基于优化LSTM的雷达威胁预测

根据相关信息对未来时刻的威胁程度进行合理预测,将有利于提前做好各种威胁应对措施,提升电子对抗作战效能。但是目前对雷达威胁度预测的研究还处于起步阶段,针对该问题,提出基于优化长短期记忆网络(Long Short-Term Memory,LSTM)的雷达威胁预测方法。该方法从两个方面对传统的LSTM进行优化:一是通过引入注意力机制对威胁等级影响指标赋予不同的权重,以突出影响威胁度的关键指标;二是使用差分进化(Differential Evolution,DE)算法对引入注意力机制后的LSTM网络超参数进行优化,以提高模型预测性能和预测精度。实验结果表明,该模型的决定系数相比传统LSTM、未经DE算法优化的Attention-LSTM模型分别提高了7.07%和4.57%,能够实现对雷达威胁等级的准确预测。

一种用于雷达资源管理的目标雷达截面积预测算法

在管理与优化雷达有限资源的应用中,目标的雷达截面积的起伏会对资源分配后的效果产生巨大的影响。针对该问题,该文提出一种对目标的雷达截面积进行预测的方法。该方法首先对回波进行处理以获取目标雷达截面积的测量值,进而通过概率密度转移的方法对目标在下一个时刻的雷达截面积进行预测。通过对来自3种飞机的雷达实测数据的计算,验证了该预测方法能够得到较为准确的预测值。最后,建立功率分配的优化方程,并通过仿真验证了对雷达截面积准确的预测能够提高功率分配后的测量精度。

一种雷达故障预测模型建模方法研究

提出了一种雷达故障预测模型建模方法,设计了五类能够精确反映雷达各分系统健康状态的参数类型,并提供了每种特征参数的提取办法。提取出的参数数据作为基于隐式半马尔可夫模型的雷达故障预测模型的输入,通过预测模型训练、退化状态计算与状态预警建模、故障预测建模等过程最终实现雷达故障预测模型的建模。本文方法对于雷达故障预测具有一定的指导意义,有广泛的工程应用前景。

Analysis of radar fault prediction based on combined model

Based on modeling principle of GM（1,1）model and linear regression model,a combined prediction model is established to predict equipment fault by the fitting of two models.The new prediction model takes full advantage of prediction information provided by the two models and improves the prediction precision.Finally,this model is introduced to predict the system fault time according to the output voltages of a certain type of radar transmitter.

Analysis of Physical Quantities and Radar Parameters about Hail Shooting and Heavy Convective Rainfall

[Objective] The paper is to analyze physical quantities and radar parameter of hail shooting and heavy convective rainfall weather. [Method] Using radar data of Jinan station during 2002 and 2008, combined with sounding data, the physical quantities and radar parameter of hail shooting and heavy convective rainfall weather are compared and analyzed. [Result] The smaller Sl is conducive to the generation of hail weather. When K〉 35 ~C, the probability for occurrence of heavy rainfall weather is significantly increased; when K〈20 ^（3, the probability for occurrence of heavy rainfall weather is significantly decreased. When CAPE value is greater than 1 500 J/KG, the probability for occurrence of hail weather is significantly decreased, while the probability for occurrence of heavy rainfall weather is significantly in- creased. The possibility for occurrence of hail monomer is small when the wind shear is less than 5 m/s; and it is large while wind shear is greater than 20 m/s. The radar forecasting indexes of hail monomer is as follows： VIL value reaches 35 kg/m2 （May）, 43 kg/m2 （June and July）, the monomer height is greater than 9 km, the maximum reflectivity factor is larger than 60 dBz, strong center height reaches 3.3 km （May）, 4.3 km （June） and 5.5 km （July）; VlL value of heavy rainfall monomer generally is below 25 kg/m2. [Conclusion] The paper provides basis form prediction of hail and heavy rainfall.

