Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Currently the most accurate WLAN positioning systems are based on the fingerprinting approach, where a "radio map" is constructed by modeling how the signal strength measurements vary according to the location. Hover, collecting a sufficient amount of location-tagged training data is a rather tedious and time consuming task, especially in indoor scenarios -- the main application area of WLAN positioning -- where GPS coverage is unavailable. To alleviate this problem,  present a semi-supervised manifold learning technique for building accurate radio maps from partially labeled data, where only a small portion of the signal strength measurements need to be tagged with the corresponding coordinates. The basic idea is to construct a non-linear projection that maps high-dimensional signal fingerprints onto a two-dimensional manifold, thereby dramatically reducing the need of location-tagged data. Our results from a deployment in a real-world experiment demonstrate the practical utility of the method.
['Teemu Pulkkinen', 'Teemu Roos', 'Petri Myllymäki']
I am writing a research paper on a idea Semi-supervised learning for WLAN positioning
[{"from": "human", "value": "I am writing a research paper on a idea Semi-supervised learning for WLAN positioning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Currently the most accurate WLAN positioning systems are based on the fingerprinting approach, where a \"radio map\" is constructed by modeling how the signal strength measurements vary according to the location. Hover, collecting a sufficient amount of location-tagged training data is a rather tedious and time consuming task, especially in indoor scenarios -- the main application area of WLAN positioning -- where GPS coverage is unavailable. To alleviate this problem,  present a semi-supervised manifold learning technique for building accurate radio maps from partially labeled data, where only a small portion of the signal strength measurements need to be tagged with the corresponding coordinates. The basic idea is to construct a non-linear projection that maps high-dimensional signal fingerprints onto a two-dimensional manifold, thereby dramatically reducing the need of location-tagged data. Our results from a deployment in a real-world experiment demonstrate the practical utility of the method."}]