Among the various Ultra-wideband (UWB) ranging methods, the absence of uplink
communication or centralized computation makes downlink
time-difference-of-arrival (DL-TDOA) localization the most suitable for
large-scale industrial deployments. However, temporary or permanent obstacles
in the deployment region often lead to non-line-of-sight (NLOS) channel path
and signal outage effects, which result in localization errors. Prior research
has addressed this problem by increasing the ranging frequency, which leads to
a heavy increase in the user device power consumption. It also does not
contribute to any increase in localization accuracy under line-of-sight (LOS)
conditions. In this paper, we propose and implement a novel low-power
channel-aware dynamic frequency DL-TDOA ranging algorithm. It comprises NLOS
probability predictor based on a convolutional neural network (CNN), a dynamic
ranging frequency control module, and an IMU sensor-based ranging filter. Based
on the conducted experiments, we show that the proposed algorithm achieves 50%
higher accuracy in NLOS conditions while having 46% lower power consumption in
LOS conditions compared to baseline methods from prior research.

本文提出并实施了一种新颖的低功耗、适应信道特性的动态频率下行到达差异时间测距算法，通过基于卷积神经网络（CNN）的 NLOS 概率预测器、动态测距频率控制模块和基于 IMU 传感器的测距滤波器，实验证明该算法在 NLOS 条件下的准确度比先前研究的基准方法提高了 50%，在 LOS 条件下的功耗降低了 46%。

自适应信道感知超宽带 DL-TDOA 的功耗效率室内定位

Power-Efficient Indoor Localization Using Adaptive Channel-aware  Ultra-wideband DL-TDOA

Non-line-of-sight (NLOS) imaging techniques use light that diffusely reflects
off of visible surfaces (e.g., walls) to see around corners. One approach
involves using pulsed lasers and ultrafast sensors to measure the travel time
of multiply scattered light. Unlike existing NLOS techniques that generally
require densely raster scanning points across the entirety of a relay wall, we
explore a more efficient form of NLOS scanning that reduces both acquisition
times and computational requirements. We propose a circular and confocal
non-line-of-sight (C2NLOS) scan that involves illuminating and imaging a common
point, and scanning this point in a circular path along a wall. We observe that
(1) these C2NLOS measurements consist of a superposition of sinusoids, which we
refer to as a transient sinogram, (2) there exists computationally efficient
reconstruction procedures that transform these sinusoidal measurements into 3D
positions of hidden scatterers or NLOS images of hidden objects, and (3)
despite operating on an order of magnitude fewer measurements than previous
approaches, these C2NLOS scans provide sufficient information about the hidden
scene to solve these different NLOS imaging tasks. We show results from both
simulated and real C2NLOS scans.

本文介绍了一种名为 Circular and Confocal Non-line-of-sight (C2NLOS) 扫描的非直线视野成像技术，它使用激光和超快速感应器来测量多次散射光的行程时间，以了解隐藏的场景。与现有的 NLOS 技术相比，这种技术可以更高效的扫描目标，同时提高成像的质量和效率。