Earticle

Random demodulation based compressive sampling technique is a novel approach that it can break through the Shannon sampling theorem for the sparse signal capturing. A major challenge in the random demodulation based sampling system is the random sequence generation. In this paper, we introduce an approach to generate the high-speed random sequence that meets the incoherence of compressive sampling. The proposed technique employs a field programmable gate array (FPGA). First, the random sequence is parallel stored in the memory of FPGA, and it is read out byte by byte using a low speed clock. Second, the low-speed byte sequence is converted to a high-speed bit sequence by a circuitry. This proposed approach can program the random sequence dynamically without making any change to the circuitry system. Experimental results indicate that, the random sequence generated by the proposed approach is feasible to sensing the signal, and the constructed system can compressively sample and reconstruct the sparse signal.

Coastal surveillance and harbour defence are the most complex and challenging opera- tional issues for modern navy in the current turbulent global political climate. In most of the coastal surveillance and harbour defence systems, long sea-bed arrays consisting of hundreds of pressure sensors are deployed along the coastal belt to capture the low frequency compo- nents emanating from the sub-surface targets. Deployment of these sensor-arrays along with its associated signal conditioning hardware at the ocean-bed is a challenging task. The output of the sensor-array is to be conditioned and then digitized using multi-bit analog to digital converters (ADC). Further, the digitized channel data are required to be send to a base station through a radio frequency link. In this paper, we propose a compressively sampled (CS) architecture of acoustic vector sensor (AVS) array, to estimate the direction of arrival (DoA) of multiple acoustic sources, in a range independent shallow ocean using a one-dimensional search without prior knowledge of the ranges and the depths of the sources. We extend the high resolution angular spectral estimators MUSIC, MVDR and subspace in- tersection method (SIM) to suit the compressively sampled AVS array architecture operating in a shallow ocean environment. This architecture promises a signicant reduction in the number of sensors, analog signal conditioning hardware, data rate or bandwidth, the number of snapshots and the software complexity, leading to easy installation and maintenance.

In order to improve signal reconstruction accuracy, a CoSaMP (Compressive Sampling Matching Pursuit) algorithm based on peak signal to noise ratio is proposed in allusion to the disadvantages of CoSaMP algorithm. Firstly, the discrete cosine wave transform is improved to initially estimate the signal sparseness; secondly, the optimum iteration number is determined according to the peak signal to noise ratio to gradually approach to the real sparseness of the signal for signal reconstruction; finally, the simulation experiment is adopted to analyze the algorithm performance. The result shows: compared with CoSaMP algorithm and other improved CoSaMP algorithms, the proposed algorithm can not only obtain more ideal reconstruction effect, but also improve the reconstruction success probability and reduce the reconstruction time, thus having higher reconstruction efficiency.