One-Bit Decentralized Detection With a Rao Test for Multisensor Fusion

Abstract

Abstract-In this letter we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. We consider sensors observing an unknown deterministic parameter with symmetric and unimodal noise. A decision fusion center (DFC) receives quantized sensor observations through error-prone binary symmetric channels and makes a global decision. We analyze the optimal quantizer thresholds and we study the performance of the Rao test in comparison to the GLRT. Also, a theoretical comparison is made and asymptotic performance is derived in a scenario with homogeneous sensors. All the results are confirmed through simulations.

Specific Emitter Identification Based on the Natural Measure

“…The experimental results show that the proposed method is not only easy to operate, but also very effective for SEI even though the amount of data is small and the sample rate is low in real application. Moreover, we will investigate the application of the proposed method to sensor networks in the future, just as the detection of target [30] and the modulation classification [31] have been extended to this field.…”

Method for Feature Extraction Based on Composite Spectrum and Relative Entropy Fusion

“…[1] 。由于自身 结构影响，液压泵在性能退化过程中振动信号的复 杂度和随机性也会发生相应变化 [2][3] 。因此，如何从 振动信号中提取相对完整而又简洁的特征，以有效 表征液压泵性能退化过程，是目前亟待解决的主要 问题。 信息融合技术能够对冗余信息、互补信息以及 * 国家自然科学基金资助项目(51275524)。20161015 收到初稿，20170720 收到修改稿 协同信息进行综合分析处理，提高系统的可靠性， 目前已在信号分析和故障诊断等领域得到了广泛应 用，显现出自身极大的优越性 [4] 。考虑到液压泵自 身的结构特点，需要采用合适方法对多方向振动信号 进行融合，综合利用各信号所包含的特征信息。常用 方法主要有加权融合法、卡尔曼滤波法以及小波分析 法 [5][6] 。但是，加权融合法对权值系数的设置与调整存 在较大的主观性 [7][8] ； 卡尔曼滤波法对于非线性系统缺 乏严格的滤波公式 [9][10] ；小波分析法的重构过程会导 致特征部分信息的遗漏 [11] 。复合谱 [12][13] (Composite spectrum, CS)在傅里叶变换的基础上，利用分量间 的相关系数及互功率谱，能够实现多通道信号间的 信息融合，在抑制噪声分量的同时，提高特征的表 月 2017 年 12 月 孙 健等：一种基于复合谱与关联熵融合的特征提取方法 97 征能力。但是，由于傅里叶变换自身的特点，CS 算法多次采用相邻信号的傅里叶系数与共轭的连乘 模式，会造成部分信息的遗漏，导致对能量变化的 敏 感 度 降 低 。 考 虑 到 离 散 余 弦 变 换 [14] (Discrete cosine transform，DCT)是傅里叶变换的推广，其系 数均为实数，避开了 CS 中对傅里叶系数取共轭的 环节，且它具有能量聚集性质 [15] ，能够有效改善特 征敏感度。 此外，不同算法所提出来的特征虽然能够从不同 侧面反映故障信息，改善描述的完整性，但是也会增 加信息的冗余程度，甚至带来维数灾难等问题 [16] 。 目前解决此问题的主要方法是降维处理，对特征 进行约简，进而大大降低计算量，但是这种降维 处理是对部分特征的直接剔除， 容易造成部分敏感 信息的遗漏 [17] 。为此，需要采用合适的特征融合策 …”

One-Bit Quantization and Distributed Detection with an Unknown Scale Parameter

“…In the context of wireless sensor networks (WSNs), the detection of unknown parameters by using a multitude of sensor nodes has been of significant interest over the past few decades (e.g., [1][2][3][4][5][6][7][8][9][10][11][12][13] and the references therein). The widely-deployed sensors are designated to provide measurements of a given physical process (temperature, humidity, etc.…”

“…In practice, especially to meet bandwidth/energy constraints, there is a need to reduce the dimension of raw measurements from each sensor to the FC by using one-bit quantization [1][2][3][4][5][6][7][8][9][10][11]. In [4], a fixed quantization (FQ) approach was examined for distributed estimation of a mean parameter (i.e., the mean of observations).…”

“…Using a generalized likelihood ratio test (GLRT) framework, joint estimation and detection of an unknown mean parameter based on one-bit quantization was considered in [8]. A one-bit Rao detector was introduced as a computationally-efficient alternative to the GLRT in [9]. Meanwhile, distributed estimation of both an unknown mean parameter and a scale parameter was considered in [10], where sensors are divided into two groups, each using a different threshold to generate binary observations.…”