CAJ | 학술논문

由于无线传感器网络（Wireless Sensor Networks，WSNs）资源受限，如何有效利用资源，提高目标辨别的准确度，是WSNs中目标识别系统的研究难题。以隐马尔科夫模型为分类框架，对一个声音传感器阵列节点簇内的目标识别问题进行建模；基于节点信号的空间关联性，改进了子节点Viterbi最大似然序列的计算状态，设置了子节点报送间隔，从而有效地判别局部状态。实验证明，改进后的算法在维持判别正确率的同时降低信息传输量10%以上。
유우무선전감기망락（Wireless Sensor Networks，WSNs）자원수한，여하유효이용자원，제고목표변별적준학도，시WSNs중목표식별계통적연구난제。이은마이과부모형위분류광가，대일개성음전감기진렬절점족내적목표식별문제진행건모；기우절점신호적공간관련성，개진료자절점Viterbi최대사연서렬적계산상태，설치료자절점보송간격，종이유효지판별국부상태。실험증명，개진후적산법재유지판별정학솔적동시강저신식전수량10%이상。
It is challenging to classify multiple targets in wireless sensor networks based on the time-varying and continuous signals. In this paper, Hidden Markov Model is utilized as a framework for classification. The states in the HMM represent various combinations of vehicles of different types. With a sequence of observations, Viterbi algorithm is used at each sensor node to estimate the most likely sequence of states. Simulation results show that it reduce transmission more than 10%while maintaining identification rate.