Airborne SAR Moving Target Signatures and Imagery Based on LVD

Abstract

This paper presents a new ground moving target imaging (GMTIm) algorithm for airborne synthetic aperture radar (SAR) based on a novel time-frequency representation (TFR), Lv's distribution (LVD). We first analyze generic moving target signatures for a multichannel SAR and then derive the analytical spectrum of a point target moving at a constant velocity by a polar format algorithm for SAR image formation. SAR motion deviation from a predetermined flight track is considered to facilitate airborne SAR applications. LVD, as a recently developed TFR for the analysis of multicomponent linear-frequencymodulated signal, is adopted to represent the target kinematic spectrum in the Doppler centroid frequency and chirp rate domain. As a result, the proposed SAR-GMTIm algorithm is capable of imaging multiple moving targets even when they are located at the same range resolution cell. Some practical issues such as imaging maneuvering targets and small/weak targets are discussed to enhance the applicability of the proposed algorithm. Simulation results with isotropic point moving targets are presented to validate the effectiveness and superiority of the proposed algorithm. Raw data collected by an airborne multichannel SAR are also used to verify the performance improvement made by the proposed algorithm.Index Terms-Ground moving target imaging (GMTIm), Lv's distribution (LVD), synthetic aperture radar (SAR), timefrequency representation (TFR).

A Novel Approach of Slope Detection Combined with Lv’s Distribution for Airborne SAR Imagery of Fast Moving Targets

“…Furthermore, the development of radar signal processing technology has provided it with the capability of imaging and positioning of ground moving targets, so that SAR has tracked even more attentions in both civilian and military applications [1][2][3][4][5][6][7][8][9]. Generally, in order to achieve a high azimuth resolution, the SAR system adopts the synthetic aperture technique by installing radar sensor on aerial platforms, which is required to fly on a fixed routine [10]. The SAR sensor monitors ground moving targets by transmitting a large number of broadband pulses, so the reflected pulses is related to the motion parameters, which makes the moving targets unfocused and smeared in SAR images [2].…”

“…Multiple channel algorithms utilize the interferometric phase between different SAR channels to resolve the ambiguity problem related to the target motion parameters. The algorithms, such as Along-Track Interferometry (ATI) [22], Displaced Center Antenna (DPCA) [23,24] and Space-Time Adaptive Processing (STAP) [25,26] are capable of moving target focusing and precise motion parameter acquiring [2,10,[27][28][29][30]. However, these multiple channel SAR algorithms usually suffer from both expensive computing costs and complicated equipment installation.…”

Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network

“…There has been a considerate amount of research in optical imagery understanding focusing on detection of different types of objects, such as roads [3,4], buildings [5,6], oil tanks [7,8], vehicles [9][10][11] and airplanes [12][13][14]. Aside from detecting scattered objects, the classification of scenes also receives a lot of attention recently, such as in [15][16][17], where the objective is to classify image patches into different classes, such as buildings, forest, harbor, etc.…”

Power Transmission Tower Series Extraction in PolSAR Image Based on Time-Frequency Analysis and A-Contrario Theory

“…In addition, there are some models adopted to PTT detection, such as the G0 statistical clustering model [13] and spherically invariant random vector model [14]. However, the results of general preprocessing methods (like multi-look processing and spatial filtering) when using CFAR to detect point targets are not satisfactory under complex ground scenes [15,16]. …”