scholarly journals Parameter estimation in multi-antenna system based on iterative adaptive approach

2018 ◽  
Vol 232 ◽  
pp. 04064
Author(s):  
Lihua Lei ◽  
Ju Zhou
Keyword(s):  
Parameter Estimation ◽  
Synthetic Aperture Radar ◽  
Estimation Method ◽  
Velocity Estimation ◽  
Motion Parameter ◽  
Synthetic Aperture ◽  
Target Velocity ◽  
Adaptive Approach ◽  
Iterative Adaptive Approach ◽  
Aperture Radar

As one of the main means for remote sensing and detecting, synthetic aperture radar is playing more important role in many fields such as country reconnaissance, ocean observation, environment disaster monitoring and military reconnaissance. The synthetic aperture radar system based on multi-antenna technology can achieve high resolution of still image and estimate the motion parameter. The echo model of target is set up for motion parameter estimation and the performance of system parameter estimation is given according to the moving target velocity estimation method based on iterative adaptive approach.

scholarly journals Two-Dimensional Ship Velocity Estimation Based on KOMPSAT-5 Synthetic Aperture Radar Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1474 ◽  
Author(s):  
Minyoung Back ◽  
Donghan Kim ◽  
Sang-Wan Kim ◽  
Joong-Sun Won
Keyword(s):  
Synthetic Aperture Radar ◽  
Estimation Method ◽  
Velocity Estimation ◽  
Synthetic Aperture ◽  
Minimum Size ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Synthetic Aperture Radar Data ◽  
Mean Square Errors ◽  
Aperture Radar

Continuously accumulating information on vessels and their activities in coastal areas of interest is important for maintaining sustainable fisheries resources and coastal ecosystems. The speed, heading, sizes, and activities of vessels in certain seasons and at certain times of day are useful information for sustainable coastal management. This paper presents a two-dimensional vessel velocity estimation method using the KOMPSAT-5 (K5) X-band synthetic aperture radar (SAR) system and Doppler parameter estimation. The estimation accuracy was evaluated by two field campaigns in 2017 and 2018. The minimum size of the vessel and signal-to-clutter ratio (SCR) for optimum estimation were determined to be 20 m and 7.7 dB, respectively. The squared correlation coefficient R2 for vessel speed and heading angle were 0.89 and 0.97, respectively, and the root-mean-square errors of the speed and heading were 1.09 m/s (2.1 knots) and 17.9°, respectively, based on 19 vessels that satisfied the criteria of minimum size of vessel and SCR. Because the K5 SAR is capable of observing a selected coastal region every day by utilizing various modes, it is feasible to accumulate a large quantity of vessel data for coastal sea for eventual use in building a coastal traffic model.

scholarly journals A Maximum Likelihood Based Nonparametric Iterative Adaptive Method of Synthetic Aperture Radar Tomography and Its Application for Estimating Underlying Topography and Forest Height

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2459 ◽  
Author(s):  
Xing Peng ◽  
Xinwu Li ◽  
Changcheng Wang ◽  
Haiqiang Fu ◽  
Yanan Du
Keyword(s):  
Maximum Likelihood ◽  
Synthetic Aperture Radar ◽  
Structural Parameters ◽  
Synthetic Aperture ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Adaptive Approach ◽  
Forest Height ◽  
Iterative Adaptive Approach ◽  
Aperture Radar

Synthetic aperture radar tomography (TomoSAR) is an important way of obtaining underlying topography and forest height for long-wavelength datasets such as L-band and P-band radar. It is usual to apply nonparametric spectral estimation methods with a large number of snapshots over forest areas. The nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES) can obtain a high resolution; however, it only tends to work well with a small number of snapshots. To overcome this problem, this paper proposes the nonparametric iterative adaptive approach based on maximum likelihood estimation (IAA-ML) for the application over forest areas. IAA-ML can be directly used in forest areas, without any prior information or preprocessing. Moreover, it can work well in the case of a large number of snapshots. In addition, it mainly focuses on the backscattered power around the phase centers, helping to detect their locations. The proposed IAA-ML estimator was tested in simulated experiments and the results confirmed that IAA-ML obtains a higher resolution than IAA-APES. Moreover, six P-band fully polarimetric airborne SAR images were applied to acquire the structural parameters of a forest area. It was found that the results of the HH polarization are suitable for analyzing the ground contribution and the results of the HV polarization are beneficial when studying the canopy contribution. Based on this, the underlying topography and forest height of a test site in Paracou, French Guiana, were estimated. With respect to the Light Detection and Ranging (LiDAR) measurements, the standard deviation of the estimations of the IAA-ML TomoSAR method was 2.11 m for the underlying topography and 2.80 m for the forest height. Furthermore, compared to IAA-APES, IAA-ML obtained a higher resolution and a higher estimation accuracy. In addition, the estimation accuracy of IAA-ML was also slightly higher than that of the SKP-beamforming technique in this case study.

scholarly journals Synthetic Aperture Radar Image Background Clutter Fitting Using SKS + MoM-BasedG0Distribution

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Zhengwei Zhu ◽  
Jianjiang Zhou ◽  
Hongyu Chu
Keyword(s):  
Parameter Estimation ◽  
Synthetic Aperture Radar ◽  
Estimation Method ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Characteristic Functions ◽  
Sar Image ◽  
Method Of Moment ◽  
Distribution Parameter Estimation ◽  
Aperture Radar

G0distribution can accurately model various background clutters in the single-look and multilook synthetic aperture radar (SAR) images and is one of the most important statistic models in the field of SAR image clutter modeling. However, the parameter estimation ofG0distribution is difficult, which greatly limits the application of the distribution. In order to solve the problem, a fast and accurateG0distribution parameter estimation method, which combines second-kind statistics (SKS) technique with Freitas’ method of moment (MoM), is proposed. First we deduce the first and second second-kind characteristic functions ofG0distribution based on Mellin transform, and then the logarithm moments and the logarithm cumulants corresponding to the above-mentioned characteristic functions are derived; finally combined with Freitas’ method of moment, a simple iterative equation which is used for estimating theG0distribution parameters is obtained. Experimental results show that the proposed method has fast estimation speed and high fitting precision for various measured SAR image clutters with different resolutions and different number of looks.

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