A collision avoidance algorithm for unmanned aerial vehicles (UAVs) based on the conventional proportional navigation (PN) guidance law is investigated. The proportional navigation guidance law being applied to a wide range of missile guidance problems is tailored to the collision avoidance of UAVs. This can be accomplished by guiding the relative velocity vector of the aircraft to a vector connecting the current aircraft position to the safety boundary of the target aircraft. Stability of the proposed algorithm is also studied using the circle criterion. The stability condition can be established by choosing the navigation coefficient within a certain bound. The guidance law is extended to 3-dimensional maneuver problems. Inherent simplicity and robustness of the PN guidance law provides satisfactory collision avoidance performance with different initial conditions.
We provide a tutorial-like description of Kalman filter and extended Kalman filter. This chapter aims for those who need to teach Kalman filters to others, or for those who do not have a strong background in estimation theory. Following a problem definition of state estimation, filtering algorithms will be presented with supporting examples to help readers easily grasp how the Kalman filters work. Implementations on INS/GNSS navigation, target tracking, and terrain-referenced navigation (TRN) are given. In each example, we discuss how to choose, implement, tune, and modify the algorithms for real world practices. Source codes for implementing the examples are also provided. In conclusion, this chapter will become a prerequisite for other contents in the book.
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