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Yang F.,Air Force Command College | Sun J.-B.,Air Force Command College | Lu Y.,Air Force Radar Institute
Xitong Fangzhen Xuebao / Journal of System Simulation | Year: 2012

The target is to study the effectiveness of air defense combat from the standpoint of flow management. Firstly, as one new concept, force flow management was proposed and elaborated. Then, the matter of Exploratory Modeling and Analysis was pulled into the effectiveness and evaluation of force flow management. And at the same time, the effectiveness and evaluation framework of force flow managemen based on EM&A, was also proposed. On the background of operation upon problem, the math model of force flow management was built. A specific analysis way of force flow management based on EM&A and the conclusive suggestion were proposed. The simulation does a whole research on the result which is produced by all kinds of uncertain combat factor. By the simulation, the analysis of air defense combat strategic decision will become more flexible and efficient. The suitability is better, too. © Right.

Feng X.,Air Force Radar Institute | Wang S.,Air Force Radar Institute | Zhu X.,Air Force Radar Institute
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | Year: 2012

It is not easy to establish multiple input multiple output (MIMO) radar statistical test model in non-Gaussian clutter backgrounds when signal parameter is stochastic or unknown. Based on the particle filtering, the proposed method calculated the likelihood function by transforming the integral operation to the sum operation according to the probability distribution function of unknown parameter. And a general MIMO radar target likelihood-ratio detection model is established. This method is available for different kinds of clutter and it resolves the difficulties that it is unable to establish statistical test model in non-Gaussian clutter backgrounds. Taking SG-Alpha stable distribution as the model of non-Gaussian clutter, a likelihood-ratio detection method based on particle filtering is established. Moreover, simulations show that the detection performances of the proposed method are better than the traditional MIMO radar detection method in non-Gaussian clutter backgrounds.

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