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Kalman filter and its nonlinear extension, extended Kalman filter provide a feasible solution to mitigating non-line of sight (NLOS) propagation effects, and therefore improving accuracy of mobile target tracking in indoor wireless environments. Most wireless communication systems for indoor positioning and tracking may suffer from different error sources, including process errors, measurement errors, the NLOS propagation effects and dense multipath arrivals. The errors sources, if not properly eliminated or mitigated, generally yield severe degradation of accuracy in ranging, positioning and tracking. Among the factors that cause performance degradation, the NLOS effect is considered the major error source in indoor location systems using one or more types of measured location metrics. Accurate indoor positioning and tracking play an important role in home safety, public services, and other commercial or military applications (Pahlavan et al., 1998). In recent years, indoor localization has drawn increasing interests from academia and industry. There is an increasing demand of indoor localization systems for tracking persons with special needs, such as the elders and children who may be away from visual supervision. Other applications need the solutions to tracing mobile devices or movable objects in the covered areas of sensor networks, or localizing accurately in-demand portable equipments in hospitals and laboratories. In public safety and military operations, the tracking systems can be used in navigating and coordinating police officers, fire-fighters or soldiers to complete cooperative missions inside buildings. Various positioning techniques have been developed in the past few years. Handset-based positioning methods generally require that a modified handheld device calculate its own position by using a fully or partially equipped global positioning system (GPS) receiver. The method is, however, unfortunately not suitable for many indoor localization applications. Network-based methods have their advantages in wireless location and indoor positioning. The methods can be used for location estimation in situations where GPS solutions are not applicable. In the network-based wireless location, various schemes using received signal strength indication (RSSI) have also been extensively investigated in the past two decades. In location systems using the RSSI, location estimation is usually obtained from or augmented with the location fingerprinting scheme. Though no complex measurement equipments are involved, the build-up of a “radio map” of RSSI may be time-consuming.

Kalman Filtering for NLOS Mitigation and Target Tracking in Indoor Wireless Environment