Feature-Based Sensing Matrix Design for Analog to Information Converters

In this paper, we propose a novel sensing matrix design for the pulse-width modulation (PWM)-based analog-to-information converter (AIC), which obtains the digital feature of an analog signal rather than its sparse coefficients. The method firstly selects feature subsets by feature selection algorithm of support vector machines (SVMs) and then establishes the relationship between feature subsets and the vector of sensing matrix of PWM-based AIC. Then, a sensing matrix with a higher compression ratio can be obtained. The new optimized sensing matrix is mapped to the reference modulation sequence of the PWM-based AIC’s modulation signal to obtain the PWM-based analog-to-feature converter (AFC). Experimental results show that the PWM-based AFC can reach 99.40% accuracy even when the compression ratio is higher than that of other literature.