Quality Analysis of Early Warning Radar Intelligence Based on Asymmetrical Proximity
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摘要:
针对预警雷达情报质量评估中，模糊因素影响评估有效性的问题，该文提出一种基于非对称贴近度的预警雷达情报质量分析评估方法。分析预警雷达情报获取、传输和作战运用环境特点，从及时性、准确性、完整性、连续性和客观性等6个方面构建其质量评估指标体系，进而建立因素集、评语集和权重集，利用非对称贴近度对其进行模糊综合评估。该研究方法和结论可有效分析评判预警雷达情报质量，也能帮助发现影响情报质量优劣的瓶颈与短板，还可为解决复杂作战环境下预警雷达情报质量评估与分析等问题研究提供一定借鉴。
Abstract:Considering the limits of fuzzy comprehensive evaluation on quality of early warning radar intelligence in actual training, a method of quality evaluation on radar intelligence based on the theory of asymmetric proximity and multilevel fuzzy comprehensive evaluation is proposed. Through the analysis of the producing, transmission, using environmental factors of early warning radar intelligence, the evaluating metric of quality evaluation on radar intelligence integrated for six classes, that are timely, accuracy, completeness, continuity, objectiveness and so on, and then factor set, weight set, and comment set are established, and the quality of the radar intelligence based on the asymmetric proximity with the fuzzy comprehensive evaluation is carried out. This researching methods and results not only can take comprehensive evaluations of a certain quality of radar intelligence, help for finding out the factors to determine the quality of the radar intelligence, but also can fight for providing certain reference to solve complex environment of radar intelligence of operational effectiveness evaluation problem.
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表 1 雷达情报质量评价指标体系
影响因素子集 影响因素 及时性${F_1}$ 搜索发现${F_{11}}$ 报知速度${F_{12}}$ 准确性${F_2}$ 方位距离误差${F_{21}}$ 高度误差${F_{22}}$ 速度误差${F_{23}}$ 判断准确性${F_{24}}$ 错批率${F_{25}}$ 混批率${F_{26}}$ 连续性${F_3}$ 报知密度${F_{31}}$ 航迹漏点率${F_{32}}$ 完整性${F_4}$ 要素完整率${F_{41}}$ 目标掌握率${F_{42}}$ 客观性${F_5}$ 情报要素客观${F_{51}}$ 情报来源客观${F_{52}}$ 错漏压情${F_6}$ 错情${F_{61}}$ 漏情${F_{62}}$ 压情${F_{63}}$ 
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表 2 1～9标度法评价标准
标度 相对比较 1 因素A, B同等重要 2 1, 3标度之间的状态 3 A因素比B因素重要一点 4 3, 5标度之间的状态 5 A因素比B因素重要较多 6 5, 7标度之间的状态 7 A因素比B因素重要很多 8 7, 9标度之间的状态 9 A因素比B因素绝对重要
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表 3 影响因素评价结果统计表
优秀 良好 合格 不合格 一般事故 严重事故 ${F_{11}}$ 9 10 8 3 0 0 ${F_{12}}$ 11 8 9 2 0 0 ${F_{21}}$ 12 9 7 1 1 0 ${F_{22}}$ 11 8 9 2 0 0 ${F_{23}}$ 7 11 9 2 1 0 ${F_{24}}$ 5 12 11 2 0 0 ${F_{25}}$ 10 10 10 0 0 0 ${F_{26}}$ 11 9 8 2 0 0 ${F_{31}}$ 7 9 11 2 1 0 ${F_{32}}$ 7 11 11 1 0 0 ${F_{41}}$ 9 9 11 1 0 0 ${F_{42}}$ 6 11 10 2 1 0 ${F_{51}}$ 12 9 9 0 0 0 ${F_{52}}$ 7 10 10 2 1 0 ${F_{61}}$ 9 9 11 1 0 0 ${F_{62}}$ 9 10 11 0 0 0 ${F_{63}}$ 9 10 10 1 0 0
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