Cooperative Multipath-Assisted Navigation and Tracking: A Low-Complexity Approach

Abstract

Cooperative agent networks are a common technology for localization nowadays, nevertheless it is still illusive to reach a certain level of accuracy and robustness for wireless applications, especially in indoor environments which are characterized by harsh multipath condition. In this work we introduce a cooperative low complexity algorithm that utilizes multipath components for localization that are seen as impairment in other respects. The algorithm uses two types of measurements: (i) bistatic measurements between agents and (ii) monostatic (bat-like) measurements by the individual agents. Simulations that use data generated from a realistic channel model, show the applicability of the methodology and the high level of accuracy that can be reached.

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.…”

“… Multidimensional Scaling C W/I E PA+R [ 115 ] UWB UWB TDoA RSSI Coop. Algorithm EKF C I-L S PA+PP 2015 [ 150 ] UWB UWB RSSI Multiphath C. Ranging EKF N/S N/S S PA+CC+R [ 114 ] Wi-Fi Wi-Fi F. printing RSSI KNN Self-organized map D I-L S PA [ 42 ] Wi-Fi Wi-Fi, Bluetooth RSSI RSSI Ranging Trilateration …”

High-accuracy positioning for indoor applications: RFID, UWB, 5G, and beyond

“…The agents localize themselves utilizing the multipath propagation between an agent and anchors. A promising way to further decrease the dependence on infrastructure is cooperation of the agents [44], [45]. The agents share the belief about their position with their neighbors and also perform cooperative measurements.…”