A dual-channel fully parallel shifting-based WVD architecture for real-time processing applications

Abstract

The Wigner-Ville distribution (WVD) is a widely used time-frequency analysis (TFA) technique in non-stationary signal processing. However, as signal bandwidths increase, the low throughput of traditional serial architectures hinders real-time WVD processing. In this paper, we propose a novel dual-channel, fully parallel WVD architecture based on data shifting method. We comprehensively tap into the parallel potential in the WVD algorithm and present the corresponding data flow graph. By utilizing a symmetric dual-channel structure, we significantly streamline the generation and combination of autocorrelation sequences, while halving the total number of complex multiplications and FFTs. Additionally, we develop a memory-based, three-stage autocorrelation generator using a data-shifting technique, thereby obviating the necessity for address generation stages. Implementation results show that our design achieves a 95% latency reduction compared to the state-of-the-art WVD architectures, attaining a maximum clock frequency of 322 MHz on a Xilinx UltraScale+ FPGA. The proposed architecture holds substantial potential for augmenting the real-time processing of wide-band signals.