摘要
为了在观测值有复杂噪声的情况下获得良好的弹道跟踪效果,提出了一种基于小生境粒子群优化粒子滤波算法的弹道跟踪预测方法.根据外弹道理论建立了跟踪状态模型,在贝叶斯框架下通过由观测量获取状态量的后验概率而消除观测噪声带来的影响,得到当前状态的准确信息,在重采样过程中使用了小生境粒子群优化算法,使得重采样过程更高效.最后进行了基于某型弹丸的弹道跟踪效果仿真,在观测量具有高斯噪声和厚尾噪声的情况下,跟踪弹道与预测弹道均与实际弹道吻合得很好,并优于卡尔曼滤波方法.说明本文方法对弹道的预测跟踪是有效的,为弹道跟踪的实际应用提供了一种参考.
To track the ballistic precisely under the condition of observe signal with complex noise, a tracking method based on particle swarm optimization optimize particle filter was proposed. Tracking model was built firstly, and eliminated the effect of noise according to posterior probability of observe value under the bayes frame, and got exact state value, microhabitat particle swarm optimization was used in resample course, made resample course more active. At last, simulation is carried out based on a kind of projectile to track, when ob- serve signal has complex noise of Gaussian and thick tail, the tracking and precision ballistic is fit the real bal- listic well and is better than kalman filter. That means this method is effective to predict track ballistic, and offers a reference for the application of ballistic tracking.
出处
《中北大学学报（自然科学版）》
CAS
北大核心
2014年第5期536-540,共5页
Journal of North University of China(Natural Science Edition)
基金
中北大学校科学基金资助项目
关键词
粒子群优化
粒子滤波
弹道目标
跟踪
particle swarm optimization
particle filter
ballistics target
tracking
