跳频信号的归一化多普勒频差最大似然估计

针对双站定位涉及的跳频信号多普勒频差估计问题，提出了归一化的频差最大似然估计算法，利用两个定位站接收到的跳频脉冲串信号，构建一个关于基准跳频频率多普勒频差的似然函数，通过网格搜索得到使似然函数最大的多普勒频差估计，既解决了跳频信号在不同跳频频率上多普勒频差不一致的问题，又充分利用在不同频率的脉冲串信号提高了多普勒频差估计精度。通过仿真对算法的性能进行了评估，结果表明，与基于子空间的算法比较，在脉冲数达到240个时，所提算法执行效率提升30%以上。     

Supply‐ versus Demand‐Side Policies in the Presence of Carbon Leakage and the Green Paradox

We consider a climate coalition that seeks to reduce global emissions in the presence of carbon leakage and resource exhaustibility. We show that a credible announcement of future unilateral supply‐side policies delays foreign emissions, and we derive the optimal combination of consumer taxes and producer taxes when we consider leakages from free riders, both within periods and across periods. The tax shares generally differ over time. A decline in the present value of the social cost of carbon over time supports a time path where the consumers’ tax share of the total carbon tax also declines over time. We illustrate our findings with a numerical model.     

广义空间调制系统的正则化OMP检测算法

在广义空间调制（GSM）系统中，最大似然（ML）检测可以取得最优的检测性能，然而其计算复杂度随激活天线数的增加急剧增长。针对这一问题，提出了一种基于稀疏重构理论的低复杂度检测算法——正则化正交匹配追踪（ROMP）算法。该算法首先根据信道矩阵和当前残差的内积选取多个候选激活天线索引，接着对候选天线索引按正则化标准进行可靠性验证，剔除错误索引，缩小信号的搜索空间，最后通过求解最小二乘问题估计信号。仿真结果表明，与经典的正交匹配追踪（OMP）算法相比，所提算法以少许复杂度的增加为代价极大提升了检测性能，能够在检测性能与复杂度之间取得更好的折中。     

Private information in currency markets

Using daily abnormal currency returns for the universe of countries with flexible exchange rates, we show local currency depreciations ahead of unscheduled, public sovereign debt downgrade announcements. Consistent with the private information hypothesis, the effect is stronger in lower institutional quality countries and holds after we control for concurrent public information and for publicly available rumors about the forthcoming downgrades. Our results persist when abnormal currency returns are adjusted for global carry and dollar risk factors, world equity and bond returns, as well as local stock market returns. Finally, the currency depreciations are permanent, providing evidence for a link between fundamentals and currency markets.     

利用相位差变化趋势的多源信号关联

针对多源信号关联难度大、关联准确率低的问题，利用同一目标平台上搭载的多个辐射源信号具有一致的动态变化趋势这一特点，提出一种利用相位差特征的多源信号关联方法。已知阵列形式和侦测点位置，通过对侦收到的多个辐射源信号进行处理，获取相位差信息并累积形成相位差变化趋势；对比提取疑似相似段，根据相似性判决策略进行有效关联。该方法能有效避免不同传感器对位置测量的误差影响，相比传统基于位置信息关联的方法，能有效提升关联准确率。仿真和实测数据分析证明了该方法的有效性，在阵列形式和侦测点位置已知的条件下该方法具有重要工程意义。     

Energy efficiency promotion backfires under cap-and-trade

Major climate-cum-energy policies and respective impact projections rest on the widespread belief that increased energy efficiency can be equated with savings in energy use and emissions. This belief is flawed. Due to the rebound effect emissions savings from energy efficiency improvements will be generally less than what is technically feasible, or even be reversed. By means of an analytical general equilibrium model we demonstrate the latter to be true in a case that is both stark and relevant: if electricity generation is subject to a cap-and-trade scheme with partial coverage, increased efficiency of electric devices leads unambiguously to increased carbon emissions. The result implies that a proper distinction between the energy rebound and the carbon rebound is warranted, and that public policy must carefully consider the interactions between energy efficiency promotion and carbon pricing.     

Information leakage of ADRs Prior to company issued guidance

We examine whether the degree of selective disclosure (as measured by information leakage prior to managerial earnings guidance) of foreign firms with ADRs is different from that of U.S. firms. We find that there is no variation of leakage before earning guidance between ADRs and U.S. firms. This result is consistent with the prediction that, despite being exempt from Regulation FD, ADRs have sufficient incentives to avoid selective disclosure even without the regulatory enforcement. In addition, we attempt to determine whether the variation in selective disclosure among the foreign firms with ADRs is conditioned on the respective home country transparency and governance standards. We find that country-specific characteristics, such as corruption perception, economic freedom and the legal system origin, explain the variation in information leakage of ADRs.     

基于深度学习的跳频信号识别

针对跳频信号分选存在人工提取参数特征具有复杂性的问题，提出了一种基于深度学习的识别方法。首先对跳频信号进行短时傅里叶变换，得到二维的时频矩阵；接着提取信号的轮廓特征，构造三维矩阵作等高线图，并对等高线图进行预处理；最后把预处理后的等高线图输入到卷积神经网络中进行训练、测试，进而实现分类识别。仿真结果表明，在不需要复杂的人工提取参数特征的基础上，在分选率为100〖WT《Times New Roman》〗%〖WTBZ〗时，所提方法经裁剪处理下的信噪比为-15 dB，比支持向量机和传统K-Means聚类算法都低10 dB。实测数据的算法验证表明，所提方法能够将大疆精灵4Pro、hm无人机、司马航模X8HW以及大疆悟2这四类无人机正确分类。     

基于软件无线电平台的1090ES扩容信号收发处理

随着空中交通的信息传输与新应用拓展需求不断增加，1090ES信号容量有限问题日益显现，对1090ES信号进行容量扩充是一种有效解决途径。针对一种基于脉内调相的1090ES信号扩容技术，给出了叠加调制的信号模型，研究了1090ES信号的解调解码算法；在GNU Radio+ USRP软件无线电平台上，实现了1090ES扩容信号的产生和接收处理，包括脉冲位置调制（Pulse Position Modulation，PPM）与8PSK叠加调制、低通滤波、报头检测、载波同步、PPM解码和相位解调等，并利用Matlab对实验的解码错误率和信号的频谱特性做了统计分析与仿真，结果表明了该扩容技术的可行性。     

Correlated daily time series and forecasting in the M4 competition

We participated in the M4 competition for time series forecasting and here describe our methods for forecasting daily time series. We used an ensemble of five statistical forecasting methods and a method that we refer to as the correlator. Our retrospective analysis using the ground truth values published by the M4 organisers after the competition demonstrates that the correlator was responsible for most of our gains over the naïve constant forecasting method. We identify data leakage as one reason for its success, due partly to test data selected from different time intervals, and partly to quality issues with the original time series. We suggest that future forecasting competitions should provide actual dates for the time series so that some of these leakages could be avoided by participants.