|Abstract:||With smart environments, such as modern manufacturing facilities and smart homes, the demand for location-aware applications is swiftly increasing. Thus, wireless localization and tracking presently attracts high interest in research. Basically, radio frequency (RF)-based localization systems can be classified as active and passive, depending on the targeted entities, i.e. persons and/or objects, being equipped with an actively probing device or not . In active systems, the position of a target is determined based on an information exchange between a device and anchor nodes. In passive systems, the position is estimated by a wireless sensor network, not requiring a target to carry any device. Therefore, passive systems are also referred to as device-free localization. The capability to infer a target's position attributes to the physical impact of a target on the received RF-signals in the network through absorption, reflection, diffraction, or scattering. A prominent example of passive localization was introduced in , as radio tomographic imaging (RTI). This device-free localization approach solely relies on variations of the received signal strength (RSS) between individual links of a wireless network. An RTI system employs K transceiving communication nodes spanning a dense network of bidirectional links in line-of-sight (LoS). In each link, the RSS is mainly influenced by transmit power, distance dependent path loss, fading loss, and a shadowing loss induced by obstacles within the link. In this regard, targets are considered as time-variant obstacles which attenuate the RSS of various links according to their position. In order to localize a target based on RSS-measurements, the network area is initially sectioned in N discrete regions with known location. On the basis of this partitioning, an image vector is introduced which describes the attenuation of each respective region. This allows to express the shadowing loss of any individual link as a weighted sum of the attenuation values. The differences in RSS are measured for every link resulting in a measurement vector, which depends on the position of the target. The measurement equations form a linear system with a weighting matrix and a noise vector incorporating fading and measurement noise. Thus, the image vector can be estimated, e.g., using a least-squares solution with Tikhonov regularization as proposed in . Since the locations of the N regions corresponding to the image vector are known, the position of the attenuating obstacles can be directly inferred from the estimated image vector. As shown in , the performance of an RTI system is mainly influenced by the node density. Particularly, an increasing number of nodes leads to an increasing localization accuracy. This can be intuitively explained by the coverage of the network area with LoS-links. The lack of information in areas, where no link-related RSS measurements can be retrieved results in a poor localization performance. Motivated by the resource consuming deployment of communication infrastructure required in RTI systems, the approach proposed in this document attempts to downsize the amount of nodes. In comparison to , only relying on RSS measurements between LoS-links, our approach bases on an additional exploitation of propagation characteristics. To gather this additional information, the measured channel impulse response (CIR) is processed at each node. Therewith, the proposed passive localization approach can be divided in two stages: 1. In an initial calibration phase, the additional information of the propagation channel is precisely estimated from all measured CIRs. 2. The second stage applies commonly known RTI processing, as e.g. described in , now including the additional channel characteristics, to determine the position of targets within the network. Due to the usage of additional information of the propagation channel, the proposed passive localization approach improves common RTI methods. Particularly, the approach addresses the problem of link coverage within an observation area. As link coverage correlates to localization quality, the supplemental integration of propagation characteristics results in either an increased localization performance for an unchanged amount of nodes, or allows to reduce the amount of required nodes to achieve a targeted localization performance. The algorithm is evaluated in an indoor scenario using five ultrawide-band (UWB) devices. Thereby, one UWB node is acting as transceiver, pinging four relay nodes in a round robin manner. The nodes are stationary mounted, spatially separated at different locations within the observation area. Thus, this setup spans a network of four direct links incorporating additional channel features, which can be used for the positioning of passive targets. For ground truth, the passive target is tracked with a Vicon high-precision motion tracking system. Using the proposed approach, it is shown for this setup, that the location of a moving person can be estimated and tracked, successfully. The positioning error is shown to be smaller than 1m. References  G. Deak, K. Curran, and J. Condell, "A survey of active and passive indoor localization systems," Computer Communications, vol. 35, no. 16, pp. 1939-1954, 2012.  J. Wilson and N. Patwari, "Radio tomographic imaging with wireless networks," IEEE Transactions on Mobile Computing, vol. 9, no. 5, pp. 621-632, May 2010.|
Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018)
September 24 - 28, 2018
Hyatt Regency Miami
|Pages:||653 - 665|
|Cite this article:||
Schmidhammer, Martin, Gentner, Christian, "A Novel Approach of Passive Localization for Indoor Positioning," Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018), Miami, Florida, September 2018, pp. 653-665.
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