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An unsatisfactory property of particle filters is that they
may become inefficient when the observation noise is low.
In this paper we consider a simple-to-implement particle filter,
called ‘LIS-based particle filter’, whose aim is to overcome
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基于粒子滤波实现的纯方位目标跟踪,最后证明该算法收敛-Particle filter based on the realization of the bearings-only target tracking, the final proof of the convergence of the algorithm
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BOT模型的一种跟踪方法,使用粒子滤波实现-BOT model of a tracking method, using the particle filter implementation
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基于粒子滤波实现的纯方位目标跟踪踪,最后证明该算法收敛可直接使用。
-Tracking based on particle filter implementation of the bearings-only target tracking, and finally prove the convergence can be used directly.
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基于多站纯方位被动定位算法仿真程序(分为CA模型和CA模型,经matlab command window运行通过)-Bearings based on multi-station passive location algorithm simulation program (divided into the CA model and CA model, the matlab command window run through)
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扩展Kalman滤波在纯方位目标跟踪中的应用实例-Application of Extended Kalman Filter in Bearings- only Target Tracking
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可以解决纯方位跟踪系统的CKF滤波,针对单个两个平台之间。(It can solve the CKF filtering of bearings only tracking system for single two platforms.)
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