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Finding optimal trajectory points for TDOA/FDOA geo-location sensors

机译:为TDOA / FDOA地理位置传感器找到最佳轨迹点

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In emitter geo-location estimation systems, it is well known that the geometry between sensors and the emitter can seriously impact the accuracy of the location estimate. Here we consider a case where a set of sensors is tasked to perform a sequence of location estimates on an emitter as the sensors progress throughout their trajectories. The goal is to select the trajectories so as to optimally improve the location estimate at each step in the sequence. To build the optimal trajectories, the aircraft, at their current locations, need to know their optimal next states at the time of next estimation, under the constraint of a reachable set due to limited reachable velocity or thrust. In this paper, we propose a one-step method to tackle the optimal next state (ONS) problem using the particle swarm optimization (PSO) by solving the optimal amount of applied thrust along the flying trajectories. Simulation results show that the proposed method dramatically improves the estimation accuracy along the flying trajectories, compared to the random walk and constant velocity scheme. We also show that the estimation accuracy performance is also insensitive to the problem dimensionality.
机译:在发射器地理位置估计系统中,众所周知,传感器和发射器之间的几何形状会严重影响位置估计的准确性。在这里,我们考虑一种情况,即随着传感器在其整个轨迹上前进,一组传感器被分配执行发射器上一系列位置估计的任务。目的是选择轨迹,以便最佳地改善序列中每个步骤的位置估计。为了建立最佳轨迹,在由于可到达的速度或推力有限而无法到达的集合的约束下,飞机在其当前位置处需要在下次估计时知道其最佳的下一状态。在本文中,我们提出了一种通过解决沿飞行轨迹施加的最佳推力来使用粒子群优化(PSO)解决最优下一状态(ONS)问题的单步方法。仿真结果表明,与随机行走和恒速方案相比,该方法极大地提高了沿飞行轨迹的估计精度。我们还表明,估计精度性能对问题的维数也不敏感。

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