Standard ISAR image focusing techniques utilize the magnitude and position of range domain target signatures to achieve translational motion compensation. Alternatively, range can be determined by analyzing the slope of the unwrapped phase function associated with the frequency domain signature of a moving target. Phase slope based method uses the phase of the target's echo transfer function to calculate a focal quality indicator while avoiding two-dimensional Fourier processing. Simple phase averaging of targets having low signal-to-noise ratio does not lead to a useful estimate of the signal phase. Maximum likelihood phase gradient estimation method is then utilized to determine the phase slope of the radar signature. The presence of an absolute minimum without local minima guarantees that the estimated motion parameters are an accurate representation of the target's motion and allows the use of a simple search procedure. Results show that parameter estimates for motion compensation obtained in this manner converge to a unique solution, thus providing focused ISAR imagery of the target.
SAR/ISAR image processing involves a two-dimensional Fourier transform that may produce significant high intensity sidelobes which obscure low intensity scatterers in the image. Spatially variant sidelobe apodization is a technique that reduces sidelobe levels in a final Fourier image while maintaining the image resolution that would be obtained using the rectangular window. In this paper, a generalization of this technique based on the use of different parametric windows is proposed. Low sidelobe levels are obtained at the expense of increasing the complexity of the sidelobe apodization algorithm. Similar resolution and lower sidelobe levels were obtained using a one-dimensional example when compared to the spatially variant apodization technique. The method was also tested and results are shown when using this new sidelobe apodization technique with a two dimensional ISAR image.
Standard ISAR image focusing techniques utilize the magnitude and position of range domain target signatures to achieve translational motion compensation. Alternatively, range can be determined by analyzing the slope of the unwrapped phase function associated with the frequency domain signature of a moving target. We propose a motion compensation method based on such phase slope analysis. To improve noise performance, the probability density function of additive noise corrupting the signature is modeled as a Weibull distribution to determine a threshold for constant false alarm rate filtering. Complex analysis is then utilized to determine sample intervals where noise is relatively weak, where the amplitude of the filtered signature is high above the noise interference level and the phase of the same signature is nearly linear Weighted least squares is subsequently used to estimate the target's kinematic parameters based on the phase slope measurement. Results show that parameter estimates for motion compensation obtained in this manner converge to a unique solution, thus providing focused ISAR imagery of the target.
We present a new method for estimating the motion parameters of a target from its inverse synthetic aperture radar (ISAR) signature. This method uses the phase of the target's echo transfer function to calculate a focal quality indicator while avoiding two-dimensional Fourier processing. The focal quality indicator reaches the global minimum of a parametric motion surface when the phase is compensated with the target's actual motion parameters. The presence of an absolute minimum without local minima guarantees that the estimated motion parameters are an accurate representation of the target's motion and allows the use of a simple search procedure. Polynomial fitting is incorporated to the new method to improve the robustness by reducing estimation errors due to the finite order of the parametric motion model.
In this article, we present a method for estimating the translational motion parameters of a target from its ISAR signature. The method exploits the phase of the target's frequency response and does not require 2D Fourier processing. The basis for this method is a phase difference indicator which converges to an absolute minimum when the phase is compensated with the true values of the motion parameters. The analysis of the phase difference indicator leads to an algorithm for motion parameter optimization. Processing of simulated and experimental ISAR signatures demonstrates that the phase difference method is extremely computationally efficient and equally accurate when compared to robust techniques based on entropy or Fisher information indicators.
In this paper we formalize a theory for indicators designed to focus ISAR imagery of non-cooperative targets. These indicators represent variations of the Fisher information and entropy measures, and are capable of operating either in the spatial-frequency domain or in the spatial domain. This freedom of choice is advantageous since the information on the target's representation in either domain has phase and magnitude components, which can be efficiently exploited to resolve and focus the target's primary elements. These elements are displayed as radar cross section (RCS) distribution, we propose a phase correction algorithm based on parametric models of a target's temporal maneuvers. The approach is to quantify the phase non-linearities via the Fisher information or entropy measure that is dependent on motion parameter estimates. The optimization of these parameter estimates is a m-dimensional search problem that minimizes the focus quality indicator over a prescribed tolerance for a given SNR. The coordinates of this minimum point are subsequently used to generate a phase correction factor that eliminates image blurring, thus providing better focusing for effective target recognition.
Inverse synthetic aperture radar (ISAR) is an imaging technique that can be utilized in the identification of targets such as ships and aircraft. Since these targets are free to maneuver during the time required to collect their signature, kinematic motion parameter estimates are needed to focus ISAR imagery. In order to perform this estimation, a burst derivative measure, which has global minimum coordinates that provide optimum estimates of the motion parameters, is utilized in conjuction with unconstrained optimization algorithms. It is shown that the burst derivative is a multivariate function with a strong dependence on radar parameters. Results indicate that this dependence can be exploited by the optimization algorithms to obtain efficient motion estimation, thus improving the overall processing speed.