Radar Cross Section Prediction and Reduction for Naval Ships

Radar cross section（RCS） is the measurement of the reflective strength of a target.Reducing the RCS of a naval ship enables its late detection,which is useful for capitalizing on elements of surprise and initiative.Thus,the RCS of a naval ship has become a very important design factor for achieving surprise,initiative,and survivability.Consequently,accurate RCS determination and RCS reduction are of extreme importance for a naval ship.The purpose of this paper is to provide an understanding of the theoretical background and engineering approach to deal with RCS prediction and reduction for naval ships.The importance of RCS,radar fundamentals,RCS basics,RCS prediction methods,and RCS reduction methods for naval ships is also discussed.

Automatic prediction of time to failure of open pit mine slopes based on radar monitoring and inverse velocity method

Radar slope monitoring is now widely used across the world, for example, the slope stability radar(SSR)and the movement and surveying radar(MSR) are currently in use in many mines around the world.However, to fully realize the effectiveness of this radar in notifying mine personnel of an impending slope failure, a method that can confidently predict the time of failure is necessary. The model developed in this study is based on the inverse velocity method pioneered by Fukuzono in 1985. The model named the slope failure prediction model(SFPM) was validated with the displacement data from two slope failures monitored with the MSR. The model was found to be very effective in predicting the time to failure while providing adequate evacuation time once the progressive displacement stage is reached.

Assimilation of High Frequency Radar Data into a Shelf Sea Circulation Model

High Frequency(HF) radar current data is assimilated into a shelf sea circulation model based on optimal interpolation(OI) method. The purpose of this work is to develop a real-time computationally highly efficient assimilation method to improve the forecast of shelf current. Since the true state of the ocean is not known, the specification of background error covariance is arduous. Usually, it is assumed or calculated from an ensemble of model states and is kept in constant. In our method, the spatial covariances of model forecast errors are derived from differences between the adjacent model forecast fields, which serve as the forecast tendencies. The assumption behind this is that forecast errors can resemble forecast tendencies, since variances are large when fields change quickly and small when fields change slowly. The implementation of HF radar data assimilation is found to yield good information for analyses. After assimilation, the root-mean-square error of model decreases significantly. Besides, three assimilation runs with variational observation density are implemented. The comparison of them indicates that the pattern described by observations is much more important than the amount of observations. It is more useful to expand the scope of observations than to increase the spatial interval. From our tests, the spatial interval of observation can be 5 times bigger than that of model grid.

大型集装箱船雷达阴影扇形区域假回波的研究

传统的雷达阴影扇形理论不完全适用于大型集装箱船甲板货箱引起的雷达阴影扇形区域，以及在该区域内假回波的表现，为此对这一特殊现象作了深入研究与讨论，对在雷达阴影扇形区域回波的表现和性质，以及间接回波的识别等方面，提出了自己的见解，在理论和实践上都有参考价值．

灰色累加算子与灰色累减算子的统一及其应用

当前灰色算子阶数取值范围(R^(+))的局限性导致其难以实现对序列差异信息的有效挖掘.本文在传统Gamma函数基础引入参数δ,构建了一种阶数面向实数域R的新型灰色算子,实现了传统灰色累加算子和累减算子的统一.进一步研究表明,阶数通过其极性的正负来判定灰色算子的类型:当阶数r> 0,灰色算子对序列做累加生成;当r <0,灰色算子对序列做累减生成;当r=0,灰色算子对序列不做任何运算.通过三个典型序列验证了新型灰色算子的有效性.最后,基于新型灰色算子构建雷达故障预测的RDGM(1,1)模型,其模型综合精度高达99.5%,实现了对雷达故障发生时点的精准预测.本研究成果解决了阶数取值非负性所导致的灰色算子运算类型单一性的不足,对提高灰色预测模型性能具有积极意义.

