Global navigation satellite systems (GNSSs) can deliver very good position estimates under optimum conditions. However, especially in urban and indoor scenarios with severe multipath propagation and blocking of satellites by buildings the accuracy loss can be very large. Using WiFi for indoor positioning is a common approach because WiFi infrastructure is widely deployed. Recently the WiFi IEEE 802.11-2016 standard was released, which includes a fine timing measurement (FTM) protocol, more commonly known as WiFi-round-trip-time (WiFi-RTT) protocol, for WiFi ranging. This paper researches timing based positioning algorithms, in this case using WiFi-RTT distance estimates. Based on two measurement campaigns, in an antenna measurement chamber and in a typical indoor environment, a WiFi-RTT distance error model is derived. Both measurement campaigns show, that the distance is underestimated, hence, the estimated distance is lower than the true distance. The WiFi-RTT distance error model is included in the likelihood function of a particle filter (PF) and the positioning performances is evaluated in an indoor scenario. These evaluations show clearly the possibility of using WiFi-RTT distance estimates for indoor positioning.
Increasing the autonomy of multi-agent systems or swarms for exploration missions requires tools for efficient information gathering. This work studies this problem from theoretical and experimental perspectives and evaluates an exploration system for multiple ground robots that cooperatively explore a stationary spatial process. For the distributed model, two conceptually different distribution paradigms are considered. The exploration is based on fusing distributively gathered information using Sparse Bayesian Learning (SBL), which permits representing the spatial process in a compressed manner and thus reduces the model complexity and communication load required for the exploration. An entropy-based exploration criterion is formulated to guide the agents. This criterion uses an estimation of a covariance matrix of the model parameters, which is then quantitatively characterized using a D-optimality criterion. The new sampling locations for the agents are then selected to minimize this criterion. To this end, a distributed optimization of the D-optimality criterion is derived. The proposed entropy-driven exploration is then presented from a system perspective and validated in laboratory experiments with two ground robots. The experiments show that SBL together with the distributed entropy-driven exploration is real-time capable and leads to a better performance with respect to time and accuracy compared with similar state-of-the-art algorithms.
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