Moving Target Detection and Parameter Estimation via a Modified Imaging STAP with a Large Baseline in Multistatic GEO SAR

With the development trends of multistatic spaceborne synthetic aperture radar (SAR), geosynchronous SAR (GEO SAR) employing several formation-flying small satellites also has great potential for remote sensing. The small satellites can cooperate to acquire multi-channel data for moving target detec...

Full description

Bibliographic Details
Main Authors: Xichao Dong, Chang Cui, Weiming Tian, Yuanhao Li, Melzi Mounir, Cheng Hu
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/13/3/346
id doaj-43ec0ed5155346c78473732976b19878
record_format Article
spelling doaj-43ec0ed5155346c78473732976b198782021-01-21T00:06:17ZengMDPI AGRemote Sensing2072-42922021-01-011334634610.3390/rs13030346Moving Target Detection and Parameter Estimation via a Modified Imaging STAP with a Large Baseline in Multistatic GEO SARXichao Dong0Chang Cui1Weiming Tian2Yuanhao Li3Melzi Mounir4Cheng Hu5School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaDepartment of Geoscience and Remote Sensing, Delft University of Technology, 2628 CN Delft, The NetherlandsSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaWith the development trends of multistatic spaceborne synthetic aperture radar (SAR), geosynchronous SAR (GEO SAR) employing several formation-flying small satellites also has great potential for remote sensing. The small satellites can cooperate to acquire multi-channel data for moving target detection and parameter estimation in strong clutters. However, multistatic GEO SAR has large satellite spacing and a curved trajectory, which induce the near-field effects and channels out of alignment, respectively, bringing about challenges for the spatial adaptive processing. These problems produce a high-order term in the multi-channel slant range model, making the traditional model and adaptive processing method invalid. In this paper, to meet the requirement of SAR focusing, we firstly derive a fourth-order slant range model and a third-order path difference model for multistatic GEO SAR. Secondly, based on the derived model, the principle of stationary phase and series reversion method are utilized to derive the spatial steering vector for a moving target, which is a basis of spatial adaptive processing in the range-Doppler domain. Thirdly, the time-domain match filtering is constructed based on the fourth-order slant range model to image the moving target. Additionally, the moving targets are detected in the image domain. The motion parameter is estimated by iteratively maximizing the output signal to clutter and noise ratio (SCNR) through the range of possible target velocities. Finally, considering that the GEO SAR is still in development, the computer simulations are carried out to verify the effectiveness and evaluate the performance.https://www.mdpi.com/2072-4292/13/3/346multistatic GEO SARmoving target detectionmotion parameter estimationnear-field effectscurved trajectory
collection DOAJ
language English
format Article
sources DOAJ
author Xichao Dong
Chang Cui
Weiming Tian
Yuanhao Li
Melzi Mounir
Cheng Hu
spellingShingle Xichao Dong
Chang Cui
Weiming Tian
Yuanhao Li
Melzi Mounir
Cheng Hu
Moving Target Detection and Parameter Estimation via a Modified Imaging STAP with a Large Baseline in Multistatic GEO SAR
Remote Sensing
multistatic GEO SAR
moving target detection
motion parameter estimation
near-field effects
curved trajectory
author_facet Xichao Dong
Chang Cui
Weiming Tian
Yuanhao Li
Melzi Mounir
Cheng Hu
author_sort Xichao Dong
title Moving Target Detection and Parameter Estimation via a Modified Imaging STAP with a Large Baseline in Multistatic GEO SAR
title_short Moving Target Detection and Parameter Estimation via a Modified Imaging STAP with a Large Baseline in Multistatic GEO SAR
title_full Moving Target Detection and Parameter Estimation via a Modified Imaging STAP with a Large Baseline in Multistatic GEO SAR
title_fullStr Moving Target Detection and Parameter Estimation via a Modified Imaging STAP with a Large Baseline in Multistatic GEO SAR
title_full_unstemmed Moving Target Detection and Parameter Estimation via a Modified Imaging STAP with a Large Baseline in Multistatic GEO SAR
title_sort moving target detection and parameter estimation via a modified imaging stap with a large baseline in multistatic geo sar
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2021-01-01
description With the development trends of multistatic spaceborne synthetic aperture radar (SAR), geosynchronous SAR (GEO SAR) employing several formation-flying small satellites also has great potential for remote sensing. The small satellites can cooperate to acquire multi-channel data for moving target detection and parameter estimation in strong clutters. However, multistatic GEO SAR has large satellite spacing and a curved trajectory, which induce the near-field effects and channels out of alignment, respectively, bringing about challenges for the spatial adaptive processing. These problems produce a high-order term in the multi-channel slant range model, making the traditional model and adaptive processing method invalid. In this paper, to meet the requirement of SAR focusing, we firstly derive a fourth-order slant range model and a third-order path difference model for multistatic GEO SAR. Secondly, based on the derived model, the principle of stationary phase and series reversion method are utilized to derive the spatial steering vector for a moving target, which is a basis of spatial adaptive processing in the range-Doppler domain. Thirdly, the time-domain match filtering is constructed based on the fourth-order slant range model to image the moving target. Additionally, the moving targets are detected in the image domain. The motion parameter is estimated by iteratively maximizing the output signal to clutter and noise ratio (SCNR) through the range of possible target velocities. Finally, considering that the GEO SAR is still in development, the computer simulations are carried out to verify the effectiveness and evaluate the performance.
topic multistatic GEO SAR
moving target detection
motion parameter estimation
near-field effects
curved trajectory
url https://www.mdpi.com/2072-4292/13/3/346
work_keys_str_mv AT xichaodong movingtargetdetectionandparameterestimationviaamodifiedimagingstapwithalargebaselineinmultistaticgeosar
AT changcui movingtargetdetectionandparameterestimationviaamodifiedimagingstapwithalargebaselineinmultistaticgeosar
AT weimingtian movingtargetdetectionandparameterestimationviaamodifiedimagingstapwithalargebaselineinmultistaticgeosar
AT yuanhaoli movingtargetdetectionandparameterestimationviaamodifiedimagingstapwithalargebaselineinmultistaticgeosar
AT melzimounir movingtargetdetectionandparameterestimationviaamodifiedimagingstapwithalargebaselineinmultistaticgeosar
AT chenghu movingtargetdetectionandparameterestimationviaamodifiedimagingstapwithalargebaselineinmultistaticgeosar
_version_ 1724330230911336448