO) radar system. This application has led to substantial improvements over existing methods in MIMO radar. But there are also some challenges that should be resolved in order to benefit the most from DCS-based MIMO radar, such as radar signal with low signal to noise ratio and optimizing measurement matrix design. In distributed DCS-based MIMO radar context, this paper presents a cognitive mechanism for optimizing transmit and receive gain by applying the optimization guideline which based on the coherence of the sensing matrix (CSM) and signal-to-noise ratio. This paper proposed two kinds of method: the first one is to optimize transmit gain with the aim to maximize SNR, and the second one is to minimize CSM by adjusting receive gain. Simulations show that the proposed methods obtain significant better recovery performance than traditional DCS-based MIMO radar systems. [ABSTRACT FROM AUTHOR]

For radar imaging, a target usually has only a few strong scatterers which are sparsely distributed. In this paper, we propose a compressive sensing MIMO radar imaging algorithm based on smoothed l0 norm. An approximate hyperbolic tangent function is proposed as the smoothed function to measure the sparsity. A revised Newton method is used to solve the optimization problem by deriving the new revised Newton directions for the sequence of approximate hyperbolic tangent functions. In order to improve robustness of the imaging algorithm, main value weighted method is proposed. Simulation results show that the proposed algorithm is superior to Orthogonal Matching Pursuit (OMP), smoothed l0 method (SL0), and Bayesian method with Laplace prior in performance of sparse signal reconstruction. Two-dimensional image quality of MIMO radar using the new method has great improvement comparing with aforementioned reconstruction algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recently, the idea of compressed sensing (CS) has been used in radar system, and the concept of compressed sensing radar (CSR) has been proposed in which the target scene can be sparsely represented in the range-Doppler plane. With sufficiently incoherent transmission waveform, the target scene can be reconstructed by the technique of CS. With the idea that the transmission waveform can adapt in response to the operational information in cognitive radar system, we propose the notion of adaptive compressed sensing radar (ACSR) whose transmission waveform and sensing matrix can be updated by the target scene information fed back by the recovery algorithm. The methods for optimizing the transmission waveform and sensing matrix separately and simultaneously are both presented to decrease the cross correlations between different target responses. The principle for an ACSR system to synthesize the transmission waveform and sensing matrix matched to the target scene is also investigated. This novel ACSR system offers more degrees of freedom than classical radar system and better recovery performance than the CSR system. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

A new methodology for sparse signal acquisition using adaptive selective compressive sampling (ASCS) is presented. By employing the estimated prior information, the measurement matrix in the ASCS method can be adapted in order to selectively sense the sparse signal. The proposed ASCS method has an inherent characteristic of noise suppression, thus provides fewer noisy measurements. The experimental results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]