分布式检测中基于最大平均熵的局部观测量化
Maximum Average Entropy-Based Quantization of Local Observations for Distributed Detection
摘要：
在无线传感器网络中，多级量化是必要的，以找到一个折衷之间的最小可能的功耗的传感器和检测性能的融合中心（FC）。一般的方法是使用距离的措施，如J-散度和Bhattacharyya距离在这个量化。这项工作提出了一种不同的方法，这是基于最大化的平均输出熵的传感器在这两个假设下，并利用它在一个基于Neyman-Pearson准则的分布式检测方案，以检测点源。当传感器输出在FC处是无差错的时，以及当非相干M进制频移键控通信用于在瑞利衰落信道上传输基于最大平均熵（MAE）的多级量化传感器输出时，获得了所提出的MAE方法在量化传感器输出中的接收机操作特性。仿真研究表明，无论是在孤立的无差错融合的情况下，并在无线信道的影响被纳入的情况下，MAE的成功。正如预期的那样，随着量化级别的增加，性能变得更好，并且在六级别量化的情况下，它接近非量化数据传输的性能。
英文摘要：
In a wireless sensor network, multilevel quantization is necessary in order to find a compromise between the smallest possible power consumption of the sensors and the detection performance at the fusion center (FC). The general methodology is using distance measures such as J-divergence and Bhattacharyya distance in this quantization. This work proposes a different approach which is based on maximizing the average output entropy of the sensors under both hypotheses and utilizes it in a Neyman-Pearson criterion based distributed detection scheme in order to detect a point source. The receiver operating characteristics of the proposed maximum average entropy (MAE) method in quantizing sensor outputs was obtained for multilevel quantization both when the sensor outputs are available error-free at the FC and when non-coherent M-ary frequency shift keying communication is used for transmitting MAE based multilevel quantized sensor outputs over a Rayleigh fading channel. The simulation studies show the success of the MAE both in the cases of isolated error-free fusion and in the case where the effect of the wireless channel is incorporated. As expected the performance gets better as the level of quantization increases and with six-level quantization it approaches the performance of non-quantized data transmission.
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