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The multitemporal interferometric synthetic aperture radar (InSAR) technique based on distributed scatterers (DSs) has been widely applied in high-precision deformation measurements, which compensates for the drawback that the persistent scatterer InSAR technique does not obtain sufficient monitoring points, especially in rural areas. Considering that DS pixels are susceptible to various decorrelation factors, it is necessary to retrieve the optimal phase series by phase optimization algorithms (POAs). However, conventional POAs rely on a sample covariance matrix or complex coherence matrix (CCM) derived by spatially averaging statistically homogeneous pixel neighborhoods, which may blur and destroy phase information, especially in dense fringe areas. To overcome this limitation, an adaptive POA is proposed in this article. The adaptive POA artificially constructs a superior CCM by the filtered interferometric phase, which is derived through spatial adaptive filtering approach fusion of principal phase component estimation and fast nonlocal means filtering, and an accurate coherence matrix determined via coherence estimation bias correction. Moreover, the modified eigen-decomposition-based maximum-likelihood-estimator of the interferometric phase (EMI) with coherence-power-weighting is proposed to further improve the estimation precision and computational efficiency. The estimated CCM is then processed with the modified coherence-power-weighted EMI algorithm, and the optimal phase history is retrieved. The experimental results validated against both simulated and Sentinel-1A data demonstrate the superior optimization performance and robustness of the adaptive POA over traditional POAs.

An air-to-ground guidance missile aimed to hit a main battle tank(MBT) should detect a ground moving target and estimate the target position to guide. In this paper, we detect a front ground moving target by using FMCW(Frequency Modulated Continuous Wave) and estimate the position by forward-looking SAR(Synthetic Aperture Radar) via scanning certain front ground section by steering a beam with narrow beamwidth left to right mechanically. Also, by MLE(Maximum Likelihood Estimation), degree of how fast the target approach or recede from the radar can be figured out from the estimated radial velocity of the moving target. Subsequently, we generate a radar image via corrected matched filter from phase history including the radial velocity. [ABSTRACT FROM AUTHOR]

Abstract: In a recent contribution Ferretti and co-workers (Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., Rucci, A., 2011. A new algorithm for processing interferometric data-stacks: SqueeSAR IEEE Transactions on Geoscience and Remote Sensing 49(9), pp. 3460–3470) have proposed the SqueeSAR method, a way to exploit temporally coherent distributed scatterers in coherent SAR data stacks. Elevation and deformation or subsidence estimates are obtained with accuracy similar as in the well known persistent scatterer interferometry (PSI). In this paper we propose an alternative approach and provide a first demonstration of the optimal estimation of distributed scatterers’ phase histories in urban areas. Different to SqueeSAR, we derive phase histories for each distributed scatterer pixel rather than for groups of pixels. We use the Anderson–Darling statistical test to identify neighboring samples of the same distribution. Prior to covariance matrix estimation required for maximum likelihood estimation we apply a multi-resolution defringe technique. By using TerraSAR-X high resolution spotlight data, it is demonstrated that we are able to retrieve reliable phase histories and motion parameter estimates from distributed scatterers with signal-to-noise-ratio far below the common range. [Copyright &y& Elsevier]

This paper describes a solution algorithm for ultrawideband orthogonal frequency-division multiplexing (OFDM) synthetic aperture radar (SAR) signal processing. A single pointlike target is assumed, and stripmap SAR geometry is used for the analysis. The peculiarity of extracting phase history from a multicarrier return signal—such as OFDM—is in solving for the phase component using all signals' subbands in frequency domain. The least squares approach is used to find the solution form, and a grid search algorithm is used to find the phase history sample estimates. Simulation results illustrating the algorithm's performance in a 1-D cross-range profile reconstruction case, as well as in 2-D SAR imaging scenarios, are presented and discussed. It is shown that the proposed method is effective in reconstructing cross-range profiles and possesses high potential for adaptive SAR imaging while exhibiting good computational performance approximating that of range-Doppler algorithm. [ABSTRACT FROM AUTHOR]