基于接收信号强度的迭代最大似然方法的发射器定位

Sichun Wang, B. Jackson, S. Rajan, F. Patenaude
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引用次数: 5

摘要

批处理模式最大似然(ML)接收信号强度(RSS)发射器地理定位算法通过使用单个传感器或由多个空间分散的传感器在同一时刻收集的观测期间收集的数据块产生位置估计。由于处理器速度、数据存储的内存、数据传输的时间和通信带宽等实际限制,批处理模式算法只能在小数据集上实时实现。本文提出了一种用于大数据集实时实现的ML RSS地理定位算法的似然函数的迭代公式。仿真和实验结果验证了所提出的公式。
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Received Signal Strength-Based Emitter Geolocation Using an Iterative Maximum Likelihood Approach
Batch-mode maximum likelihood (ML) received signal strength (RSS) emitter geolocation algorithms produce location estimates from a block of data collected over an observation period using either a single sensor or collected at one time instant by multiple spatially dispersed sensors. Due to practical constraints such as processor speed, memory for data storage, time for data transfer and communications bandwidth, batch-mode algorithms can only be implemented in real-time for small data sets. This paper presents an iterative formulation of the likelihood function for the ML RSS geolocation algorithm for real-time implementation with large data sets. Simulation and experimental results are included to validate the proposed formulation.
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