Cooperative indoor positioning by exchange of bluetooth signals and state estimates between users

Abstract

This paper presents a Bayesian indoor positioning system for smartphones based on the strengths of WiFi and Bluetooth signals. A framework for improving the performance of existing positioning methods with the help information sharing between users is proposed and evaluated. Bluetooth signals are sent between users, and the signal strengths contain information about their relative distances, which is used to evaluate the probability distribution functions of their states. A particle filter is used for the state estimation, together with an unscented transform to propagate probability distributions through nonlinearities.

Collaborative Indoor Positioning Systems: A Systematic Review

“…A majority of research focuses on positioning scenarios in existing buildings, some of them re-using already existing infrastructure (i.e., signals of opportunity). Typical scenarios that are receiving a growing interest involve locating people within houses, offices [ 63 , 115 , 123 , 131 ], and universities [ 40 , 43 , 63 , 79 , 98 , 101 , 119 , 120 , 128 , 131 ], where the viability of installing complex infrastructure is low in terms of costs, unlike industrial or warehouse scenarios, which are capable of developing and deploying robust and costly infrastructures designed for positioning. As a result, our review indeed shows that infrastructure-less approaches are predominantly selected for CIPSs in research (see Figure 4 c), and that the majority of technologies used (see Figure 5 and Figure 6 ) are already present in the environment.…”

“…The results provided in Section 4.3 showed that in 66 articles, positioning was done in two steps: each node or actor used an indoor positioning method (based on one or multiple positioning technologies) to get an initial estimation with only its collected data [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 63 , 77 , 79 , 80 , 81 , 83 , 84 , 86 , 87 , 89 , 91 , 92 , 93 , 94 , 97 , 98 , 100 , 101 , 102 , 103 , 104 , 106 , 107 , 108 , 109 , 110 , 111 , 113 , 114 , 116 , 117 , 118 , 119 , 120 , 123 , 124 , 125 , 127 , 128 , …”

“…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 ,…”

“…As expected, most of collaborative works try to exploit widely used low-cost and simple IPSs with known issues, in order to improve them by means of collaboration. In fact, only a few CIPSs combined two or more positioning technologies in the non-collaborative [ 43 , 63 , 98 , 100 , 102 , 118 , 123 , 129 ] and in the collaborative [ 42 , 43 , 118 , 131 ] parts, although it is common in regular IPS. Just in one paper [ 43 ], multiple technologies—Wi-Fi+IMU in the non-collaborative part and Wi-Fi+Bluetooth in the collaborative part—were combined in both parts.…”

Improving RF Fingerprinting Methods by Means of D2D Communication Protocol

“…Karlsson et al [32] showed smartphone based Wi-Fi signal measurements exchange via Bluetooth connection between the users. They used Bluetooth signal strength difference between users to calculate relative distance, which is used to evaluate the probability distribution functions of their states.…”