Self-Contained Sensor Aided Kinematic HSGPS Positioning Algorithm

High Sensitivity GPS (HSGPS) provides significantly better availability in challenging urban environments but is also subjected to harsh cross-correlation interference and erroneous signal tracking due to multipath and echo-only signals (Lachapelle et al., 2003b). These effects can cause very large errors in the pseudorange measurements. Often, lack of redundancy at a single epoch limits the detection of such blunders by Receiver Autonomous Integrity Monitoring (RAIM) or other statistical testing techniques (Lu, 1991; Brown, 1992; Parkinson and Spilker, 1996). The inability to perform such testing, results in a poor estimation of navigation solution accuracy. In this work, a novel approach to process sensor aided HSGPS data especially in a very challenging GPS environment is proposed. The approach takes advantage of a self-contained sensor augmentation to HSGPS that provides additional information, such as travelled distance and heading between epochs, thus enabling a batch LSQ procedure in a kinematic mode. In the case under study, a pedestrian navigation application is selected. It should be noted that this paper is not aiming at an optimal strategy of sensor integration with HSGPS, but rather considers a particular novel approach of kinematic LSQ batch processing of HSGPS data with the sensor aiding. The proposed method intends to provide a means for better accuracy assessment of HSGPS results, and thus a more reliable HSGPS position solution that later can be successfully used in other filtering methods, such a Kalman Filter.