在交互式多模型算法中引入神经网络算法以改进目标跟踪的精度。
The interacting multiple model(IMM) algorithm has been shown to be one of the most effective estimation algorithms in the field of maneuvering target tracking.
该算法使用无源定位的方法确定目标位置，并运用基于多速率运动模型的交互式多模型算法进行滤波。
Thus the IMM-EDKFCS/CV algorithm can ensure the performances on tracking maneuvering targets and /or non-maneuvering targets.
近年来各国研究者对交互式多模型算法的研究重点主要集中于对模型、滤波算法以及数据融合技术等方面的改进。
In recent years, the research points of all researchers in countries mainly centralize on improvement of the model, filtering algorithm and data fusion technology aspects.
针对交互式多模型(IMM)，提出一种自适应跟踪数据率的算法。
An adaptive tracking data rate algorithm based on interactive multi-model (IMM) is proposed.
基于这种思想，提出一种多模态视频检索模型以及相应的手动式搜索和交互式搜索的算法方案。
We propose a multimodal retrieval model in this paper and introduce the corresponding algorithms of manual search and interactive search.
本文提出基于期望系统噪声模型的自适应交互式多模型(IMM)算法。
An interacting multiple model (IMM) adaptive filtering algorithm based on expected system noise model was presented.
针对交互式多模型(IMM)算法中模型集的设计问题，提出一种模型集设计方法。
Focusing on the design of the model set for the interacting multiple model (IMM) algorithm, a model set design method is proposed.
探讨了一种利用单个观测站对机动辐射源目标进行无源定位与跟踪的交互式多模型（IMM）算法。
The Interacting Multiple-Model (IMM) algorithm for passive localization of maneuvering emitter by mono-static is discussed in this paper.
探讨了一种利用单个观测站对机动辐射源目标进行无源定位与跟踪的交互式多模型（IMM）算法。
The Interacting Multiple-Model (IMM) algorithm for passive localization of maneuvering emitter by mono-static is discussed in this paper.
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