Low-Cost Localization based on Spatial Sparsity

Indoor localization has been a long-standing and important issue in the areas of signal processing and sensor networks that has raised increasing attention recently. One of the key demands in assistive environment is to promptly and accurately determine the state and activities of an inhabitant subject. The indoor localization provides an effective means in tracking the positions, motions, and reactions and detecting the falls of a patient, the elderly or any person with special needs for medical observation or accident prevention. The classic approach for localization is to first estimate one or more location-dependent signal parameters, such as time-of-arrival (TOA), angle-of-arrival (AOA) or received-signal-strength (RSS). Then in a second step, the collection of estimated parameters is used to determine an estimate of the subject’s location. The TOA-based methods are usually more accurate than RSS or AOA techniques. However, the accuracy of the classic TOA based methods often suffer from massive multipath conditions for indoor localization, which is caused by the reflection and diffraction of the RF signals from objects (e.g., interior walls, doors or furniture) in the environment ‎[1]. Moreover, it usually necessitates using synchronized emitters/sensors to be able to estimate accurate time-of arrival or time-difference-of-arrival. In ‎[2],‎[3],‎[4],‎[5], we proposed an accurate localization method based on both TOA and RSS and the spatial sparsity in the x-y-z space. In this approach, we directly estimate the location of the emitter without going through the intermediate stage of TOA or RSS estimation. The results demonstrate that the proposed method has very good performance even with small number of sensors. The results also indicate that, in contrary to the classic TOA-based methods, the proposed approach is a very effective and robust tool to overcome multipath issues, which is a very serious problem in indoor localization. Furthermore, the system works well in noisy environments with low SNRs. It implies that, even with low transmitted power (to keep the devices small with long battery life), we can still achieve a high localization accuracy. Figure 1 shows the preliminary results for localization and tracking in a sample building using 4 RF sensors. Figure 1 shows the actual trajectory (blue line) of an individual walking around in the room, and the estimated path (red line) by the proposed system. However, in this proposal, the main goal is to provide a low-cost and easy-to-install system that can be easily and quickly installed in a building, and yet to estimate the location of subjects with acceptable accuracy. To this end, we propose a low-cost system based on RSS only, and exploit the spatial sparsity of the target in the space.