采用目标假设的三被动式传感器数据关联算法

被动式传感器目标跟踪是多传感器多目标跟踪领域的一个重要研究方向。针对三被动式传感器多目标定位系统全局最优数据关联的三维分配问题，在允许传感器漏检和虚警的情况下，通过分析拉格朗日松弛算法，提出一种假定真实目标的快速收敛算法。该算法通过粗关联假定真实目标并重新修改代价矩阵，然后进行细关联，使得拉格朗日松弛算法在保证关联精度的前提下有效地提高了收敛速度。理论分析和实验结果表明，该算法提高了数据关联的速度,并在一定程度上提高了关联准确率。     

Target‐Setting,Pay for Performance,and Quality Improvement: A Case Study of Ontario Hospitals’ Quality‐Improvement Plans

This study examines whether difficult targets and quality indicators in executives' pay‐for‐performance (P4P) plans affect performance. The impact of target‐setting and P4P plans on quality improvement in the public sector is unclear. The Ontario government initiated the Quality Improvement Plan (QIP), which requires hospitals to set targets for quality indicators annually and link executive pay to target achievement since 2011. Analyzing Health Quality Ontario's database and hospitals' 2012–2013 QIPs, this study shows greater quality improvement in hospitals with difficult targets than hospitals with easy targets or without assigned targets; however, the positive impact disappears for high‐performance hospitals relative to their peers. We find no significant effect of the use of quality indicators in executives' P4P plans on quality improvement. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.     

一种经验模式分解下的海杂波小目标检测方法

利用海杂波有效探测海上小目标是目前雷达探测领域的热点问题，具有重要的应用价值。鉴于海杂波是一种非线性非平稳性的雷达回波信号，充分发挥整体平均经验模式分解的优势，将海杂波分解为若干个不同尺度的独立分量。通过研究发现有目标时，分解出的前5个分量与未分解前信号的相关系数明显减小，因此提出了一种新的海杂波背景下的目标检测方法。通过实测和模拟的海杂波数据进行训练和测试，研究结果表明，该方法能有效地实现海杂波下目标的探测，性能优于经典时域下、分数阶傅里叶变换域下以及平均经验模式分解后的广义Hurst指数的目标检测方法。     

采用航迹特征的智能机型识别

针对空战目标识别中机型自动识别比较困难的问题，提出了采用航迹特征的智能目标识别方法。利用卷积神经网络（Convolutional Neural Network，CNN）分层学习特征的能力，训练CNN算法模型自动地从航迹数据中学习有用的特征并分类。利用沿海实地采集的15个类别的飞机航迹数据，经一系列数据预处理后作为智能识别算法的训练和测试数据，在验证实验中描述了算法网络的相关配置,对比了CNN与其他分类器的识别结果。实验结果表明，CNN具有很好的识别性能。     

基于多重交叉残差方法的空间目标旋转特性分析

针对传统的频谱分析和自相关函数反演周期时常出现虚假周期的问题，提出了多重交叉残差法处理空间旋转目标雷达散射截面(Radar Cross Section，RCS)的时间序列。以空间跟踪和监视系统(Space Tracking and Surveillance System,STSS)卫星为例，仿真了空间目标失稳旋转状态，对比了频谱分析、自相关函数和多重交叉残差3种方法的处理结果，发现当目标存在进动运动时多重交叉残差法抗干扰能力更强。在此基础上，进一步研究了不同章动角时多重交叉残差法对旋转周期的反演效果，结果表明章动角大小会影响旋转周期的反演准确度，且章动角达到一定数值后，卫星进动幅度过大导致RCS序列周期性模糊，无法用多重交叉残差法反演出旋转周期。     

Effectiveness of harvest strategies in achieving multiple management objectives in a multispecies fishery

Fisheries management is characterised by multiple objectives, some of which may be complementary, while others may require trade‐offs between outcomes. Balancing these objectives is made more complex in the case of multispecies and multigear fisheries. In this paper, we develop a bioeconomic model that captures the key elements of such a fishery to test a range of potential harvest strategies to provide insights into how economic target reference points could lead to both desirable and undesirable management outcomes (e.g. discards). The model is developed as a long‐run optimisation model to identify target reference points to achieve multispecies maximum economic yield, and a dynamic recursive optimisation model, which includes more realistic representation of fishers’ behaviour, such as discards and trading of under‐caught species quotas. The potential economic, social and ecological impacts are evaluated using data envelopment analysis (DEA). The results suggest that the use of proxy target reference points can result in short‐term economic benefits at the cost of slower stock recovery and higher discarding. Limiting the number of species subject to quota controls may also prove beneficial in multispecies fisheries, while ensuring quota markets are efficient is likely to produce benefits irrespective of the harvest strategy adopted.     

一种基于预测值量测转换的卡尔曼滤波跟踪算法

在雷达目标跟踪中，系统量测信息通常在球坐标系下获得。为了采用经典卡尔曼滤波算法实现有效目标跟踪，通常采用量测转换方法将非线性量测信息转换到直角坐标系中。针对传统量测转换方法基于量测值计算转换误差统计特性而导致的估计结果有偏问题，提出了一种基于预测值的量测转换方法，并将其与卡尔曼滤波算法相结合，获得了一种基于预测值量测转换的卡尔曼滤波跟踪算法。仿真结果表明，与现有的基于量测转换的卡尔曼滤波算法相比，该算法能在不提高运算量的情况下有效改善目标跟踪效果，跟踪精度提升约20%。     

空间目标的联合矩阵束极点估计

极点作为谐振区雷达目标的最主要电磁特性，具有很大的空间目标识别前景。但是在使用频域观测数据提取目标极点时，存在虚假极点和估计精度问题。针对上述问题，首先通过加窗和截断改进了后时响应信号的获取流程，抑制了虚假极点影响；然后采用最小描述长度法（MDL）估计极点个数，避免了定阶错误引起的虚假极点；最后采用联合矩阵束法估计极点，有效减少了单角度数据估计极点时存在的遗漏极点和虚假极点问题。FEKO软件验证表明，所提算法具有更高的精度和抗噪能力。     

Target price forecasts: The roles of the 52-week high price and recent investor sentiment

This paper reveals that in addition to fundamental factors, the 52-week high price and recent investor sentiment play an important role in analysts’ target price formation. Analysts’ forecasts of short-term earnings and long-term earnings growth are shown to be important explanatory variables for target prices; equally, the 52-week high price and recent investor sentiment are also shown to explain target price levels and especially target price biases. Our analysis additionally reveals that analysts place greater weight on these two non-fundamental factors in settings with greater task complexity and to some extent in those with greater resource constraints. Conversely, on balance, the results suggest that this increased reliance does not translate into an increased impact per unit of each non-fundamental factor on forecast bias. Finally, our results show that target prices are useful in predicting future stock returns beyond earnings forecasts and commonly used risk proxies. However, in an internally consistent fashion, the informativeness of target prices for future returns is significantly reduced when greater weight is placed on either the 52-week high or recent investor sentiment in the target price formation process.     

管理层收购与政府规制的关系分析

文章从我国管理层收购存在的问题、政府规制管理层收购的现状等入手,进一步阐述管理层收购与政府规制之间的关系,分析政府时管理层收购规制的目标以及政府规制管理层收购的转变。