基于WGS-84坐标系的空地雷达最小二乘配准算法

空地雷达组网能实现二者的优势互补,特别是增强低空目标的探测跟踪能力,但其前提是实现运动的空基雷达和静止的地基雷达的配准。为贴近实际,在WGS-84(World Geodetic System)坐标系下建立空地雷达异步变数据率观测模型,采用内插外推的方法对空地观测进行时间配准,在此基础上导出适合工程应用的最小二乘配准算法进行空间配准。仿真结果表明了该算法的有效性和可实现性。

空地双基地雷达在导弹精确拦截过程的数值仿真

导弹精确拦截是一种复杂条件下的精确打击,而双基地雷达可以应用在导弹精确拦截系统中,双基地雷达的配置将大大影响整个导弹精确拦截系统的性能。对于双基地雷达,可采用地面双基地雷达、空载双基地雷达,但都有其局限性。在现有方法的基础上研究了空地双基地雷达在导弹精确拦截系统中的应用,对脉冲压缩空地双基地雷达一维距离成像进行了仿真,利用卡尔曼滤波原理建立了导弹的状态方程和测量方程;通过导弹发射初始阶段的地杂波模拟,分析了空地双基地雷达的杂波特性。仿真结果表明,脉冲压缩空地双基地雷达可以应用于导弹拦截系统中,对不同配置的双基地雷达,其杂波频谱分布差异很大,因此需要合理配置雷达的参数。

单/双基地雷达超低空探测研究

为应对超低空突防带来的严重威胁,仿真分析了双基地雷达距离探测精度随双基地角的变化关系,综合考虑了双基地角临近180°的无效探测区域,以及不同单双基地角下的雷达截面积等因素,对单基地雷达、地基双基地雷达和空地双基地雷达在超低空区域对巡航导弹等目标的探测能力进行对比分析。仿真结果表明,目标在大双基地角下雷达截面积会显著升高,空地双基地雷达在超低空区域具有显著的探测优势,可以为现役单基地雷达的超低空补盲雷达设计提供参考。

一种降低新型OFDM数据链误码率的方法

针对空地雷达数据链可靠性的问题,基于单向耦合映像格子模型,提出了基于OCML的新型OFDM数据链模型。上述模型通过利用OCML时空混沌序列对CCSK软扩频进行改进,同时结合编码技术以及调制技术进行仿真。仿真分析显示,基于OCML的新型OFDM数据链模型可以有效改善CCSK中伪随机序列由于自相关性差导致的可靠性差等问题,显著提高系统误比特率性能,且有效提高空地雷达数据链的可靠性。

Application of GPR reverse time migration in tunnel lining cavity imaging

Correctly locating the tunnel lining cavity is extremely important tunnel quality inspection.High-accuracy imaging results are hard to obtain because conventional one-way wave migration is greatly aff ected by lateral velocity change and inclination limitation and because the diff racted wave cannot be accurately returned to the real spatial position of the lining cavity.This paper presents a tunnel lining cavity imaging method based on the groundpenetrating radar(GPR)reverse-time migration(RTM)algorithm.The principle of GPR RTM is described in detail using the electromagnetic wave equation.The finite-difference timedomain method is employed to calculate the backward extrapolation electromagnetic fi elds,and the zero-time imaging condition based on the exploding-reflector concept is used to obtain the RTM results.On this basis,the GPR RTM program is compiled and applied to the simulated and observed GPR data of a typical tunnel lining cavity GPR model and a physical lining cavity model.Comparison of RTM and Kirchhoff migration results reveals that the RTM can better converge the diff racted waves of steel bar and cavity to their true position and have higher resolution and better suppress the eff ect of multiple interference and clutter scattering waves.In addition,comparison of RTM results of diff erent degrees of noise shows that RTM has strong anti-interference ability and can be used for the accurate interpretation of radar profi le in a strong interference environment.

