Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/20947
Title: Super Resolution Algorithms for Indoor Positioning Systems
Authors: GODALIYADDA, G. M. ROSHAN INDIKA
Keywords: Indoor Localization,Super Resolution Techniques,Time of Arrival Estimation,Location based Fingerprinting,MUSIC Algorithm,Wireless Sensor Networks
Issue Date: 7-Jul-2010
Source: GODALIYADDA, G. M. ROSHAN INDIKA (2010-07-07). Super Resolution Algorithms for Indoor Positioning Systems. ScholarBank@NUS Repository.
Abstract: The hostile nature of indoor radio environments and the rapid growth of commercial indoor positioning systems have placed a significant emphasis on developing robust localization techniques. The challenging problem of accurate positioning in hostile indoor environments with severe multipath and noise conditions is tackled through the introduction of the MUSIC super resolution algorithm. Due to its higher resolution capability and superior noise immunity, compared to other standard correlation techniques, it can be utilized to provide accurate time delay estimates under LoS conditions. The resultant pseudo-spectrums obtained by using this method, can also be used as location information rich fingerprints for NLoS conditions as well. The research work presented in this thesis focuses on the introduction of new variants in addition to the standard FD-MUSIC algorithm, such as the TD-MUSIC algorithm for more versatile and accurate performance. In-depth behavioural analysis is presented on the FD-MUSIC, FD-EV and TD-MUSIC algorithms to properly understand the strengths and limitations of each of the methods. The ESPRIT algorithm is introduced as an alternative, for systems that wish to forego a peak detection process at the expense of diminished accuracy. The variation of the steering vector pulse spread enabled us to identify the spectral leakage phenomenon of the TD-MUSIC algorithm, thereby enabling us to use it for our own advantage under certain conditions. The Eigen value de-weighting of the FD-EV method, is identified for having the capability to resurface underestimated signal peaks submerged beneath the noise floor, under friendly SNR and bandwidth conditions. The superior resolution capability, bandwidth versatility and noise immunity of the TD-MUSIC algorithm is then demonstrated. Finally, we introduce the TD-EV method, which effectively combines the positive attributes of the TD-MUSIC algorithm and the FD-EV algorithm. This is done in order to utilize the superior resolution capability, noise immunity and bandwidth versatility of the TD-MUSIC algorithm and the resurfacing capability of the FD-EV method. Thus it is demonstrated how the TD-EV method emerges as the ultimate performer, under band limited conditions with low SNR, while the signal subspace dimension is underestimated.
URI: http://scholarbank.nus.edu.sg/handle/10635/20947
Appears in Collections:Ph.D Theses (Open)

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