| The core of Precision Agriculture (PA) is to make use of modern information science andtechnology for “Farming on Prescription”, which requires accurate farming information formaximizing the crop yield, increasing resource utilization rate, and minimizing impact on theenvironment with limited agricultural inputs. At the same time, a wireless sensor network (WSN)with sensor nodes spreading throughout the field can periodically collect useful data to informationprocessing center, so monitoring system for agricultural information based on WSN can serve forPA. In a data-centric WSN, in order to transmit information accurately, using channel equalizationtechnology to overcome distortion of signal is a usual method.This paper deeply studies blind channel estimation and equalization in WSN for precisionagriculture. As wireless sensor nodes densely deployed, signals of adjacent sensor nodes within asame cluster can be highly cross-correlated, which is different from signals of traditional wirelesscommunication, this paper proposes to utilize such a property to excogitate suitable blind channelequalization algorithms (Ant Colony Optimization–Cross-correlation Algorithm, ACO-CA)applied in WSN. Firstly, signals from all nodes must be scrambled to be unrelated. Then, AntColony Optimization algorithm is used to blindly estimate scrambled signals and descrambler isused to restore original signals of reference node. At the same time, the transmission channel ofreference node is also estimated. Besides, the paper makes the most of high cross-correlation toestimate the transmission channels within a cluster of other senor nodes. With known information ofthe channels and scrambled codes, all signals can be restored. Simulation results show that thealgorithm is appropriate for WSN, the channel can be corrected to eliminate interference and signalsof all nodes in the cluster can be restored, and algorithm has a good performance in aspects of errorrate, normalized mean square error and so forth. |
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