An improved bicubic imaging fitting algorithm for 3D radar detection target

3D ground-penetrating radar has been widely used in urban road underground disease detection due to its nondestructive,efficient,and intuitive results.However,the 3D imaging of the underground target body presents the edge plate phenomenon due to the space between the 3D radar array antennas.Consequently,direct 3D imaging using detection results cannot reflect underground spatial distribution characteristics.Due to the wide-beam polarization of the ground-penetrating radar antenna,the emission of electromagnetic waves with a specific width decreases the strong middle energy on both sides gradually.Therefore,a bicubic high-precision 3D target body slice-imaging fitting algorithm with changing trend characteristics is constructed by combining the subsurface target characteristics with the changing spatial morphology trends.Using the wide-angle polarization antenna’s characteristics in the algorithm to build the trend factor between the measurement lines,the target body change trend and the edge detail portrayal achieve a 3D ground-penetrating radar-detection target high-precision fitting.Compared with other traditional fitting techniques,the fitting error is small.This paper conducts experiments and analyses on GpaMax 3D forward modeling and 3D ground-penetrating measured radar data.The experiments show that the improved bicubic fitting algorithm can eff ectively improve the accuracy of underground target slice imaging and the 3D ground-penetrating radar’s anomaly interpretation.

Automated Building Block Extraction and Building Density Classification Using Aerial Imagery and LiDAR Data

This paper examines the utility of high-resolution airborne RGB orthophotos and LiDAR data for mapping residential land uses within the spatial limits of suburb of Athens, Greece. Modem remote sensors deliver ample information from the AOI （area of interest） for the estimation of 2D indicators or with the inclusion of elevation data 3D indicators for the classification of urban land. In this research, two of these indicators, BCR （building coverage ratio） and FAR （floor area ratio） are automatically evaluated. In the pre-processing step, the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an nDSM （normalized digital surface model） comprised of upsampled elevation data with considerable improvement regarding region filling and ＂straightness＂ of elevation discontinuities. Following this step, a MFNN （multilayer feedforward neural network） is used to classify all pixels of the AOI into building or non-building categories. The information derived from the BCR and FAR building indicators, adapted to landscape characteristics of the test area is used to propose two new indices and an automatic post-classification based on the density of buildings.

Combining spatial modelling and radar to identify and protect avian migratory hot-spots

Migrating landbirds are known to follow coast lines and concentrate on peninsulas prior to crossing water bodies, es- pecially during daylight but also at night, creating enhanced potential collision hazards with man-made objects. Knowing where these avian migration ＂hot-spots＂ occur in time and space is vital to improve flight safety and inform the spatial planning process （e.g. environmental assessments for offshore windfarms）. We developed a simple spatial model to identify avian migration hot- spots in coastal areas based on prevailing migration orientation and coastline features known, from visual and radar observations, to concentrate migrating landbirds around land masses. Regional scale model validation was achieved by combining nocturnal passerine movement data gathered from two tier radar coverage （long-range dual-polarization Doppler weather radar and short- range marine surveillance radar） and standardised bird ringing. Applied on a national scale, the model correctly identified the ten most important Danish coastal hot-spots for spring migrants and predicted the relative numbers of birds that concentrated at each site. These bird numbers corresponded well with historical observational data. Here, we provide a potential framework for the es- tablishment of the first three-dimensional avian airspace sanctuaries, which could contribute to more effective conservation of long-distance migratory birds [Current Zoology 60 （5）： 680-691, 2014].

Bistatic weak target detection method using non-cooperative air surveillance radar

At present, most of the passive radar system researches utilize FM radios, TV broadcasts, navigation satellites,etc. as illuminators. The transmitted signals are not specifically designed radar waveforms. In this work, the frequency agile, phased array air surveillance radar(ASR) is used as the illuminator of opportunity to detect the weak target. The phased array technology can help realize beam agility to track targets from different aspects simultaneously. The frequency agility technology is widely employed in radar system design to increase the ability of anti-jamming and increase the detection probability. While the frequency bandwidth of radar signals is usually wide and the range resolution is high, the range cell migration effect is obvious during the long time integration of non-cooperative bistatic radar. In this context, coherent integration methods are not applicable. In this work, a parametric non-coherent integration algorithm based on task de-interweaving is proposed. Numerical experiments verify that this is effective in weak target detection.