![]() ![]() These subimages are of low resolution 1/(Delta) T, and though normally in focus, they are of limited use in estimating the parameters governing defocus over the longer interval T. Over short subintervals or segments of duration (Delta) T covering the interval T, subimages may be formed via the normal weighted FFT applied to aligned complex range profiles. This paper presents an application of the WVD (Wigner-Ville Distribution) to the problem of focusing an inverse SAR (ISAR) image over an extended period T of the order of two seconds or more. Monte Carlo simulations show that the MLE attains the Cramer-Rao bound for low to moderate signal-to-noise depending on the a priori estimates of range, velocity and acceleration. A fast MLE is then proposed which uses the Hough transform (HT) to initialize the MLE algorithm. In addition, the Cramer Rao bound is computed in closed form which shows that the velocity bound is decoupled from the corresponding bounds in range and acceleration. Convexity and symmetry properties of the ambiguity function over range, velocity and acceleration are presented these are useful for determining region and speed of convergence for recursive algorithms used to compute the MLE. Analytic properties of the associated wideband ambiguity function are derived in particular the ambiguity function, with acceleration set to zero, is derived in closed form. The algorithm is demonstrated for 0.3 meter resolution SAR data of a Winnebago van from a Ku-band radar.Īn efficient implementation of the maximum likelihood estimator (MLE) is presented for the estimation of target range, radial velocity and acceleration when the radar waveform consists of a wideband linear frequency modulated (LFM) pulse train. In forming the motion model, the algorithm avoids undue reliance on particular bright or narrow-Doppler targets in favor of a global fit to many partially resolved or glinting features. The algorithms attempt to estimate the bulk motion of the vehicle and its rotation about its mean position by fitting many small pieces of information gathered from low quality or short duration scatterers detected in either the raw range- compressed signal history or in the complex image. This paper explores the effectiveness of models of the second kind by testing a set of motion estimation algorithms against motor vehicle targets imaged by a high-frequency SAR at long range and low grazing angle. By contrast, the imaging of large ships in sea clutter yields a wealth of information of the motion of the body. This question is particularly important for the imaging of small targets in ground clutter since the available information on the target motion may be very limited. A major unsolved problem is whether it is better to develop a deterministic model of the motion or to fit the observations to a mathematical model which may be ambiguous with a wide range of physical target motions. Inverse-synthetic aperture radar (ISAR) processing algorithms require an explicit or implicit model of the motion of the target, since this information is not measurable at the sensor. In addition, the analysis shows how cavity/inlet shape-specific information may be estimated from an ordinary ISAR image. We examine an older (and less accurate) model based on a weak-scattering modal expansion of the structure which appears to be well-suited to ISAR imaging. Many of the more complete and accurate scattering models require extensive knowledge about the cavity/inlet shape and size and, moreover, are numerically intensive - features that make them unsuitable for many imaging applications. Since inlets and cavities (typically) make a strong contribution to the radar field scattered from aircraft targets, these artifacts often confound the image interpretation process and considerable effort has been spent in recent years to model, isolate, and remove these sources of error. " File must be in Activity format (suffix not relevant).The standard ISAR high-frequency weak-scatterer model is inappropriate to targets with inlets and cavities, and images created under this model assumption often display artifacts associated with these structures. If ((act_value != 0) & act_value != 1) Ĭerr File must either have suffix. ![]() You should have received a copy of the GNU General Public Licenseįor (string::size_type i = str-> find(nl) i!=string::npos i=str-> find(nl)) str-> erase(i, 1) // erase dos cr GNU General Public License for more details. MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. This program is distributed in the hope that it will be useful,īut WITHOUT ANY WARRANTY without even the implied warranty of The Free Software Foundation, either version 3 of the License, or It under the terms of the GNU General Public License as published by This program is free software: you can redistribute it and/or modify ![]()
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