CAJ | 학술논문

针对封锁雷在无人值守状态下如何有效防止敌方排雷动作，延长自身对目标区域的封锁控制时间的问题，采用三轴 MEMS 加速度传感器构建一种新型的封锁雷智能防排系统。设计试验采集封锁雷静止姿态和运动状态特征信号，采用 SMA 和 DSVM 算法对特征信号进行分析，提取特征参数，设计二叉决策树特征分类器，利用特征信号对分类器进行参数训练。试验结果表明，建立的封锁雷智能防排系统针对5种静止姿态和3种运动状态具有较好的识别率，所建系统有效可行。
침대봉쇄뢰재무인치수상태하여하유효방지활방배뢰동작，연장자신대목표구역적봉쇄공제시간적문제，채용삼축 MEMS 가속도전감기구건일충신형적봉쇄뢰지능방배계통。설계시험채집봉쇄뢰정지자태화운동상태특정신호，채용 SMA 화 DSVM 산법대특정신호진행분석，제취특정삼수，설계이차결책수특정분류기，이용특정신호대분류기진행삼수훈련。시험결과표명，건립적봉쇄뢰지능방배계통침대5충정지자태화3충운동상태구유교호적식별솔，소건계통유효가행。
In order to prevent the blockage mine from being eliminated by the enemy in the unattended operation state and to postpone the work time in the target area,a novel intelligent anti removal system for blockage mines based on a tri-axis MEMS accelerometer was established. An experiment was designed to acquire the characteristic signal of the blockage mine including the static posture and the dynamic state. The SMA and DSVM algorithms were introduced to analyze the characteristic signal and abstract the character parameters. A binary decision tree charac-ter classifier was designed based on these parameters and the parameter of the classifier was trained from the charac-teristic signal. The verification experiment results show that the recognition accuracy of the blockage mine′s five static postures and three dynamic processes is relatively high and as a result the established system is effective and feasible.