摘要翻译：
提出了一种新的状态空间技术来估计带有VHF标记的野生动物个体在固定VHF阵列下的位置和空中运动。该方法结合了一个运动模型（横向（水平）平面上的Ornstein-Uhlenbeck随机过程和垂直方向上的Cox-Ingersoll-Ross过程）以确保三维轨迹的生物一致性，以及一个考虑距离、高度和方位角对接收信号强度影响的观测模型。接收信号的观测模型考虑了接收机噪声引起的低端饱和、接收机非线性引起的高端饱和以及无线多径现象，根据八木阵列的距离、高度和辐射特性对接收信号进行调制。合成了八木阵列的方向图函数，便于接收信号的线性化和随后的Kalman滤波应用。我们首先用模拟弹道验证模型，然后用区域自动无线电遥测网对一只带有甚高频标记的候鸟进行时空轨迹估计。该算法在给定系统参数和初始条件（平均误差<1km)的情况下，准确地预测了系统的平均运动轨迹。模拟的滨鸟航迹代表了使用固定遥测阵列对无线电标记鸟类的三维(3D)的首次估计，定性上是合理的，但在垂直面和初始条件上表现出一定的敏感性。
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英文标题：
《A State Space Technique for Wildlife Position Estimation Using
  Non-Simultaneous Signal Strength Measurements》
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作者：
Ramakrishna Janaswamy, Pamela Loring, and James McLaren
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  A novel state-space technique is presented to estimate the location and airborne movements of VHF tagged wildlife individuals with fixed VHF arrays. The approach combines a movement model (Ornstein- Uhlenbeck random process in the transverse (horizontal) plane and a Cox- Ingersoll-Ross process in the vertical direction) to ensure biologically-consistent trajectories in three-dimensions, and an observation model to account for the effect of range, altitude and bearing angle on the received signal strength. The observation model of received signals accounts for low-end saturation from receiver noise, high-end saturation from receiver non-linearities as well as a wireless multipath phenomena, which modulates the received signal according to range, the altitude and radiation characteristics of the Yagi array. A pattern function for the Yagi array is synthesized that facilitates linearization of the received signals and subsequent application of Kalman filtering.   We first validate the model using a simulated trajectory and then estimate the space-time trajectory of a migrating VHF-tagged shorebird, which was tracked with a regional automated radio telemetry network. The algorithm accurately predicted the average movement trajectory given the system parameters and the initial conditions (average error $<$ 1 km). The modeled shorebird track represents a first estimate in three-dimensional (3D) of a radio tagged bird using a fixed telemetry array, and was qualitatively reasonable, but exhibited some sensitivity in the vertical plane and to initial conditions.
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PDF链接：
https://arxiv.org/pdf/1805.11171