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This paper presents a star tracking algorithm to determine the orientation of aircrafts, spacecrafts and automobiles. Such tracking is especially challenging since it requires robustness and should work continuously and autonomously. A wider view of this problem is utilizing the algorithm on automobiles and aircrafts and predict the device's orientation throughout time from the computed position. The novelty of the proposed framework lies in both the computational efficiency and the ability of the star tracker to cope with noisy measurements and outliers. Both simulator and initial field experiments confirm the benefits of the proposed framework.

As GNSS receivers become common in our daily lives and our reliance on them grows stronger, GNSS attacks also are becoming more common and sophisticated than ever. These attacks can range from accidental interruption to intentional denial of service to advanced spoofing equipment designed to deceive the GNSS receiver and make it think it is located elsewhere. Without proper protection, these real-life threats can cause, for example, temporal closure of airports and loss of autonomous vehicles. Today's solutions for defending against such attacks are limited, expensive or require significant processing power that cannot be implemented in energy-critical applications. This work suggests a low cost, efficient and simple method to cope with the threats and demonstrates its operation in a real-life scenario.

This work presents a generic method for improving the velocity estimation of GNSS devices. The suggested algorithm is based on the famous Kalman Filter which utilizes both GNSS and IMU measurements. The suggested sensor-fusion method contains a built-in classifier for identifying measurements with low confidence. The algorithm was implemented and tested using both simulation and real-world data. The results show significant improvement in the velocity estimation. This improvement can be used for further positioning accuracy improvement of a wide range of COTS GNSS devices.