Approximation capabilities of multilayer feedforward networks
Neural Networks
The active badge location system
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The handbook of brain theory and neural networks
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Indoor geolocation science and technology
IEEE Communications Magazine
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Analysis of WLAN's received signal strength indication for indoor location fingerprinting
Pervasive and Mobile Computing
Wireless Personal Communications: An International Journal
Wireless Personal Communications: An International Journal
Wireless Personal Communications: An International Journal
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Estimating location of mobile devices based on received signal strength (RSS) patterns is an attractive method to realize indoor positioning systems. Accuracy of RSS based location estimation, particularly in large target sites, is effected by several environmental factors. Especially the temporal or permanent absence of radio signals introduces null values rendering sparsity and redundancy in feature space. We present a visibility matrix based modular classification model which systematically caters for unavailable signals. This model is practically realized using two eminent classification methods: (1) multi-layer perceptron and (2) LVQ. In order to confirm robustness and applicability of this model, we developed two location systems at different sites. Experimental results in real-world environments demonstrate that modular classification model consistently achieves superior location accuracy.