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Target localization has a wide range of military and civilian applications in wireless mobile networks. Examples include battle-field surveillance, emergency 911 (E911), traffic alert, habitat monitoring, resource allocation, routing, and disaster mitigation. Basic localization techniques include time-of-arrival (TOA), direction-of-arrival (DOA) and received-signal strength (RSS) estimation. Techniques that are proposed based on TOA and DOA are very sensitive to the availability of Line-of-sight (LOS) which is the direct path between the transmitter and the receiver. If LOS is not available, TOA and DOA estimation errors create a large localization error. In order to reduce NLOS localization error, NLOS identification, mitigation, and localization techniques have been proposed. This research investigates NLOS identification for multiple antennas radio systems. The techniques proposed in the literature mainly use one antenna element to enable NLOS identification. When a single antenna is utilized, limited features of the wireless channel can be exploited to identify NLOS situations. However, in DOA-based wireless localization systems, multiple antenna elements are available. In addition, multiple antenna technology has been adopted in many widely used wireless systems such as wireless LAN 802.11n and WiMAX 802.16e which are good candidates for localization based services. In this work, the potential of spatial channel information for high performance NLOS identification is investigated. Considering narrowband multiple antenna wireless systems, two xv NLOS identification techniques are proposed. Here, the implementation of spatial correlation of channel coefficients across antenna elements as a metric for NLOS identification is proposed. In order to obtain the spatial correlation, a newmulti-input multi-output (MIMO) channel model based on rough surface theory is proposed. This model can be used to compute the spatial correlation between the antenna pair separated by any distance. In addition, a new NLOS identification technique that exploits the statistics of phase difference across two antenna elements is proposed. This technique assumes the phases received across two antenna elements are uncorrelated. This assumption is validated based on the well-known circular and elliptic scattering models. Next, it is proved that the channel Rician K-factor is a function of the phase difference variance. Exploiting Rician K-factor, techniques to identify NLOS scenarios are proposed. Considering wideband multiple antenna wireless systems which useMIMO-orthogonal frequency division multiplexing (OFDM) signaling, space-time-frequency channel correlation is exploited to attain NLOS identification in time-varying, frequency-selective and spaceselective radio channels. Novel NLOS identification measures based on space, time and frequency channel correlation are proposed and their performances are evaluated. These measures represent a better NLOS identification performance compared to those that only use space, time or frequency.

Multi-antenna non-line-of-sight identification techniques for target localization in mobile ad-hoc networks