3D motion and geometric information system of single-antenna radar based on incomplete 1D range data

A 3D motion and geometric information system of single-antenna radar is proposed,which can be supported by spotlight synthetic aperture radar（SAR） system and inverse SAR（ISAR） system involving relative 3D motion of the rigid target.In this system,applying the geometry invariance of the rigid target,the unknown 3D shape and motion of the radar target can be reconstructed from the 1D range data of some scatterers extracted from the high-resolution range image.Compared with the current 1D-to-3D algorithm,in the proposed algorithm,the requirement of the 1D range data is expanded to incomplete formation involving large angular motion of the target and hence,the quantity of the scatterers and the abundance of 3D motion are enriched.Furthermore,with the three selected affine coordinates fixed,the multi-solution problem of the reconstruction is solved and the technique of nonlinear optimization can be successfully utilized in the system.Two simulations are implemented which verify the higher robustness of the system and the better performance of the 3D reconstruction for the radar target with unknown relative motion.

A preliminary study of level 1A data processing of a low-low satellite to satellite tracking mission

With the Gravity Recovery and Climate Experiment {GRACE） mission as the prime example, an overview is given on the management and processing of Level IA data of a low-low satellite to satellite tracking mission. To illustrate the underlying principle and algorithm, a detailed study is made on the K-band ranging （KBR） assembly, which includes the measurement principles, modeling of noises, the generation of Level 1A data from that of Level 0 as well as Level IA to Level IB data processing.

Monthly gravity field solution from GRACE range measurements using modified short arc approach

In this paper we present a series of monthly gravity field solutions from Gravity Recovery and Climate Experiment（GRACE） range measurements using modified short arc approach,in which the ambiguity of range measurements is eliminated via differentiating two adjacent range measurements.The data used for developing our monthly gravity field model are same as Tongji-GRACEOl model except that the range measurements are used to replace the range rate measurements,and our model is truncated to degree and order 60,spanning Jan.2004 to Dec.2010 also same as Tongji-GRACE01 model.Based on the comparison results of the C_（2,0）,C_（2,1）,S_（2,1）,and C_（15,15）,S_（15,15）,time series and the global mass change signals as well as the mass change time series in Amazon area of our model with those of Tongji-GRACE01 model,we can conclude that our monthly gravity field model is comparable with Tongji-GRACE01 monthly model.

Range Information Propagation Transform

A novel method of model-based object recognition is presented in this paper. Its novelty stems from the fact that the gray level image captured by a camera is merged with sparse range information in an active manner. By using a projective transform,which is determined by the sparse range data, features (e.g. edge points) related to a single planar surface patch or figure in the scene can be assigned with their corresponding range values respectively. As a result, the shape of the very planar patch or figure can be recovered and various kinds of description in the Euclidean space can be calculated. Based on these descriptions values, the hypothesis about the identification of the object and its pose in space can be obtained with a high probability of success, and a high efficiency of hypothesis- verification process can be expected. Another advantage of this method is that the edge detection process can be navigated to the proper location hinted by the sparse range image. In consequence edge features can be extracted even in the regions with low contrast. In this paper the principle of range information propagation transform (RIPT) is explained, and some implementation issues, such as the algorithms using calibrated or uncalibrated gray level image for object recognition, are discussed. The preliminary experimental results are presented to indicate the effectiveness and efficiency of the proposed method.