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Unmanned aerial vehicles (UAVs) are increasingly used in military and scientific research. Some miniaturized UAVs rely entirely on the global positioning system (GPS) for navigation. GPS is vulnerable to accidental or deliberate interference that can cause it to fail. It is not unusual, even in a benign environment, for a GPS outage to occur for periods of seconds to minutes. For UAVs relying solely on GPS for navigation such an event can be catastrophic. This paper proposes an extended Kalman filter approach to estimate the location of a UAV when its GPS connection is lost, using inter-UAV distance measurements. The results from a recent trial conducted by DSTO in Australia with three UAVs are presented. It is shown that the location of a manoeuvering UAV that has lost the GPS signal can be determined to an accuracy of within 40m of its true location simply by measuring the range to two other UAVs at known location, where the range measurement error has a zero mean and a standard deviation of 10m.

Unmanned aerial vehicles (UAVs) are increasingly used in military and scientific research. Some miniaturized UAVs rely entirely on the global positioning system (GPS) for navigation. GPS is vulnerable to accidental or deliberate interference that can cause it to fail. It is not unusual, even in a benign environment, for a GPS outage to occur for periods of seconds to minutes. For UAVs relying solely on GPS for navigation such an event can be catastrophic. This paper proposes an extended Kalman filter approach to estimate the location of a UAV when its GPS connection is lost, using inter-UAV distance measurements. The results from a recent trial conducted by DSTO in Australia with three UAVs are presented. It is shown that the location of a manoeuvering UAV that has lost the GPS signal can be determined to an accuracy of within 40m of its true location simply by measuring the range to two other UAVs at known location, where the range measurement error has a zero mean and a standard deviation of 10m.

For a flying military vehicle, avoiding detection can be a key objective. To achieve this, flying the least-probability-of-detection path from A to B through a field of detectors is a fundamental strategy. While most of the previous optimization models aim to minimize the cumulative radar exposure, this paper derives a model that can directly minimize the probability of being detected. Furthermore, a variational dynamic programming method is applied to this model which allows one finding a precise local optimal path with low computational complexity. In addition, a homotopy method is derived to adjust the optimal path with exceptionally low computational complexity when the detection rate function changes due to the removal of detectors, the addition of detectors or the changes of understanding of detectors.

For a flying military vehicle, avoiding detection can be a key objective. To achieve this, flying the least-probability-of-detection path from A to B through a field of detectors is a fundamental strategy. While most of the previous optimization models aim to minimize the cumulative radar exposure, this paper derives a model that can directly minimize the probability of being detected. Furthermore, a variational dynamic programming method is applied to this model which allows one finding a precise local optimal path with low computational complexity. In addition, a homotopy method is derived to adjust the optimal path with exceptionally low computational complexity when the detection rate function changes due to the removal of detectors, the addition of detectors or the changes of understanding of detectors.