Experimental Activity on Cooperative Mobile Positioning in Indoor Environments

Human-Induced Effects on RSS Ranging Measurements for Cooperative Positioning

“…For mass market location-based applications, the RSS is considered more easily available than the aforementioned parameters as it can be passively listened from the APs of the infrastructure (e.g., WLAN). Technically the APs periodically broadcast beacon frames containing information for network identification (e.g., SSID, BSSID, RSS, RSSI) [3][4][5][6][7]. For network management and connectivity quality purposes MSs can switch through different channels and store information from any received beacon.…”

“…Cooperative mobile positioning (CMP) [4] uses hybrid schemes and data-fusion filters to combine short-range (MSs-MSs) and long-range (BSs/APs-MSs) measurements ( Figure 2). The main concept is that "exploiting the most likely reliable short-range measurements coming from the neighboring mobile devices it is possible to enhance the location accuracy with respect to conventional techniques" [4,5]. In fact it has been demonstrated how the exploitation of spatial proximity estimated within a group of neighboring devices can enhance the location estimation accuracy [4,6] and it can be easily applied in case of (i) outdoor environments, by merging the measurements from hybrid technologies; (ii) indoor environments, by combining infrastructure and ad hoc mode WLAN communications signals; (iii) GPS-equipped mobiles, where the location estimation can be enhanced in challenging environments [4].…”

“…In fact it has been demonstrated how the exploitation of spatial proximity estimated within a group of neighboring devices can enhance the location estimation accuracy [4,6] and it can be easily applied in case of (i) outdoor environments, by merging the measurements from hybrid technologies; (ii) indoor environments, by combining infrastructure and ad hoc mode WLAN communications signals; (iii) GPS-equipped mobiles, where the location estimation can be enhanced in challenging environments [4]. The most promising approaches proposed in [4,5] adopt least squares (LS), non-linear-least-squares (NLLS) algorithms and Bayesian filters. While Bayesian filters could be a potential alternative to the previous ones [4], the nonlinear characteristics of measurements and positions make the common Kalman filter (KF) not suitable for solving the proposed problem.…”

“…Additionally, the indoor environment sets great, although interesting, challenges for location-based applications since its intrinsic complexity severely affects the accuracy of measurements, causing huge signal fluctuations [1,2]. Overlapping channels, shadowing, multipath, objects, and sensitivity variations of heterogeneous wireless cards make it difficult to perform accurate applications targeting positioning services [2][3][4][5][6].…”

“…The exploitation of the most reliable RSS measurements detected in the ad hoc links represent a valid and complementary approach to traditional noncooperative methods. The hybrid cooperative mobile positioning with signals of opportunity is deeply rooted in the exploitation of the most likely reliable short-range RSS measurements coming from neighbouring devices [4,5]. It also depends on the estimation of the distances among them (ranging), and processing data-fusion using nonlinear filtering, enhancing indeed the final position estimation with respect to conventional non-cooperative schemes [4].…”