动态跳跃点火(DSF)停缸技术节油效果

动态跳跃点火(DSF)为一项全可变停缸技术,发动机的每一个气缸都可以动态地执行停缸操作。其控制逻辑对停缸作出适当搭配,在兼顾NVH的同时,赋予了发动机更广泛的停缸范围,停缸缸数增加,节油能力优于传统停缸技术。基于车辆实测解析评估,一款1.4T轿车改装DSF在中国工况CLTC_P下的节油效果近于怠速起停的收益,例如7%左右。DSF在不同工况下节油效果有差别,数据显示NEDC循环下节油率略优于CLTC_P循环。DSF可以与48 V技术、智能控制技术、米勒循环发动机及均质稀燃发动机技术相配合,达成综合节油,这些组合为汽车厂家满足第五阶段油耗目标值提供了技术选项。

MAXIMUM LIKELIHOOD ESTIMATION FOR INSAR IMAGE CO-REGISTRATION

This paper presents a closed-form robust phase correlation based algorithm for performing image registration to subpixel accuracy.The subpixel translational shift information is directly obtained from the phase of the normalized cross power spectrum by using Maximum Likelihood Estimation(MLE).The proposed algorithm also has slighter time complexity.Experimental results show that the proposed algorithm yields superior registration precision on the Cramér-Rao Bound(CRB) in the presence of aliasing and noise.

WIDE SWATH FMCW SAR DATA PROCESSING IN SQUINT MODE

This paper concentrates on the data processing of Frequency Modulation Continuous Wave(FMCW),Synthetic Aperture Radar(SAR)in the case of wide swath and squint mode.In the mode,the Doppler centroid dramatically varies along slant range compared to conventional pulsed-SAR.This poses a challenge for system design and signal processing since a very large azimuth bandwidth would be introduced.In the paper,we accommodate the Doppler centroid variations with range by an improved spectral-length extension method,where a bulk range shift and updated Doppler centroid variations are introduced to greatly reduce the azimuth aliasing with respective to the existing methods.Moreover,an image formation approach that integrates wave number domain algorithm is presented to focus the raw data of FMCW SAR in the case of wide swath and squint mode.Point target simulation experiment demonstrates the advantages of the presented method.

UNSUPERVISED POLINSAR CLASSIFICATION BASED ON OPTIMAL COHERENCE SET

Aiming to solve the misclassification problems of unsupervised polarimetric Wishart clas- sification algorithm based on Freeman decomposition, an unsupervised Polarimetric Synthetic Aper- ture Radar （SAR） Interferotnery （PolInSAR） classification algorithm based on optimal coherence set parameters is studied and proposed. This algorithm uses the result of Freeman decomposition to divide the image into three basic categories including surface scattering, volume scattering, and double-bounce Then, the PolInSAR optimal coherence set parameters are used to finely divide each of the three basic categories into 9 categories, and the whole image is divided into 27 categories. Because both the Freeman decomposition result and optimal coherence set parameters indicate specific scattering characteristics, the whole image is merged into 16 categories based on physical meaning. At last, the Wishart cluster is employed to obtain the final classification result. To preserve the purity of scattering characteristics, pixels with similar scattering characteristics are restricted to be classified with other pixels. The final classification results effectively resolve the misclassification problem, not only the buildings can be effectively distinguished from vegetation in urban areas, but also the road is well distinguished from grass. In this paper, the E-SAR PolInSAR data of German Aerospace Center （DLR） are used to verify the effectiveness of the algorithm.

HIGH-RESOLUTION AUTOMOBILE FMCW SAR AND SIGNAL PROCESSING

The paper presents a high-resolution automobile Frequency Modulation Continuous Wave Synthetic Aperture Radar(FMCW SAR) named MiniSAR and the procedure of its signal processing.The imaging geometry of automobile SAR is very different from that of airborne SAR,leading to a different data processing method for automobile SAR.Therefore,in the paper,we propose an image formation approach that can well handle the focusing issues of automobile SAR.The effects of the strong reflected signal and the spatial-variant synthetic aperture length are analyzed.The processed results with automobile FMCW SAR read data validate the presented method.

ADAPTIVE MODEL-BASED SCATTERING DECOMPOSITION OF POLARIMETRIC SAR INTERFEROMETRY

In this paper,a new decomposition method is proposed to solve the problems that vegetation component is overestimated and is not sensitive to directional scattering features with traditional polarimetric Synthetic Aperture Radar(SAR)decomposition.It uses a Polarimetric Interferometric Similarity Parameter(PISP)calculated from Polarimetric SAR Interferometry(PolInSAR)datasets to the scattering decomposition.The PISP is proposed to reveal the geometric sensitivity of SAR interferometry.It is defined by three optimized mechanisms obtained from PolInSAR datasets,therefore,it not only relates to the coherent scattering mechanism closely,but also sufficiently uses the phase and amplitude information.The PISP of building is high,and forest’s PISP is low.The proposed method uses the PISP as a judge condition to select different vegetation model adaptively.The decomposition results show the proposed method can effectively solve the vegetation ingredients overestimation problem.In addition,it is sensitive to the directional scattering.

连续热镀锌生产线上的直燃式加热法

本文介绍了在镀锌之前用直燃式无氧化加热法处理带钢过程的主要特征。还将该法同其它镀锌线通常使用的带钢清洗加热的方法作了比较。对直燃式无氧化加热炉的优点和缺点进行分析后得出结论是,它非常适用于中等规模的生产线。

Robust PCA for Ground Moving Target Indication in Wide-Area Surveillance Radar System

Robust PCA has found important applications in many areas,such as video surveillance,face recognition,latent semantic indexing and so on.In this paper,we study its application in ground moving target indication(GMTI)in wide-area surveillance radar system.MTI is the key task in wide-area surveillance radar system.Due to its great importance in future reconnaissance systems,it attracts great interest from scientists.In(Yan et al.in IEEE Geosci.Remote Sens.Lett.,10:617–621,2013),the authors first introduced robust PCA to model the GMTI problem,and demonstrate promising simulation results to verify the advantages over other models.However,the robust PCA model can not fully describe the problem.As pointed out in(Yan et al.in IEEE Geosci.Remote Sens.Lett.,10:617–621,2013),due to the special structure of the sparse matrix(which includes the moving target information),there will be difficulties for the exact extraction of moving targets.This motivates our work in this paper where we will detail the GMTI problem,explore the mathematical properties and discuss how to set up better models to solve the problem.We propose two models,the structured RPCA model and the row-modulus RPCA model,both of which will better fit the problem and take more use of the special structure of the sparse matrix.Simulation results confirm the improvement of the proposed models over the one in(Yan et al.in IEEE Geosci.Remote Sens.Lett.,10:617–621,2013).