There are two main classes of decision fusion methods, namely hard decision fusion (HD) and soft decision fusion (SD), in which the number of bits transmitted by each local sensor to the fusion centre (FC) is always s...
详细信息
There are two main classes of decision fusion methods, namely hard decision fusion (HD) and soft decision fusion (SD), in which the number of bits transmitted by each local sensor to the fusion centre (FC) is always same, namely one bit in HD and n (n >= 2) bits in SD. However, considering that there is always a limit of bandwidth in a distributed detection system, the number of bits sent by each local sensor to the FC does not need to be the same and should be allocated reasonably and suitably. Therefore, this study proposes an optimal bit allocation scheme based on the memetic algorithm, in which the number of bits transmitted by each local sensor could be different. This scheme aims to maximise the detectionprobability under the limit of bandwidth for a detection system with imperfect channels. The overall detection probability objective function about the number of allocated bits is derived. To optimise this objectivefunction, an improved memetic algorithm with two local adjustment strategies, namely non-elite learning local adjustment optimisation strategy and elite greedy local adjustment optimisation strategy, is proposed to allocate the optimal number of bits. Simulation results show the efficiency and effectiveness of the proposed scheme.
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