吴仁彪，学者全讯担保网主页-全讯担保网

range-doppler (rd) algorithms are widely used in inverse synthetic aperture radar (isar) imaging. in the standard rd algorithm, envelope alignment and autofocus are first applied to transform the original data into equivalent turntable target data, and then fft is used for the image formation. usually, the migration through resolution cells (mtrcs) due to target rotation is ignored. with the improvement of resolution or the increase of target size, mtrcs cannot be ignored and must be mitigated. for high speed moving targets, the most widely used 'stop-and-go' data model is violated. in this case, the mitigation of mtrcs becomes even more complicated. in the paper, technical issues associated with high resolution isar imaging of high speed moving targets are addressed. a preprocessing algorithm is first proposed to convert the non-coherent raw isar data into coherent data, then a keystone formatting algorithm developed for the imaging of slow ground moving targets in sar is applied to mitigate the mtrcs owing to rotational motion, and, finally, a high resolution time-frequency analysis based range-instantaneous doppler (rid) algorithm is used to produce the focused isar image. numerical examples are provided to illustrate the performance of the proposed approach.

we present a conceptually simple and computationally efficient algorithm, which is referred to as wrelax, for the well-known time delay estimation problem. the method is a relaxation-based minimizer of a complicated nonlinear least squares criterion. wrelax can be applied to detecting and classifying roadway subsurface anomalies by using an ultra-wideband ground-penetrating radar. numerical and experimental examples are provided to demonstrate the performance of the new algorithm.

an inverse synthetic aperture radar (isar) can be used to produce high-resolution images of moving targets of interest by utilizing the relative motion between the target and the radar and by transmitting signals with large bandwidth. most isar imaging algorithms are based on the range-doppler processing, which implies that the doppler shifts remain constant during the coherent integration time. for maneuvering targets, the doppler shifts are time varying. in this case, the algorithms will produce blurred images. we present herein an adaptive capon spectral estimation algorithm for the complex isar image formation of maneuvering targets. it is an efficient recursive implementation of the well-known capon complex spectral estimation algorithm by using fft and simple matrix operations.

apes (amplitude and phase estimationl is an adaptive fir filtering approach introduced recently for complex spectral estimation. for stationary signals, apes can yield more accurate spectral estimates than the widely used capon and fft estimators. the authors present a recursive apes algorithm for time-varying spectral analysis, which is computationaly efficient and only involves fft and simple matrix operations. the recursive apes algorithm is applied to isar (inverse synthetic aperture radar) imaging and feature extraction of targets with complex manoeuvring motion. both numerical and experimental examples are provided to demonstrate the performance of the proposed method.

range-doppler (rd) processing is widely used in conventional inverse synthetic aperture radar (isar) imaging. the unwanted translational motion of moving targets is compensated by envelope alignment and autofocus. for existing isar imaging algorithms, the scatterers\' migration through resolution cells (mtrc) caused by the rotational motion is usually ignored. with the improvement of resolution or the increase of target size, mtrc cannot be neglected. in this letter, the keystone formatting algorithm developed in sar is used for the mtrc compensation in isar. before the keystone formatting, coherent processing must be performed on the raw phase history data. an effective approach is proposed for this kind of coherent processing. numerical examples are provided to demonstrate the performance of the proposed approach.

in sonar and many other applications, time-delay estimation is an important problem. when bandpass probe signals are used, the correlation function between the received and the known transmitted signals oscillates near the carrier frequency of the transmitted signal. in this case, many existing time-delay estimation algorithms perform poorly due to converging to local optimum points. in this paper, two efficient algorithms, hybrid-wrelax and exip-wrelax, are proposed to deal with the above problem. they are relaxation-based global minimizers of a highly oscillatory nonlinear least-squares cost function. both algorithms are shown to achieve the cram´er–rao bound and require only a sequence of weighted fourier transforms.

we consider estimating time delays and amplitudes (real-or complex-valued) from the superposition of very closely spaced signals with known shapes. the well—known high resolution mode (method of direction estimation) algorithm, which was originally proposed for angle estimation in array processing, is modified and used with our efficient time delay estimation method wrelax (weighted fourier transformand relaxation based) algorithm to deal with this problem. the proposed method is referred to as mode-wrelax. mode-wrelax provides better accuracy than mode and higher resolution than wrelax. moreove it can be used for both complex-and real-valued signals (including those with highly oscillatory correlation functions). numerical results show that the mode-wrelax estimates can approach the corresponding cram6r-rao bound. efficient implementation of the algorithm is also discussed.

we present an efficient algorithm for high resolution time delay estimation in colored gausslan nalse with multiple looks. by using a relaxation-based optimization approach and exploiting the toeplitz property of the noise eovaliauee matrix, the enhaneed new algorithm can achieve the corresponding cramer-rao bounds (crbs) with a very small number of lndependent looks.

high range resolution (hrr) moving target indicator radar is becoming increasingly important for many military and civilian applications involving the detection and classification of moving targets within a clutter background. for ground-based hrr radar, when targets are moving slowly or near-broadside and the coherent processing interval or dwell time is not too long, the effects of range migration and range feature distortion can be ignored. based on this assumption, relaxation-based algorithms that are robust and computationally simple are proposed for hrr feature extraction of moving targets consisting of scatterers closely spaced in range in the resence of stationary clutter. numerical examples show that the proposed algorithms exhibit super-resolution and excellent estimation performance.

to detect buried landmines, planning systems incorporated (psi) has developed a ground penetrat-ing synthetic aperture radar (gpsar) system. gpsar can be used to generate three-dimensional images of mines. it has been shown that the sar processing in the psi gpsar system can greatly improve the image quality and hence the mine (especially plastic mine) detection performance. since the electromagnetic wave propagation velocity in the soil depends on many factors, inchlding the soil texture, bulk density, as well as water content, and it changes from one location to another, velocity uncertainty is inevitable. it is well-known that for the conventional airborne/spaceborne sar processing, velocity uncertainty may result in phase errors that will severely blur the sar image. however, we have observed from experimental examples that the psi gpsar system is very robust against the velocity uncertainty. more specifically, under mild conditions velocity uncertainty does not defoeus the image but only scales the image along the depth dimension, and hence will not affcct the mine detection performance. theoretical analysis is provided to explain this observation. although our discussion is based on the psi gpsar system, it applies to other gpr based landmine detection systems as well.