LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting

Abstract

This paper presents LocSpeck, a collaborative and distributed indoor positioning system for dynamic nodes connected using an ad-hoc network, based on inter-node relative range measurements and Wi-Fi fingerprinting. The proposed system operates using peer-to-peer range measurements and does not need ultra-wideband (UWB) fixed anchor, nor it needs a predefined network topology. The nodes could be asymmetric in terms of the available sensors onboard, the computational resources, and the power capacity. This asymmetry adversely affects the positioning performance of the weaker nodes. Collaboration between different nodes is achieved through a distributed estimator without the need of a single centralized computing element. The ranging measurement component of the system is based on the DW1000 UWB transceiver chip from Decawave, which is attached to a set of smartphones equipped with asymmetric sensors. The distributed positioning filter fuses, locally on each node, the relative range measurements, the reading from the internal sensors, and the Wi-Fi received signal strength indicator (RSSI) readings to obtain an estimate of the position of each node. The described system does not depend on fixed UWB anchors and supports online addition and removal of nodes and dynamic node role assignment, either as an anchor or as a rover. The performance of the system is evaluated by real-world test scenarios using a set of four smartphones navigating an indoor environment on foot. The performance is compared to that of a commercial UWB-based system. The results presented in this paper show that weak mobile nodes, in terms of available positioning sensors, can benefit from collaboration with other nearby nodes.Sensors 2020, 20, 78 2 of 28 using relative measurements can augment the stand-alone observations of each node and improve the positioning accuracy of the ensemble [2,3]. The use of relative range measurements introduces an additional constraint to the position estimation filter, which can improve the positioning accuracy of the collaborating nodes [4]. Ultra-wideband (UWB) ranging devices are more immune to multipath errors because it can distinguish between different events with a precise temporal resolution, thanks to its large channel bandwidth. This makes the UWB-based devices capable of achieving centimeter-level ranging accuracy in ideal operating conditions [5].The objective of this paper is to present LocSpeck, a collaborative and distributed positioning system targeting smartphones and handheld applications, which uses UWB-based relative range measurements, along with Wi-Fi fingerprinting and inertial sensors. The system is evaluated experimentally, and the results are compared to the performance of Pozyx, a commercial UWB-based positioning system. The rest of this section will provide a brief overview of different ranging and positioning techniques used in UWB-based systems, and then it will discuss the different network architectures used for range-based positioning applications. UWB-Based RangingSensors 2020, 20,...

Collaborative Indoor Positioning Systems: A Systematic Review

“…Regarding stand-alone positioning in the non-collaborative phase, the most used methods were PDR [ 43 , 63 , 92 , 97 , 98 , 100 , 101 , 102 , 106 , 107 , 110 , 117 , 118 , 120 , 123 , 127 , 128 , 129 , 131 , 141 , 144 , 145 , 146 , 147 ], Ranging [ 42 , 46 , 47 , 81 , 93 , 109 , 111 , 113 , 130 , 132 , 144 , 150 ], RSS-based [ 40 , 41 , 43 , 48 , 77 , 84 , 89 , 103 , 123 , 129 ], k -NN [ 63 , 79 , 108 , 114 , 118 , 119 ,…”

“…Another remarkable finding in the non-collaborative part is the lack of details of some key aspects of the CIPSs. Around a third of the reviewed papers did not provide enough details about the method used to provide the position estimate, only being cataloged as fingerprint-based [ 42 , 83 , 98 , 100 , 102 , 143 , 147 , 149 ], ranging [ 42 , 46 , 47 , 81 , 93 , 109 , 111 , 113 , 130 , 132 , 144 , 150 ] and RSS-based [ 40 , 41 , 43 , 48 , 77 , 84 , 89 , 103 , 123 , 129 ] methods. In those works, the authors considered the user’s positioning method in the non-collaborative part irrelevant, i.e., the main focus of the CIPS was to improve the user’s position in the collaborative part, regardless of the approach used in the non-collaborative part.…”

“…As with the non-collaborative part, the most used collaborative methods present advantages and drawbacks. The six most used methods were Particle Filter [ 83 , 89 , 93 , 98 , 102 , 117 , 118 , 120 , 123 , 126 , 129 , 136 , 146 , 147 , 149 , 149 ]; Belief Propagation [ 46 , 47 , 96 , 109 , 130 , 131 , 132 , 135 ]; EKF [ 106 , 107 , 115 , 125 , 140 , 141 , 150 ]; Geometric Algorithm [ 43 , 124 , 127 , 134 , 142 , 143 ]; LS [ 45 , 49 , 133 , 137 , 148 ]; Trilateration [ 40 , 41 , 42 , 94 ]. One of the main advantages of the methods based on Particle Filter is their capability of handling non-Gaussian and non-linear estimations; however, their computational complexity increases (increment of the number of particles) as the position accuracy increases.…”

“…M. EKF D I-L E PA [ 146 ] IMU Wi-Fi, UWB DR RSSI PDR-B. M. P. Filter D I-L S+E PA+CC [ 147 ] IMU, Wi-Fi UWB DR, F. printing TWR PDR-B. M., F. printing-B P.Filter D I-L E PA [ 148 ] 5G 5G RSSI RSSI Coop.…”

Indoor Navigation and Mapping: Performance Analysis of Uwb-Based Platform Positioning

“…Given the low cost of certain UWB transceivers, e.g. Decawave, the next generation of smartphones might be provided with such technology, opening the possibility to the development of a positioning strategy based on UWB relative range measurements between moving devices, reducing the need for an external UWB infrastructure (Sakr et al, 2020).…”