Robust adaptive UKF based on SVR for inertial based integrated navigation

Aiming at the problem that the traditional Unscented Kalman Filtering (UKF) algorithm can’t solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers, this paper proposes a robust adaptive UKF algorithm based on Support Vector Regression (SVR). The algorithm combines the advantages of support vector regression with small samples, nonlinear learning ability and online estimation capability of adaptive algorithm based on innovation. Firstly, the SVR model is trained by using the innovation in the sliding window, and the new innovation is monitored. If the deviation between the estimated innovation and the measured innovation exceeds a given threshold, then measured innovation will be replaced by the predicted innovation, and then the processed innovation is used to calculate the measurement noise covariance matrix using the adaptive estimation algorithm. Simulation experiments and measured data experiments show that SVRUKF is significantly better than the traditional UKF, robust UKF and adaptive UKF algorithms for the case where the covariance matrix is unknown and the measured values have outliers.     

An approach for predicting digital material consumption in electronic warfare

Electronic warfare is a modern combat mode, in which predicting digital material consumption is a key for material requirements planning (MRP). In this paper, we introduce an insensitive loss function (ε) and propose a ε-SVR-based prediction approach. First, we quantify values of influencing factors of digital equipments in electronic warfare and a small-sample data on real consumption to form a real combat data set, and preprocess it to construct the sample space. Subsequently, we establish the ε-SVR-based prediction model based on “wartime influencing factors - material consumption” and perform model training. In case study, we give 8 historical battle events with battle damage data and predict 3 representative kinds of digital materials by using the proposed approach. The results illustrate its higher accuracy and more convenience compared with other current approaches. Taking data acquisition controller prediction as an example, our model has better prediction performance (RMSE = 0.575 7, MAPE (%) = 12.037 6 and R² = 0.996 0) compared with BP neural network model (RMSE = 1.272 9, MAPE (%) = 23.577 5 and R² = 0.980 3) and GM (1, 1) model (RMSE = 2.095 0, MAPE (%) = 24.188 0 and R² = 0.946 6). The fact shows that the approach can be used to support decision-making for MRP in electronic warfare.     

基于集对分析和LS-SVM的装备研制风险综合评价

【】为了有效防控武器装备的研制风险,确保武器装备项目的顺利推进,提出一种基于集对分析(SPA)理论与最小二乘支持向量机(LS-SVM)方法的装备研制风险综合评价方法。在该方法中,首先,根据武器装备研制的具体特点,建立了装备研制风险评价指标体系。然后,在此基础上,引入SPA分析理论中的联系度和集对概念构建了训练样本和测试样本。最后,使用获取的样本对LS-SVM进行训练和测试,得到装备研制风险评价模型,并据此给出评价结果。案例分析表明,所提出方法过程简便,定性定量结合,形式易于理解,评价结果也更加贴近实际,对于提升装备研制项目风险管理和决策水平,具有重要的实际意义。     

Democracy for the rescue—of dictators? The role of regime type in civil war interventions

Intrastate conflicts, long eclipsing interstate conflicts, are often internationalized. This paper examines internationalized intrastate conflicts through the types of both the intervening and the embattled regimes. Do democracies, more or less than autocracies, support autocratic governments in their fights against rebels? This paper tests three hypotheses: (1) democracies support autocrats fighting rebels less than autocracies do. (2) Democracies support democratic governments fighting against rebels more than autocracies do. (3) The more democratic two states are, the higher the probability one would support the other’s fight against rebels. Covering all documented external support in intrastate wars (1975–2000), our findings support hypothesis one and two only partly and confirm hypothesis three. However, comparing the two major accounts of the Democratic Peace theory (DPT)—the normative and the structural—our findings corroborate only the former robustly. The paper thus helps enriching the insights of the DPT beyond interstate conflicts.     

机动式保障基地系统防护需求分析

当前，军队工程防护对象以地下工程和野战阵地为主，对于设施设备及军事活动以地表为主的机动式保障基地系统，其毁伤特点规律、防护威胁、评估方法及防护标准等尚无专门研究。本文介绍了机动式保障基地系统的基本概念、主要功能及构成要素，研究了近年来典型军事基地遇袭案例，采用案例分析与数据库研究相结合的方法，分析了机动式保障基地系统的主要威胁及其毁伤元和毁伤评估量，并提出机动式保障基地系统防护需求的构建原则和评估步骤。根据建立的原则和步骤，建立5级防护需求等级，以海外维和基地为例，分析了基地内各类保障装备的防护等级需求和防护等级设置的主要依据，为开展机动式保障基地系统防护体系研究打下基础。     

基于改进支持向量机的航空装备维修安全评估

传统航空装备维修安全评估大多采用定性评估方法,针对传统评估中存在主观性、随意性过强,实践中缺乏可操作性等问题,提出了基于核密度估计的改进支持向量机。构建了航空装备维修安全评估指标体系,在利用层次分析法对其权重进行计算的基础上,通过改进支持向量机对某单位航空装备维修状况进行了全面评估,为航空装备维修安全评估提供了科学的方法,对确保航空装备维修安全具有较强的理论和现实意义。     

支持向量顺序回归机的线性规划算法

支持向量顺序回归机是标准支持向量分类机的一个推广，它是一个凸的二次规划问题。本文根据l1范数与l2范数等价关系和优化问题的对偶原理，把凸的二次规划转化成线性规划。由此提了支持向量顺序回归机的线性规划算法，进一步用数值实验验证了此算法的可行性和有效性。并与支持向量顺序回归机相比，它的运行时间缩短了，而且误差i不超过支持向量顺序回归机；     

防空导弹武器装备备件优化模型

根据防空导弹武器装备使用保障的特点 ,确定了影响备件配置的主要因素。采用装备战备可靠度和经济因素相结合的方法 ,研究并提出了备件优化模型。最后就其实际应用作了说明 ,以便于在实际装备中采用。