首页|Chiba University Researcher Highlights Recent Research in Machine Learning (Impr oved Particle Filter in Machine Learning-Based BLE Fingerprinting Method to Redu ce Indoor Location Estimation Errors)
Chiba University Researcher Highlights Recent Research in Machine Learning (Impr oved Particle Filter in Machine Learning-Based BLE Fingerprinting Method to Redu ce Indoor Location Estimation Errors)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news originating from Chiba, Japan, by NewsRx correspondents, research stated, “Indoor position fingerprintbased locat ion estimation methods have been widely used by applications on smartphones.” Our news journalists obtained a quote from the research from Chiba University: “ In these localization estimation methods, it is very popular to use the RSSI (Re ceived Signal Strength Indication) of signals to represent the position fingerpr int. This paper proposes the design of a particle filter for reducing the estima tion error of the machine learning-based indoor BLE location fingerprinting meth od. Unlike the general particle filter, taking into account the distance, the pr oposed system designs improved likelihood functions, considering the coordinates based on fingerprint points using mean and variance of RSSI values, combining t he particle filter with the k-NN (k-Nearest Neighbor) algorithm to realize the r eduction in indoor positioning error.”
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