The accuracy of radio-based positioning is heavily influenced by a dense multipath (DM) channel, leading to poor position accuracy. The DM affects both time of flight (ToF) measurements extracted from wideband (WB) signals—specifically, if the bandwidth is below 100 MHz—as well as received signal strength (RSS) measurements, due to the interference of multipath signal components onto the information-bearing line-of-sight (LoS) component. This work proposes an approach for combining these two different measurement technologies, leading to a robust position estimation in the presence of DM. We assume that a large ensemble of densely-spaced devices is to be positioned. We use RSS measurements to determine “clusters” of devices in the vicinity of each other. Joint processing of the WB measurements from all devices in a cluster efficiently suppresses the influence of the DM. We formulate an algorithmic approach for the information fusion of the two technologies and derive the corresponding Cramér-Rao lower bound (CRLB) to gain insight into the performance trade-offs at hand. We evaluate our results by simulations and validate the approach with real-world measurement data. The results show that the clustering approach can halve the root-mean-square error (RMSE) from about 2 m to below 1 m, using WB signal transmissions in the 2.4 GHz ISM band at a bandwidth of about 80 MHz.

This work proposes a novel algorithm for robust cooperative localization using wideband time difference of arrival (TDoA) measurements and information coupling between nodes. The algorithm leverages a generalized cross-correlation framework to exploit the similarity of wideband measurements within the geometric vicinity of nodes. Alternatively, the algorithm uses a computationally efficient method based on perceptual hashing, a signal compression algorithm adapted from image-processing, which gives a distance-dependent metric between nodes. The algorithm is embedded within a message-passing framework, enabling the estimation of the joint-posterior probability density function of all the nodes. The paper evaluates the localization performance, using both simulated and real measurements in various challenging scenarios, and compares it with a cooperative localization method that uses additional received signal strength measurements inbetween nodes. The results demonstrate that the proposed algorithm can achieve similar or superior localization accuracy, with the same computational complexity and without the need for additional measurements. The proposed algorithm shows significant improvement over non-cooperative measurements. It achieves an improvement of root-mean-square error from 2.4m to 1.3m for a large-scale scenario with approximately 1000 nodes, using a signal bandwidth of 80MHz for the TDoA measurements and six fixed, dual-antenna, anchor nodes.