CAJ | 학술논문

蒙特卡罗定位算法对传感器网络节点进行布置对样本的数量需求很大，且在后验密度过大地区，会导致出现求解速度慢的问题。针对这个问题引进遗传算法，并将其与蒙特卡罗算法进行结合。介绍了蒙特卡罗算法的原理，分析了其在无线传感器定位中的应用，并介绍了无线传感器网络的布置原理，最后提出GAMC定位算法，作为无线传感器定位策略，可以快速完成对节点位置整体布置的任务。
몽특잡라정위산법대전감기망락절점진행포치대양본적수량수구흔대，차재후험밀도과대지구，회도치출현구해속도만적문제。침대저개문제인진유전산법，병장기여몽특잡라산법진행결합。개소료몽특잡라산법적원리，분석료기재무선전감기정위중적응용，병개소료무선전감기망락적포치원리，최후제출GAMC정위산법，작위무선전감기정위책략，가이쾌속완성대절점위치정체포치적임무。
Monte Carlo localization algorithm for sensor network node quantity demand arrangement on the sample is large, and the posterior density is too large, will lead to slow solving speed. To solve this problem using genetic algorithm, and the algorithm is combined with Monte carlo. Introduces the principle of Monte Carlo algorithm, analyzes its applica-tion in the wireless sensor location, and introduces the layout principles of wireless sensor networks, finally proposes the GAMC positioning algorithm, as wireless sensor positioning strategy, can quickly complete the overall layout of the task node position.