A passive indoor visible light positioning system is proposed that does not require active participation from the user and is suitable for IoT sensor networks. This approach does not require a line-of-sight path and measures the impulse response (IR) between sources and receivers installed in the room. The presence of an object of interest (OI), i.e., a person to be localized, disrupts the IRs among the source–receiver pairs, which can be related to its position. A deep learning framework is developed that learns the relationship between changes in sets of IRs and the OI position through a set of training data obtained by placing the OI at random locations in the room. This approach shows that the OI can be localized using a very limited set of training data under a wide range of illumination levels. In order to represent a realistic scenario, a room with furniture is modeled in the optical system design software. The ray trace information of the modeled room is used to construct IR measurements among different source–receiver pairs that include multiorder reflections. The results show that localization performance is crucially related to the signal-to-noise ratio and the number of training data points used in the learning process. A root-mean-square error (RMSE) near 30 cm is possible in the case of high SNR and a large training set. However, even with a very limited training set and over a range of dimming levels, RMSEs of near 80 cm were obtained without the need for explicit user involvement.

In this paper we present a new methodology for indoor localization using visible light emissions which does not require users to actively participate in the process. Our passive positioning approach utilizes a network of luminaires and receivers on the ceiling and measures the impulse responses (IRs) between each source-receiver pair. This channel sounding approach leverages changes in the measured IRs to localize an object in the room. Simulation results show that with a database of sampled IRs, the root mean squared (RMS) positioning error can be made on the order of 4 cm. In order to lower storage requirements, a simplified database based on fitting to an integrating sphere model of the IR yields localization performance with RMS error around 6 cm.

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.

One major bottleneck in the practical implementation of received signal strength (RSS) based indoor localization systems is the extensive deployment efforts required to construct the radio maps through fingerprinting. In this paper, we aim to design an indoor localization scheme that can be directly employed without building a full fingerprinted radio map of the indoor environment. By accumulating the information of localized RSSs, this scheme can also simultaneously construct the radio map with limited calibration. To design this scheme, we employ a source data set that possesses the same spatial correlation of the RSSs in the indoor environment of interest. The knowledge of this data set is then transferred to a limited number of calibration fingerprints and one or several RSS observations with unknown locations, in order to perform direct localization of these observations using manifold alignment. We test two different source data sets, namely a simulated radio propagation map and the environment's plan coordinates. For moving users, we exploit the correlation of their observations to improve their localization accuracy. The online testing in two indoor environments shows that the plan coordinates achieve better results than the simulated radio maps, and a negligible degradation with 70-85 percent reduction in the calibration load.

The main limitation of deploying/updating Received Signal Strength (RSS) based indoor localization is the construction of fingerprinted radio map, which is quite a hectic and time-consuming process especially when the indoor area is enormous and/or dynamic. Different approaches have been undertaken to reduce such deployment/update efforts, but the performance degrades when the fingerprinting load is reduced below a certain level. In this paper, we propose an indoor localization scheme that requires as low as 1% fingerprinting load. This scheme employs unsupervised manifold alignment that takes crowd sourced RSS readings and localization requests as source data set and the environment's plan coordinates as destination data set. The 1% fingerprinting load is only used to perturb the local geometries in the destination data set. Our proposed algorithm was shown to achieve less than 5 m mean localization error with 1% fingerprinting load and a limited number of crowd sourced readings, when other learning based localization schemes pass the 10 m mean error with the same information.