GPS-dependent positioning, navigation, and timing synchronization procedures have a significant impact on everyday life. Therefore, such a widely used system increasingly becomes an attractive target for illicit exploitation by terrorists and hackers for various motives. As such, spoofing and antispoofing algorithms have become an important research topic within the GPS discipline. This paper will provide a review of recent research in the field of GPS spoofing/anti-spoofing. The vulnerability of GPS to a spoofing attack will be investigated and then different spoofing generation techniques will be discussed. After introducing spoofing signal model, a brief review of recently proposed anti-spoofing techniques and their performance in terms of spoofing detection and spoofing mitigation will be provided. Limitations of anti-spoofing algorithms will be discussed and some methods will be introduced to ameliorate these limitations. In addition, testing the spoofing/anti-spoofing methods is a challenging topic that encounters some limitations due to stringent emission regulations.

This paper discusses algorithmic concepts, design and testing of a system based on a low-cost MEMS-based inertial measurement unit (IMU) and high-sensitivity global positioning system (HSGPS) receivers for seamless personal navigation in a GPS signal degraded environment. The system developed here is mounted on a pedestrian shoe/foot and uses measurements based on the dynamics experienced by the inertial sensors on the user's foot. The IMU measurements are processed through a conventional inertial navigation system (INS) algorithm and are then integrated with HSGPS receiver measurements and dynamics derived constraint measurements using a tightly coupled integration strategy. The ability of INS to bridge the navigation solution is evaluated through field tests conducted indoors and in severely signal degraded forest environments. The specific focus is on evaluating system performance under challenging GPS conditions.

A single low cost inertial measurement unit (IMU) is often used in conjunction with GPS to increase the accuracy and improve the availability of the navigation solution for a pedestrian navigation system. This paper develops several fusion algorithms for using multiple IMUs to enhance performance. In particular, this research seeks to understand the benefits and detriments of each fusion method in the context of pedestrian navigation. Three fusion methods are proposed. First, all raw IMU measurements are mapped onto a common frame (i.e., a virtual frame) and processed in a typical combined GPS-IMU Kalman filter. Second, a large stacked filter is constructed of several IMUs. This filter construction allows for relative information between the IMUs to be used as updates. Third, a federated filter is used to process each IMU as a local filter. The output of each local filter is shared with a master filter, which in turn, shares information back with the local filters. The construction of each filter is discussed and improvements are made to the virtual IMU (VIMU) architecture, which is the most commonly used architecture in the literature. Since accuracy and availability are the most important characteristics of a pedestrian navigation system, the analysis of each filter’s performance focuses on these two parameters. Data was collected in two environments, one where GPS signals are moderately attenuated and another where signals are severely attenuated. Accuracy is shown as a function of architecture and the number of IMUs used.

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Carrier phase-based positioning using Global Navigation Satellite System (GNSS) signals can provide centimeter-level accuracy; however, to do so requires robust, continuous tracking of the phase of the received signal. The phase-locked loop is typically the weakest link in GNSS signal processing, with frequent cycle slips and loss of lock occurring at lower signal-to-noise ratios. One way to improve the signal-to-noise ratio is to increase the coherent integration time; however doing so reduces the loop update rate, thereby degrading performance. This paper investigates this trade-off between sensitivity and loop update rate by investigation of the Kalman filter-based tracking loop. It is shown that it is possible to choose an optimal integration time for a given application. A relatively straightforward procedure is given to determine this optimal value. The results are confirmed through real-time kinematic processing of live satellite signals.