Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact on the transmission of channel state information (CSI) of Wi-Fi signals. Because space is divided into several small regions, the idea of classification is used to locate. Therefore, a novel localization algorithm is put forward in this paper based on Deep Neural Networks (DNN) and a multi-model integration strategy. The approach consists of three stages. First, the local outlier factor (LOF), the anomaly detection algorithm, is used to correct the abnormal data. Second, in the training phase, 3 DNN models are trained to classify the region fingerprints by taking advantage of the processed CSI data from 3 antennas. Third, in the testing phase, a model fusion method named group method of data handling (GMDH) is adopted to integrate 3 predicted results of multiple models and give the final position result. The test-bed experiment was conducted in an empty corridor, and final positioning accuracy reached at least 97%.
The prevalence of illumination equipment and the inherent advantages of the Visible Light Communication (VLC) technique have resulted in a growing interest in Visible Light Positioning (VLP). There exist many excellent VLP techniques over the past several years. However, one limitation of most VLP survey works is that they mainly focus on the analysis from the perspective of techniques but ignore the equally important hardware aspect, since the hardware part directly affects the performance and cost of VLP systems and also determines whether it can be put into practical use. Different from most surveys concentrating on a single perspective, we provide an intensive overview of VLP systems from software algorithms to hardware devices. A novel-innovative classification method is used in the software algorithms, while the hardware aspect is introduced in terms of transmitters, modems, and receivers, making up for the deficiencies of the previous works. Massive papers including pioneering papers and the state-of-the-art ones in related areas are gathered and categorized. These solutions have also been evaluated in terms of accuracy, cost, range, and complexity. Furthermore, current open issues and tendencies regarding VLP are also illustrated in this paper.
High-resolution ground-penetrating radar multiviewmultistatic diffraction-tomographic (DT) imaging usually requires the wide signal bandwidth and large antenna aperture, which results in the great amount of imaging data. To solve the aforementioned problem, an innovative 2-D multiview-multistatic DT imaging algorithm based on the multitask Bayesian compressive sensing (MT-BCS) strategy is proposed in this letter. The reduction of the measurement data can be achieved by performing a reduced set of measurements in the frequency domain. In particular, a joint Bayesian sparse reconstruction scheme is used to recover the original frequency domain data from the reduced frequency measurements across all the measurement positions. Finally, the image of the investigation domain can be reconstructed by the traditional multiview-multistatic DT imaging algorithm. Numerical simulation results have shown that the proposed imaging method can not only reduce the frequency measurement data but also provide the satisfactory quality of the reconstructed image.
Wireless sensing is an exciting new research area which enables a large variety of applications ranging from coarse-grained daily activity recognition to fine-grained vital sign monitoring. While promising in many aspects, one critical issue is the limited sensing range because weak reflection signals are used for sensing. Recently, LoRa signals are exploited for wireless sensing, moving a big step towards long-range sensing. Although promising, there is still a huge room for improvement. In this work, we qualitatively characterize the relationship between target movements and target-induced signal variations, and propose signal processing methods to enlarge the induced signal variation to achieve a longer sensing range. Experiment results show that the proposed system (1) pushes the contact-free sensing range of human walking from the state-of-the-art 50 m to 120 m; (2) achieves a sensing range of 75 m for fine-grained respiration sensing; and (3) demonstrates human respiration sensing even through seven concrete walls.
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