{"id":7891,"date":"2022-09-09T08:48:55","date_gmt":"2022-09-09T08:48:55","guid":{"rendered":"https:\/\/www.beaconzone.co.uk\/blog\/?p=7891"},"modified":"2022-09-09T08:48:57","modified_gmt":"2022-09-09T08:48:57","slug":"using-ai-machine-learning-to-improve-ranging-accuracy","status":"publish","type":"post","link":"https:\/\/www.beaconzone.co.uk\/blog\/using-ai-machine-learning-to-improve-ranging-accuracy\/","title":{"rendered":"Using AI Machine Learning to Improve Ranging Accuracy"},"content":{"rendered":"\n

There’s new research from Oregon State University, USA and Peking University, Beijing, China on A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI<\/a>.<\/p>\n\n\n

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System Workflow<\/figcaption><\/figure><\/div>\n\n\n

Machine learning was used to determine the line-of-sight distance in a multipath (reflective) environment. Due to the multipath effect, acquired signals indoors have complex mathematical models. A machine learning Artificial Neural Networks (ANN) is the most efficient way to process these signals.<\/p>\n\n\n

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The system achieved accuracy where 75% of the errors were less than 0.1 m with a median error of 0.037 m and a mean error of 0.092 m. This reduced ranging errors to under 10cm. The researchers were able to achieve high-precision indoor ranging without the need for a wide signal bandwidth nor synchronisation. The system was also simple and low cost to deploy due to low complexity of the equipment.<\/p>\n","protected":false},"excerpt":{"rendered":"

There’s new research from Oregon State University, USA and Peking University, Beijing, China on A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI. Machine learning was used to determine the line-of-sight distance in a multipath (reflective) environment. Due to the multipath effect, acquired signals indoors have complex mathematical models. A machine learning … Continue reading “Using AI Machine Learning to Improve Ranging Accuracy”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[294,173,32],"tags":[],"_links":{"self":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts\/7891"}],"collection":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/comments?post=7891"}],"version-history":[{"count":2,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts\/7891\/revisions"}],"predecessor-version":[{"id":7931,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts\/7891\/revisions\/7931"}],"wp:attachment":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/media?parent=7891"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/categories?post=7891"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/tags?post=7891"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}