TOA estimation with dynamic-threshold for IR-UWB system based on quantized compressed sensing

Abstract

Compressed sensing (CS) theory provides a new solution for the possibility of using impulse radio ultra-wideband (IR-UWB) for high-precision time of arrival (TOA) estimation. Because of the sparsity of IR-UWB signals, the CS theory enables the reconstruction of signals from a small set of random measurements at a sub-Nyquist rate. In current studies involving CS-based sampling architectures, the quantization process is usually idealized and the TOA estimation threshold is fixed. In this paper, the influence of quantization noise is fully considered, and a TOA estimation method for IR-UWB system with overloading quantization is proposed. Further, a dynamic-threshold setting model is proposed for the TOA estimation method based on the analysis of both thermal noise and quantization noise. Simulation results show that the proposed method can achieve sub-nanosecond TOA estimation accuracy under the quantized CS framework, and the effectiveness of the proposed dynamic-threshold setting model is verified.