The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.
A common approach to localization within existing radio frequency (RF)-based beacon networks is to use the received signal strength (RSS) as a fingerprint that is matched against a database of RSS-location pairs to determine the receiver's location @cite_23 . This database is typically generated manually via a site-survey,
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The Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15 percent of the fingerprinting load but the performance is severely degraded if it is reduced below this level. We propose an indoor localization framework that uses unsupervised manifold alignment. It requires only 1 percent of the fingerprinting load, some crowd sourced readings, and plan coordinates of the indoor area. The 1 percent fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5 m mean localization error, which is considerably better than semi-supervised approaches at very small amount of fingerprinting load. In addition, the few location estimations together with few fingerprints help to estimate the complete radio map of the indoor environment. The estimation of radio map does not demand extra workload rather it employs the already available information from the proposed indoor localization framework. The testing results for radio map estimation show almost 50 percent performance improvement by using this information as compared to using only fingerprints. Radio signal strength (RSS) is a reasonable proxy for link quality, but its accurate estimation requires frequency and spatial diversity due to fluctuation caused by fading. We consider a Rayleigh Rician fading model, and gather RSS measurements during motion in a complex environment to enable gradient estimation. Using the RSS gradient, we develop control laws to track active sources. These may be used to establish and preserve connectivity among collaborative autonomous agents, to locate and approach radio sources, as well as deploying agents to assist mobile ad hoc networks (MANETs). Among the various ranging techniques, Radio Signal Strength (RSS) based approaches attract intensive research interests because of its low cost and wide applicability. RSS-based ranging is apt to be affected by the multipath phenomenon which allows the radio signals to reach the destination through multiple propagation paths. To address this issue, previous works try to profile the environment and refer this profile in run-time. In practical dynamic environments, however, the profile frequently changes and the painful retraining is needed. Rather than such static ways of profiling the environments, in this paper, we try to accommodate the environmental dynamics automatically in real-time. The key observation is that given a pair of nodes, the RSS at different spectrum channels will be different. This difference carries the valuable phase information of the radio signals. By analyzing these RSS values, we are able to identify the amplitude of signals solely from the Line-of-Sight (LOS) path. This LOS amplitude is a simple function of the path length (the physical distance). We find that the analysis is a typical non-linear curvature fitting problem that has no general routing algorithms. We prove this problem format is ill-conditioned which has no stable and trustable solutions. To deal with this issue, we further explore the practical considerations for the problem and reform it to a greatly improved conditioning shape. We solve the problem by numerical iterations and implement these ideas in a real-time indoor tracking system called MuD. MuD employs only three TelosB nodes as anchors. The experiment results show that in a dynamic environment where five people move around, the averaged localization error is 1 meter. Compared with the traditional RSS-based approaches in dynamic environment, the accuracy improves up to 10 times. In this paper, a received signal strength (RSS) based localization technique is investigated for underwater optical wireless sensor networks (UOWSNs) where optical noise sources (e.g., sunlight, background, thermal, and dark current) and channel impairments of seawater (e.g., absorption, scattering, and turbulence) pose significant challenges. Hence, we propose a localization technique that works on the noisy ranging measurements embedded in a higher dimensional space and localize the sensor network in a low dimensional space. Once the neighborhood information is measured, a weighted network graph is constructed, which contains the one-hop neighbor distance estimations. A novel approach is developed to complete the missing distances in the kernel matrix. The output of the proposed technique is fused with Helmert transformation to refine the final location estimation with the help of anchors. The simulation results show that the root means square positioning error (RMSPE) of the proposed technique is more robust and accurate compared to baseline and manifold regularization.