Research On Analog-to-information Conversion Technology Based On Modulated Wideband Converter

Analog to information converter technology directly samples and compresses at the same time which breaks through the Nyquist Sampling limitations. Frequency-hopping communication technology has been widely used in the field of military communications, with the development of communication, the signal bandwidth become wider and wider, the traditional signal processing equipment has encountered great difficulties. In this paper, analog to information converter technology is deployed to do compressed sampling and recovery for the frequency hopping communication signal.For random demodulation scheme, we mainly introduce the construction of the observation matrix, and propose an over-complete dictionary trained by K-SVD algorithm as the sparse base of the frequency hopping communication signal. The performance of the proposed scheme is evaluated by the output signal-to-noise ratio, the reconstruction success rate and the time complexity. Experiments show that the proposed scheme possess good reconstruction performance and stability.As for the compressed sampling system based on MWC, we mainly introduce the design of the system, analyze the deficiency of known sparsity for current reconstruction algorithm,do some optimization and propose a multiple dimensional iterative support detection algorithm. The performance of the proposed scheme is evaluated by the bit error rate and the reconstruction success rate. Experiments show that the MWC-based signal processing systems and support set detection algorithm possess better performance.In this paper, a signal processing platform based on analog information converter technology is proposed. A dictionary optimizing learning algorithm is proposed to build the sparse base and the support set detection algorithm is optimized to break through the limitations of existing algorithms and provides a new method for the compressed sampling and reconstruction of wideband signal based on MWC.