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Optimization of RSSI based indoor localization and tracking to monitor workers in a hazardous working zone using Machine Learning techniques

This paper proposes a method for RSSI based indoor localization and tracking in cluttered environments using Deep Neural Networks. We implemented a real-time system to localize people using wearable active RF tags and RF receivers fixed in an industrial environment with high RF noise. The proposed solution is advantageous in analysing RSSI data in cluttered-indoor environments with the presence of human body attenuation, signal distortion, and environmental noise. Simulations and experiments on a hardware testbed demonstrated that receiver arrangement, number of receivers and amount of line of sight signals captured by receivers are important parameters for improving localization and tracking accuracy. The effect of RF signal attenuation through the person who carries the tag was combined with two neural network models trained with RSSI data pertaining to two walking directions. This method was successful in predicting the walking direction of the person.