A New Method of Data Compression in Multisensor Estimation Fusion
首发时间:2005-12-28
Abstract:Consider the decentralized estimation of an unknown parameter by bandwidth constrained sensor network with a fusion center. Local sensors make observations which are linearly scaled versions of these parameters corrupted by additive noise. For each sensor, the probability distribution functions of the noise is known. In this paper, we propose a new approach that convert the estimation fusion problem to decision fusion problem. With the methods of decision fusion, we find optimal local sensor compress rules which compress sensor observations into bits. The fusion center combines the transmitted bits from all the local sensors to generate a final estimator of the unknown parameter. Numerical examples show the efficiency of the new method.
keywords: Parameter estimation data compression Bayesian decision
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多传感器估计融合中数据压缩的新方法
摘要: 在通讯带宽限制的分布式多传感器网络中,我们考虑传感器和融合中心共同作用估计一个未知参数。这些参数会受到价型噪声干扰,而这些噪声的分布都是已知的,传感器观测到这些受干扰后参数,并将之量化。在本文中,我们提出了一种新的方法,将估计融合问题转化为决策融合问题,并用决策融合的方法,在最有传感器压缩律下,将传感器的观测压缩成二进制位(bit);而融合中心则将各传感器传到融合中心的二进制位信息融合后得到未知参数的最终估计,最后我们用数值例子说明了我们新方法的有效性。
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