Inverse synthetic aperture radar (ISAR) increases the target rotation angle to improve the azimuth resolution. Since the echo data points are fan-shaped in wave number domain, the effective interpolation region is broadened in azimuth direction and compressed in range direction while the target rotation angle increases in the process of Polar Format Algorithm (PFA). This gives rise to the reduction of range wave number bandwidth, which causes the range resolution loss. In this paper, an imaging algorithm based on the varying-parameter method is proposed to eliminate the change of range wave number caused by the rotation angle. This method makes the echo data points wedge distributed in the wave number domain by adjusting radar platform parameters in azimuth direction at each azimuth sampling point. Thus, the compression of range wave number width is effectively reduced. Finally, the simulations are performed to verify that the varying-parameter method can restrain the deterioration of range resolution to improve the quality of image.
Means of synthetic aperture radar (SAR) images represent the radiation densities of scenes, and the preservation of means is significant in speckle denoising for the application of SAR images. We provide an improved scheme of the minimum biased diffusion (MinBAD) algorithm for speckle denoising using partial differential equations. Considering the characteristics of SAR speckle and the radiation accuracy for postprocessing needs, several improvements such as normalization, homomorphic transformation, and average-preserving processing are introduced into the MinBAD algorithm. Besides the equivalent number of looks and edge preserving index, a new index, radiation accuracy error, is defined to evaluate the denoising effect. Experimental results for both artificial images and real SAR images are used to validate the performance of the proposed unbiased-average MinBAD speckle reducing approach.
Multiple-input multiple-output (MIMO) radar utilizes the flexible configuration of transmitting and receiving antennas to construct images of target scenes. Because of the target scenes' sparsity, the compressive sensing (CS) technique can be used to realize a feasible reconstruction of the target scenes from undersampling data. This paper presents the signal model of MIMO radar and derive the corresponding CS measurement matrix, which shows success of the CS technique. Also the basis pursuit method and total-variation minimization method are adopted for different scenes' recovery. Numerical simulations are provided to illustrate the validity of reconstruction for one dimensional and two dimensional scenes.
Oil tanks are one of the most important targets in remote sensing. Oil tank detection using optical images has been developed in recent years, but few methods have been studied for oil tank detection in synthetic aperture radar (SAR) images. Optical methods suffer incorrect assessments or false alarms when they are applied in SAR imagery. A method that combines the quasi-circular shadows and highlighting arcs is proposed to detect oil tanks with higher precision and lower false alarm. In general, a highlighting arc caused by the double reflection exists exactly at the bottom of each cylinder tank in a SAR image, so it can be employed to detect the oil tanks. However, it is very difficult to detect those arcs directly. Additionally, each cylinder tank has a quasi-circular shadow area in SAR image, which is near the highlighting arc and is easy to be detected. Cylinder tank can be detected by taking advantages of a corresponding quasi-circular shadow area in SAR image, instead of detecting a highlighting arc directly. This research proposes to detect the quasi-circular shadow first, then find the strong scattering point around the shadow areas, and finally shift the edge of the detected circle to its corresponding strong scattering point. This leads to low false alarm oil tank detection in SAR imagery. Analysis of TerraSAR-X images allows a limited validation of the method proposed.
A general synthetic aperture radar (SAR) signal model based on the Maxwell's equations is derived, and three approximations are discussed for engineering applications. Based on this signal model, a novel operation of SAR, called outer circular synthetic aperture radar (Outer-CSAR), is investigated for wide observation. The Outer-CSAR works similarly to the general circular SAR, but the beam of the SAR antenna points at the outer of the circle instead of the inner. The signal model and imaging algorithm are presented for the Outer-CSAR, and furthermore, simulation is given to validate the signal model and imaging algorithm.