Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a dynamic MMSE estimator for tracking mobile users in indoor wireless local area networks (WLAN) based on received signal strength (RSS). The method uses a training-based static estimate obtained by an adaptive kernel density estimator as the input into a Kalman Filter. Predictions from the filter are used during the next iteration to adaptively select a subset of training data, contained in a radio map, for the static estimator. Experimental results show that the combination of the Kalman filter and the adaptive radio map technique results in nearly 0.5 m (20%) improvement in root mean square location accuracy when compared to static localization
['Azadeh Kushki', 'Konstantinos N. Plataniotis', 'Anastasios N. Venetsanopoulos']
I am writing a research paper on a idea Location Tracking in Wireless Local Area Networks with Adaptive Radio MAPS
[{"from": "human", "value": "I am writing a research paper on a idea Location Tracking in Wireless Local Area Networks with Adaptive Radio MAPS"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a dynamic MMSE estimator for tracking mobile users in indoor wireless local area networks (WLAN) based on received signal strength (RSS). The method uses a training-based static estimate obtained by an adaptive kernel density estimator as the input into a Kalman Filter. Predictions from the filter are used during the next iteration to adaptively select a subset of training data, contained in a radio map, for the static estimator. Experimental results show that the combination of the Kalman filter and the adaptive radio map technique results in nearly 0.5 m (20%) improvement in root mean square location accuracy when compared to static localization"}]