Curvilinear Aperture Research Articles

Modified nonlinear chirp scaling algorithm for curvilinear trajectory synthetic aperture radar

The original nonlinear chirp scaling (NCS) algorithm was extended for high precision processing of the highly squinted curvilinear trajectory synthetic aperture radar (CTSAR). Based on the analysis of slant range model and the frequency spectrum characteristics of the echo signal, a novel nonlinear chirp scaling function and more complex phase compensation factors with both velocity and acceleration parameters were proposed in the new algorithm for accommodation to curvilinear trajectory. The processing flow and computational complexity of modified NCS algorithm were fundamentally the same as the original NCS algorithm. However, the higher order phase compensation, range cell migration correction (RCMC) and range-variant secondary range compression (SRC) caused by the non-linear aperture and the severe range-azimuth coupling were accomplished accurately and efficiently without interpolation. Simulation results show that data acquired with a curvilinear aperture and a squint angle up to about 50° for X-band can be processed with no evident degradation of impulse response function.

Autofocus algorithm for synthetic aperture radar imaging with large curvilinear apertures

An approach to autofocusing for large curved synthetic aperture radar (SAR) apertures is presented. Its essential feature is that phase corrections are being extracted not directly from SAR images, but rather from reconstructed SAR phase-history data representing windowed patches of the scene, of sizes sufficiently small to allow the linearization of the forward- and back-projection formulae. The algorithm processes data associated with each patch independently and in two steps. The first step employs a phase-gradient-type method in which phase correction compensating (possibly rapid) trajectory perturbations are estimated from the reconstructed phase history for the dominant scattering point on the patch. The second step uses phase-gradient-corrected data and extracts the absolute phase value, removing in this way phase ambiguities and reducing possible imperfections of the first stage, and providing the distances between the sensor and the scattering point with accuracy comparable to the wavelength. The features of the proposed autofocusing method are illustrated in its applications to intentionally corrupted small-scene 2006 Gotcha data. The examples include the extraction of absolute phases (ranges) for selected prominent point targets. They are then used to focus the scene and determine relative target–target distances.

Feature-Independent Aperture Evaluator for the Curvilinear SAR

Curvilinear synthetic aperture radar (SAR), as a more practicable 3-D SAR imaging system, utilizes parametric target feature estimates extracted from the received data to reconstruct the target image. The reconstructed image quality is then impacted by the estimation accuracy of the features. In this letter, through discussing the correlation between the system parameters and the estimation performance of the curvilinear SAR, a conclusion can be drawn on how the overall location accuracy of a target is determined by the correlation between the azimuth and elevation coordinates of the flight path, compactly characterizing the curvilinear aperture. Consequently, a new index, determined only with the aperture parameters, is proposed as an aperture evaluator, which is referred to as the feature-independent aperture evaluator (FAE). FAE can be used for guiding the operational aperture design

Use of curvilinear SAR for three-dimensional target feature extraction

The use of curvilinear synthetic aperture radar (CLSAR) is considered for three-dimensional feature extraction of small targets consisting of a small number of distinct point scatterers. CLSAR does not suffer from the ambiguities suffered by interferometric SAR. As CLSAR is a relatively new technology, a self-contained detailed derivation of the data model is presented. The Cramer-Rao bounds of the parameter estimates are also derived. Use of the RELAX algorithm for 3-D target feature extraction with CLSAR for different curvilinear apertures is also described.