Localization in Wireless networks via Laser scanning and Bayesian compressed sensing

Abstract

WiFi indoor localization has seen a renaissance with the introduction of RSSI-based approaches. However, manual fingerprinting techniques that split the indoor environment into predefined grids are implicitly bounding the maximum achievable localization accuracy. WoLF, our proposed Wireless localization and Laser-scanner assisted Fingerprinting system, solves this problem by automating the way indoor fingerprint maps are generated. We furthermore show that WiFi localization on the generated high resolution maps can be performed by sparse reconstruction which exploits the peculiarities imposed by the physical characteristics of indoor environments. Particularly, we propose a Bayesian Compressed Sensing (BCS) approach in order to find the position of the mobile user and dynamically determine the sufficient number of APs required for accurate positioning. BCS employs a Bayesian formalism in order to reconstruct a sparse signal using an undetermined system of equations. Experimental results with data collected in a university building validate WoLF in terms of localization accuracy under actual environmental conditions.

Sparsity-Aware Multi-Source TDOA Localization

“…2. Now, assume that is located on (8,6) as shown in the figure. The set of points that represents a constant TDOA w.r.t.…”

“…This is because if they could cross, then at the crossing point we would have and considering (8) this would mean that the two hyperbolas should coincide everywhere and thus and would be located on the same hyperbola. This is impossible according to our grid design.…”

“…The recovered is depicted in Fig. 10(a) and as can be seen, the main peaks correspond to the closest GPs to the sources, i.e., located on (4,3), (4,8) and (10,3). In the second step, knowing the closest GPs, we compute and estimate the mismatch.…”

Multiple target localization in WSNs using compressed sensing reconstruction based on ABC algorithm

“…A grid-based WSNs localization problem is modeled as N-dimensional reconfiguration when K sparsity vectors are 1 [10]; compressed sensing is used to achieve multi targeting in a wireless sensor network; the shortcomings of this approach is that WSN data can't be further compressed, which is, to M sensor nodes under the situation of K targets, M×K perception data need to be transferred. In recent years, among some works [11] [12] [13] [14], target location has been studied via the model and solving method with compressed sensing respectively, and has made some progress. This paper presents a reconstruction method of compression perception based on discrete artificial bees colony algorithm to localize multi-target in wireless sensor network.…”

Block-sparsity-based localization in wireless sensor networks

“…This issue has been recently tackled by recasting localization into a sparse approximation problem [9][10][11]. The rationale is the following: assuming that the physical space is discretized onto a grid of D cells, each of them associated with a prescribed position, the device position can be represented by a vector of length D that has non-zero entries only where a device occupies that position.…”