Adaptive Filtration of Parameters of the UAV Movement Based on the TDOA-Measurement Sensor Networks

ABSTRACT: In modern conditions unmanned aerial vehicles (UAVs) generate new classes of threats, including their use for terrorist purposes. A feature of modern UAVs is the ability to perform sudden maneuvers and to keep the same position in the point in space. For the description of the UAV movement with various types of maneuver it is used a rectangular coordinate system. We use the model in the form of stochastic dynamic system with random structure in the discrete time in which the change type UAV movement occurs at random times. When a UAV emits a sign, its location can be determined by wireless sensor networks (WSN) using the TDOA method. On the basis of a mathematical apparatus of the mixed Markov processes for in discrete time optimal and quasi-optimal adaptive algorithms for filtration of UAV movement parameters based on the TDOA-measurement, sensor networks are synthesized. Devices that realize these algorithms are multichannel and belong to the class of devices with feedback between channels. At the same time, in a quasi-optimal algorithm, a sequential procedure of the arriving measurements from sensors of a sensor network is realized, which allows to avoid the inversion of large-dimensional matrices. An analysis of the quasi-optimal adaptive algorithm is performed using statistical modeling. On the intervals of hovering and of the UAV movements without maneuver, the developed algorithm allows to increase significantly the accuracy of the estimation of the UAV coordinates, and also to recognize various types of its movement with high probability level.摘要：在当前环境下，无人机（Unmanned Aerial Vehicles, UAVs）催生了新型威胁类别，其中包括将其用于恐怖主义目的的场景。现代无人机的一项特性是能够实施突发机动，并可在空间定点保持同一位置。为描述各类机动下的无人机运动，我们采用直角坐标系。我们使用离散时间下带随机结构的随机动态系统（stochastic dynamic system）模型，其中无人机的运动模式会在随机时刻发生改变。当无人机发射信号时，无线传感器网络（Wireless Sensor Networks, WSN）可通过到达时间差（Time Difference of Arrival, TDOA）方法对其位置进行定位。基于离散时间混合马尔可夫过程的数学工具，我们合成了基于TDOA测量的无人机运动参数滤波最优及准最优自适应算法，并构建了配套的传感器网络。实现上述算法的设备为多通道设备，且属于通道间存在反馈的设备类别。同时，在准最优算法中，我们实现了对传感器网络各传感器所传来测量值的序贯处理流程，该流程可避免对高维矩阵求逆。我们通过统计建模对准最优自适应算法开展了性能分析。在无人机悬停及无机动飞行的时段内，所提出的算法可显著提升无人机坐标估计的精度，同时能够以较高概率准确识别其各类运动模式。