AUTOMATICALLY OPERATING RADARS FOR MONITORING INSECT PEST MIGRATIONS

Over the last three decades, special purpose “entomological” radars have contributed much to the development of our understanding of insect migration, especially of the nocturnal migrations at altitudes of up to ～1 km that are regularly undertaken by many important pest species. One of the limitations of early radar studies, the difficulty of maintaining observations over long periods, has recently been overcome by the development of automated units that operate autonomously and transmit summaries of their observations to a base laboratory over the public telephone network. These relatively low cost Insect Monitoring Radars (IMRs) employ a novel “ZLC” configuration that allows high quality data on the migrants' flight parameters and identity to be acquired. Two IMRs are currently operating in the semi arid inland of eastern Australia, in a region where populations of migrant moths (Lepidoptera) and Australian plague locusts Chortoicetes terminifera (Orthoptera) commonly originate, and some examples of outputs from one of these units are presented. IMRs are able to provide the data needed to characterize a migration system, i.e. to estimate the probabilities of migration events occurring in particular directions at particular seasons and in response to particular environmental conditions and cues. They also appear capable of fulfilling a “sentinel” role for pest management organisations, alerting forecasters to major migration events and thus to the likely new locations of potential target populations. Finally, they may be suitable for a more general ecological monitoring role, perhaps especially for quantifying year to year variations in biological productivity.

Assimilating Doppler radar observations with an ensemble Kalman filter for convection-permitting prediction of convective development in a heavy rainfall event during the pre-summer rainy season of South China

This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of convection evolution in a high-impact heavy-rainfall event over coastal areas of South China during the pre-summer rainy season. An ensemble of 40 deterministic forecast experiments(40 DADF) with data assimilation(DA) is conducted, in which the DA starts at the same time but lasts for different time spans(up to 2 h) and with different time intervals of 6, 12, 24, and 30 min. The reference experiment is conducted without DA(NODA).To show more clearly the impact of radar DA on mesoscale convective system(MCS)forecasts, two sets of 60-member ensemble experiments(NODA EF and exp37 EF) are performed using the same 60-member perturbed-ensemble initial fields but with the radar DA being conducted every 6 min in the exp37 EF experiments from 0200 to0400 BST. It is found that the DA experiments generally improve the convection prediction. The 40 DADF experiments can forecast a heavy-rain-producing MCS over land and an MCS over the ocean with high probability, despite slight displacement errors. The exp37 EF improves the probability forecast of inland and offshore MCSs more than does NODA EF. Compared with the experiments using the longer DA time intervals, assimilating the radial-velocity observations at 6-min intervals tends to produce better forecasts. The experiment with the longest DA time span and shortest time interval shows the best performance.However, a shorter DA time interval(e.g., 12 min) or a longer DA time span does not always help. The experiment with the shortest DA time interval and maximum DA window shows the best performance, as it corrects errors in the simulated convection evolution over both the inland and offshore areas. An improved representation of the initial state leads to dynamic and thermodynamic conditions that are more conducive to earlier initiation of the inland MCS and longer maintenance of the offshore MCS.

THE FEASIBILITY OF USING VERTICAL-LOOKING RADAR TO MONITOR THE MIGRATION OF BROWN PLANTHOPPER AND OTHER INSECT PESTS OF RICE IN CHINA

The recent development of automatically operating, inexpensive vertical-looking radar (VLR) for entomological purposes has made it practical to carry out routine, automated monitoring of insect aerial migration throughout the year. In this paper we investigate whether such radars might have a role in monitoring and forecasting schemes designed to improve the management of the Brown Planthopper (BPH), Nilaparvata lugens, and of associated rice pest species in China. A survey of the literature revealed that these insects typically migrate at altitudes between 300 to 2 000 m above ground level, but calculations based on BPH radar scattering cross-sections indicated that the maximum altitude at which they individually produce signals analysable by current VLRs is only～240 m. We also show that coverage over most of the flight altitudes of BPH could be achieved by building a VLR using a wavelength of 8.8 mm instead of the 3.2 cm of existing VLR, but that such a radar would be expensive to build and to operate. We suggest that a more practical solution would be to use a 3.2 cm VLR as a monitor of the aerial movement of the larger species, from which the migration of rice pests in general might be inferred.