Filtration of UAV Movement Parameters Based on the Received Signal Strength Measurement Sensor Networks in the Presence of Anomalous Measurements of Unknown Power at the Transmitter

IF 0.9 Q3 ENGINEERING, AEROSPACE
I. Tovkach, S. Zhuk
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引用次数: 2

Abstract

Methods based on received signal strength measurements (RSS measurements) are used to determine the unmanned aerial vehicle (UAV) location using a wireless sensor network. The UAV transmitter power is usually unknown. In real conditions, it often becomes necessary to consider existence of anomalous measurement results. The reasons for the violation of the measurement process can be: the influence of interference, errors in the identification of signals during primary processing, failures of the equipment and similar. The optimum and quasi-optimal adaptive algorithms of UAV movement parameters filtration based on the RSS-measurement sensor networks in the presence of anomalous measurements at the unknown power of the transmitter are developed. These algorithms are obtained using Bayes’ theorems and the Markov property of a mixed process, including a vector of target movement parameters and a discrete component characterizing the type of measurement. Analysis of developed algorithm performance was carried out by Monte Carlo method on 2D plane. The quasi-optimal adaptive filtering algorithm detects the appearance of anomalous measurements with probabilities close to unity and allows one to eliminate their influence on the accuracy of UAV movement parameters estimation and also to estimate the UAV unknown transmitter power.
在发射机存在未知功率异常测量的情况下,基于接收信号强度测量传感器网络的无人机运动参数过滤
基于接收信号强度测量(RSS测量)的方法用于使用无线传感器网络来确定无人机(UAV)的位置。无人机发射机功率通常未知。在实际情况下,经常需要考虑异常测量结果的存在。违反测量过程的原因可能是:干扰的影响、初级处理过程中信号识别的错误、设备故障等。在发射机功率未知的情况下,提出了基于RSS测量传感器网络的无人机运动参数过滤的最优和准最优自适应算法。这些算法是使用贝叶斯定理和混合过程的马尔可夫性质获得的,包括目标运动参数的向量和表征测量类型的离散分量。采用蒙特卡罗方法在二维平面上对所开发的算法性能进行了分析。准最优自适应滤波算法检测概率接近1的异常测量的出现,并允许消除它们对无人机运动参数估计准确性的影响,还可以估计无人机未知发射机功率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.00
自引率
0.00%
发文量
16
审稿时长
20 weeks
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