CAJ | 학술논문

为满足实际雷达系统对高精度和高实时性的要求，提出了一种改进的“当前”统计模型变采样率机动目标跟踪算法。该算法针对“当前”统计模型必须预设加速度极值和机动频率的问题，提出一种加速度方差和机动频率在线同步自适应方法，建立改进的“当前”统计模型机动目标跟踪算法；针对在线自适应方法计算量大的问题，结合采样周期的大小与目标机动特性的关系，引入变采样率方法。仿真结果表明，与传统“当前”统计模型相比，改进的“当前”统计模型机动目标跟踪算法能显著提高对不同机动强度目标的跟踪精度；变采样率方法通过减少采样点数，节省了系统资源，提高了跟踪实时性；所提算法将两者结合，用传统的“当前”统计模型1.5~2倍的平均采样周期得到了更小的位置均方根误差，实现了用单模型方法同时改善跟踪精度和实时性的目的。
위만족실제뢰체계통대고정도화고실시성적요구，제출료일충개진적“당전”통계모형변채양솔궤동목표근종산법。해산법침대“당전”통계모형필수예설가속도겁치화궤동빈솔적문제，제출일충가속도방차화궤동빈솔재선동보자괄응방법，건립개진적“당전”통계모형궤동목표근종산법；침대재선자괄응방법계산량대적문제，결합채양주기적대소여목표궤동특성적관계，인입변채양솔방법。방진결과표명，여전통“당전”통계모형상비，개진적“당전”통계모형궤동목표근종산법능현저제고대불동궤동강도목표적근종정도；변채양솔방법통과감소채양점수，절성료계통자원，제고료근종실시성；소제산법장량자결합，용전통적“당전”통계모형1.5~2배적평균채양주기득도료경소적위치균방근오차，실현료용단모형방법동시개선근종정도화실시성적목적。
To satisfy the demand of high tracking accuracy and real-time ability in actual radar system,an adaptive sampling period algorithm for maneuvering target tracking based on improved current statistical model is designed. In this algorithm,according to the problem that the current statistical model needs to preset the maximum acceleration and the maneuvering frequency,an improved current statistical model fil-tering algorithm is built by introducing the methods of the adaptive acceleration variance and the adaptive maneuvering frequency. For the problem of large calculation amount in the improved current algorithm,a-daptive sampling period algorithm is cited. The adaptive sampling period algorithm is introduced by combi-ning the sampling period with maneuvering performance. The simulation indicates that the improved cur-rent statistical model filtering algorithm can enhance the adaptability of single model method for target tracking significantly,and the adaptive sampling period algorithm can improve real-time ability by saving system resource during decreasing the sampling number;the purposed algorithm which combines the two al-gorithms above,improves the tracking accuracy by using 1. 5~2. 0 times average sampling period,and real-izes the goal of improving the tracking accuracy and real-time ability simultaneously.