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Non-line-of-sight (NLOS) target detection utilizing multipath plays an important role in anti-terrorism, urban warfare, indoor rescue and intelligent driving. In this paper, an imaging method based on single-channel ultra-wideband (UWB) radar and dual-view observation is proposed for detecting NLOS targets. First, the cause of producing ghosts from a single view in a previous study is analyzed in detail. On this basis, with focusing on dual-view observation, the ghost problem is handled at two levels. From the level of the radar echo, path partition is conducted with the utilization of dual-view observation, solving the problem of uneven energy among multipaths. After that, two images are generated by multipath imaging, respectively, which reduces the accumulation of ghosts. From the level of the image, owing to the distinct distribution of ghosts between the imaging result, the ghosts at different locations are eliminated by image fusion. Experimental results demonstrate that compared with single view and direct multipath imaging, the approach based on dual-view observation successfully eliminates most ghosts while retaining the targets in both single-target and double-targets scenarios, which verifies the effectiveness of the method. [ABSTRACT FROM AUTHOR]

The Ultra-Wide Bandwidth (UWB) signal has received much attention due to its penetrability and high positioning accuracy with the increasing demand for indoor positioning, in which Global Positioning System (GPS) signal is challenging. In practice, there are two main problems with indoor 3D positioning based on UWB. One is that the quality of Time Difference of Arrival (TDOA) measurements varies in different observation environments. Namely, the time delay generated by Non-Line-of-Sight (NLOS) causes an enormous deviation from the real distance and cannot be well distinguished from the measurement reducing the accuracy of positioning. The other problem is that the height estimates, which are calculated using the conventional least square method, are extremely unstable due to the limitation of the Base Station (BS) layout. To address these problems, this paper presents Robust Ridge Estimation (RRE) for UWB positioning. Firstly, NLOS errors are detected, and the weights of each measurement are automatically adjusted in accordance with their quality, which is represented by the residuals between the estimated measurements and real observations. Then, the ridge estimation algorithm is applied iteratively for position estimation based on a robust estimation framework, which updates the weight of the measurements at each iteration. This approach transforms unbiased estimation to biased estimation by adding constraints that minimize the weighted quadratic sum of some parameters. As a result, the impact of NLOS can be reduced. The experimental result shows an improvement of RMSE in positioning with 45.71% when compared with ridge estimation in an NLOS/Line-of-Sight (LOS) mixed environment and an increase of robustness to NLOS with 56.11%. [ABSTRACT FROM AUTHOR]

Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will face critical errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques applied to a previous classification and mitigation of the propagation effects. For this purpose, real-world cross-scenarios are considered, where the data extracted from low-cost UWB devices for training the algorithms come from a scenario different from that considered for the test. The experimental results reveal that machine learning (ML) techniques are suitable for detecting non-line-of-sight (NLOS) ranging values in this situation. [ABSTRACT FROM AUTHOR]

Summary: Among existing wireless technologies, ultra‐wideband (UWB) is the most promising solution for indoor location tracking. UWB has a great multipath fading immunity; however, great multipath resolvability alone does not eliminate the effect of non‐line‐of‐sight (NLOS) and multipath propagation. NLOS and multipath propagation in indoor environments can easily produce meters of UWB ranging error. This condition gives an enormous impact on the accuracy of indoor location tracking data. To address this problem, we propose an NLOS detection method using recursive decision tree learning. Using the UWB channel quality indicators information, we develop our model with the Gini index and altered priors splitting criteria. We then validate the constructed model using the 10‐fold cross‐validation method. Our experiment shows that the constructed model has correctly detected 90% of both line‐of‐sight (LOS) and NLOS cases on the seven different indoor environments. The result of this work can be used for the UWB indoor location tracking accuracy improvement. [ABSTRACT FROM AUTHOR]