|Abstract:||This paper presents a novel algorithm that aims at combining the pre-existing and dominating applications of radio signals, namely communication, navigation and sensing of an environment into an integrated approach. Practical applications of this algorithm include navigation in GNSS-denied environments and include the generation of geometric representations of surroundings based on signals implemented in current and future mobile cellular radio communication systems and devices. We show that the power and spectral properties of such signals allow us, in addition to estimating the position of a receiver, to infer valuable geometric information on the locations of reflectors and scatterers. We show how we represent this geometric information in the form of metric maps and how these maps relate to other geometric representations such as floor plans or CAD-models of indoor or urban environments. In this paper, we consider a positioning approach using wireless signals. In wireless propagation the transmitted signal is reflected and scattered by objects. Especially in indoor or urban scenarios, the signal reaching the receive antenna consists of multiple paths, called multipath. Multipath reception degrades the accuracy of the positioning device as long as the receiver is based on standard methods. Strategies to mitigate multipath effects on the ranging estimate are in general based on the estimation of the channel impulse response. To extract the range information the first arrived path is treated as the line-of-sight path. All these methods have in common that they determine the multipath in order to remove the influence on the estimate of the line-of-sight path. With Channel-SLAM we introduced a novel algorithm which uses multipath components for positioning instead of mitigating them. Channel sounder measurements with a moving receive antenna showed that some multipath components have a path life of several meters. Hence, the algorithm uses a SAGE Kalman filter to estimate and track the time-variant multipath components of the received signal. Channel-SLAM treats each multipath component as a line-of-sight signal from a virtual transmitter which position is unknown to the receiver. Therefore, the algorithm estimates the position of the virtual transmitters as well as the receiver position. This work builds on and extends the previous work on Channel-SLAM. Channel-SLAM is an algorithm that uses multipath components from terrestrial transmitters for positioning instead of mitigating them. Measurements with a moving receive antenna showed that some multipath components have a path life of several meters of receiver movement. These long visible paths can be used by Channel-SLAM for positioning. Channel-SLAM treats each multipath component as a line-of-sight signal from a virtual transmitter whose position is unknown to the receiver but static during the movement of the receiver. Channel-SLAM basically uses a two level approach: The first level uses a Space-Alternating Generalized Expectation-Maximization (SAGE) based Kalman filter to estimate and track the amplitude and the delay of each multipath component. Afterwards, the second level estimates simultaneously the positions of the receiver and the virtual transmitters based on the estimated parameters of the multipath components using a simultaneous localization and mapping (SLAM) approach. So the algorithm does not require any prior information such as room-layout or a database for fingerprinting. The only conditions to be fulfilled are the presence of a multipath environment, a moving receiver as well as prior knowledge of the initial receiver states, i.e. position and moving direction. In this paper, we extend Channel-SLAM to exploit the geometry information of the surrounding area. Hence, this paper shows that the power and spectral properties of the multipath signals allow us, in addition to estimating the position of a receiver, to infer valuable geometric information on the locations of reflectors and scatterers. The new approach does not rely on any prior information such as the room layout or information collected in a database for finger printing. Furthermore, mapping the geometry layout helps to improve the position estimation. Additionally, the algorithm enables to obtain geometric information of all kinds of environments by using wireless signals implemented in current and future mobile cellular radio communication systems and devices. To verify the proposed algorithm, we evaluate the algorithm based on measurements using an ultra-wideband (UWB) system. We use the DecaWave’s DW1000 UWB transceiver which enables cost effective real-time positioning with high accuracy in the order of 10cm in indoor and outdoor scenarios. The measurements are carried out inside and outside of an office building where we placed UWB anchors on different locations. We evaluated the proposed algorithm based on measurements with a moving pedestrian and fixed anchors with unknown positions. The moving pedestrian carried a system consisting of an UWB tag and an IMU. The UWB tag measures the distance between the anchors and provides additionally the channel impulse response for each link (anchor to receiver). Additionally, we use an IMU to obtain heading changes of the moving pedestrian to improve the performance of Channel-SLAM by resolving ambiguities. Based on the measured channel impulse responses, the multipath components are estimated for each individual link. We show that several multipath components can be tracked for a sufficient amount of time to allow estimating the surrounding environment. The evaluations show that the obtained geometric information in the form of metric maps fits perfectly the floor plans or CAD-model of the considered environment. Furthermore, estimating the geometry information of the environment, the algorithm is able to estimate the receiver position more accurately. To summarize, this paper shows a positioning approach using the multipath components of the received signal as an additional position information source. Each multipath component is treated as a line-of-sight signal originated from a virtual transmitter, which location is unknown. The novel algorithm does not rely on any prior information such as the room layout or a database for fingerprinting. In order to use multipath components for positioning, the positions of the virtual transmitters and the receiver are estimated simultaneously. Moreover, the multipath components allow us, in addition to estimate the position of a receiver, to infer valuable geometric information on the locations of reflectors and scatterers. Furthermore, we show how we represent this geometric information in the form of metric maps and how these maps relate to other geometric representations such as floor plans or CAD-models of environments.|
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:||635 - 652|
|Cite this article:||
Gentner, Christian, Ulmschneider, Markus, Jost, Thomas, Karásek, Rostislav, "Simultaneous Localization of a Receiver and Mapping of Multipath Generating Geometry in Indoor Environments," Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018), Miami, Florida, September 2018, pp. 635-652.
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