5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters (delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods break down or lead to significant biases. We present a novel tensor-based method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach.

A diffusion least‐mean P‐power (LMP) algorithm is proposed for distributed estimation in alpha‐stable noise environments, which is one of the widely used models that appears in various environments. Compared with the diffusion least‐mean squares algorithm, better performance is obtained for the diffusion LMP methods when the noise is with alpha‐stable distribution.

5G communication systems operating above 24 GHz have promising properties for user localization and environment mapping. Existing studies have either relied on simplified abstract models of the signal propagation and the measurements, or are based on direct positioning approaches, which directly map the received waveform to a position. In this study, we consider an intermediate approach, which consists of four phases—downlink data transmission, multi-dimensional channel estimation, channel parameter clustering, and simultaneous localization and mapping (SLAM) based on a novel likelihood function. This approach can decompose the problem into simpler steps, thus leading to lower complexity. At the same time, by considering an end-to-end processing chain, we are accounting for a wide variety of practical impairments. Simulation results demonstrate the efficacy of the proposed approach.

We design a impulse radio ultrawideband radar monitoring system to track the chest wall movement of a human subject during respiration. Multiple sensors are placed at different locations to ensure that the backscattered signal could be detected by at least one sensor no matter which direction the human subject faces. We design a hidden Markov model to infer the subject facing direction and his or her chest movement. We compare the performance of our proposed scheme on 15 human volunteers with the medical gold standard using respiratory inductive plethysmography (RIP) belts, and show that on average, our estimation is over 81% correlated with the measurements of a RIP belt system. Furthermore, in order to automatically differentiate between periods of normal and abnormal breathing patterns, we develop a change point detection algorithm based on perfect simulation techniques to detect changes in the subject's breathing. The feasibility of our proposed system is verified by both the simulation and experiment results.