The target guarding problem (TGP) comprises two players, an evader and a pursuer. The evader tries to reach a stationary target while avoiding the pursuer, and the pursuer tries to intercept the evader before the target is reached. Optimal strategies for the TGP have been studied in detail. These strategies assume the availability of noise-free measurements of positions and speeds of the respective players. The pursuer may lose the game from a position of advantage du to lack of perfect data. In this work, a strategy is derived for the pursuer when the evader’s position and speed measurements are corrupted by noise. A non-linear state space model is developed for the evader’s maneuver. An Extended Kalman filter is then designed to estimate the evader’s position and speed. This estimated data is used for calculating the probability of the target falling within the dominance regions of the pursuer or the evader. Based on this, a real time strategy is designed for the pursuer. Performance of this strategy is simulated and also validated through experiments conducted on a test-bed consisting of mobile robots.

目标守卫问题（TGP）包括两个参与者，一个逃避者和一个追击者。躲避者试图避开追击者而达到静止的目标，并且追赶者试图在到达目标之前拦截躲避者。对TGP的最佳策略已进行了详细研究。这些策略假定可以对各个播放器的位置和速度进行无噪声测量。追求者可能会因为缺乏完美数据而失去优势。在这项工作中，当逃避者的位置和速度测量被噪声破坏时，将为追击者提供一种策略。为逃避者的机动开发了非线性状态空间模型。然后设计一个扩展卡尔曼滤波器来估计逃避者的位置和速度。该估计数据用于计算目标落在追踪者或躲避者的优势区域内的概率。基于此，为追踪者设计了一种实时策略。通过在由移动机器人组成的测试台上进行的实验对这种策略的性能进行了仿真和验证。