Two novel image processing techniques have been developed to refocus a moving target image from its smeared response in the synthetic aperture radar (SAR) image which is focused on the stationary ground. Both approaches may be implemented with efficient fast Fourier transform (FFT) routines to process the Fourier spatial spectrum of the image data. The first approach utilizes a matched target filter that is derived from the signal history along the range-Doppler migration path mapped onto the SAR image from the moving target trajectory in real space. The coherent spatial filter is specified by the apparent target range in the image and the magnitude of the relative target-to-radar velocity. The second approach eliminates the range-dependence by reconstructing the moving target image from a spectral function that is obtained from the SAR image data spectrum via a spatial frequency coordinate transformation.

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Theory of synthetic aperture radar imaging of a moving target

A new technique is developed for range alignment in inverse synthetic aperture radar (ISAR) imaging. The shifts made to the echoes are modeled as a polynomial, and the coefficients of this polynomial are chosen to optimize a global quality measure of range alignment. This technique is robust against noise and target scintillation, and avoids error accumulation. In addition, the shift in the time domain is implemented by introducing a phase ramp in the frequency domain, which removes the limitation of integer steps.

Global range alignment for ISAR

This paper deals with the problem of detecting a radar target signal against correlated non-Gaussian clutter, which is modeled by the compound-Gaussian distribution. We prove that if the texture of compound-Gaussian clutter is modeled by an inverse-gamma distribution, the optimum detector is the optimum Gaussian matched filter detector compared to a data-dependent threshold that varies linearly with a quadratic statistic of the data. We call this optimum detector a linear-threshold detector (LTD). Then, we show that the compound-Gaussian model presented here varies parametrically from the Gaussian clutter model to a clutter model whose tails are evidently heavier than any K-distribution model. Moreover, we show that the generalized likelihood ratio test (GLRT), which is a popular suboptimum detector because of its constant false-alarm rate (CFAR) property, is an optimum detector for our clutter model in the limit as the tails get extremely heavy. The GLRT-LTD is tested against simulated high-resolution sea clutter data to investigate the dependence of its performance on the various clutter parameters.

Coherent Radar Target Detection in Heavy-Tailed Compound-Gaussian Clutter

A new technique is developed for phase adjustment in ISAR imaging. The adjustment phase is found by iteratively solving an equation, which is derived by minimising the entropy of the image. This technique can be used to estimate adjustment phases of any form. Moreover, the optimisation method used in this technique is computationally more efficient than trial-and-error methods.

Minimum-entropy phase adjustment for ISAR

As the undesired echo from a radar environment (clutter) is often not Rayleigh distributed, problems arise in specifying the clutter as a non-Gaussian random process and in computer-generating realisations of clutter for simulation purposes. The paper shows that modelling the clutter as a compound Gaussian or a spherically invariant random process allows a complete specification of the clutter suitable for use in radar design. It also lends itself readily to computer simulation procedures. Specific procedures for generating clutter realisations with assigned correlation properties and with K or Weibull amplitude distributions are suggested, and are validated by statistical tests. Then the issue of resolving between the Weibull and K distributions is considered, and empirical operative characteristics of the associated hypothesis test are computed.

Modelling and simulation of non-Rayleigh radar clutter

For pt.1 see ibid., vol.139, no.1, p.79-88 (1992). The authors show how time-frequency representation by Wigner-Ville distribution of the echoes received by a synthetic aperture radar provides a useful tool for detection of moving objects and the estimation of the instantaneous phase shift induced by relative radar-object motion. The phase history is then used to compensate the received signal and to form a synthetic aperture with respect to the moving object, necessary to produce a high resolution image.<
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Detection and imaging of moving objects with synthetic aperture radar. Part 2: Joint time-frequency analysis by Wigner-Ville distribution

The subject of this work is the detection and high resolution microwave imaging of objects moving on the ground and observed by an airborne radar. The proposed approach is based on a combined space-time and time-frequency processing. The space-time processing makes use of a linear array antenna and exploits the radar motion for filtering the received echoes in order to improve as much as possible the signal-to-disturbance ratio. The signal is then mapped onto the time-frequency domain, by computing its Wigner-Ville distribution, for a further filtering and for estimating its instantaneous frequency, necessary for the formation of a high resolution image of the moving object. >

Space-time-frequency processing of synthetic aperture radar signals

The paper deals with the problem of radar detection of a target echo embedded in Weibull clutter and white Gaussian noise (WGN). Relevant features of the paper, with respect to previous papers on the same subject, refer to the coherent nature of the Weibull process (that modelling the clutter) and of the processing chain. In more detail, the in-phase and quadrature components of the clutter echoes have been modelled to give a Weibull probability density function (PDF) of the amplitude and a uniform PDF of the phase. Any shape of the correlation function among consecutive clutter samples is also allowed in the model. The so called 'coherent Weibull clutter' (CWC) introduced in the paper represents a suitable generalisation of the conventional ?coherent Gaussian clutter? (CGC). The processing chain is also coherent, i.e. it operates on the in-phase and quadrature components of the signals. To derive a suitable processing scheme, we resort to the general theory of radar detection which applies to any type of PDF and autocorrelation function (ACF) of the target and clutter. Briefly, it is found that the processing scheme is based on two nonlinear estimators of the clutter samples in the two alternative hypotheses (i.e. H0 and H1 the fully fledged architecture being discussed in the paper. The detection processor turns out to be a suitable generalisation of that concerning the CGC case. A new family of processing schemes is derived in accordance with the statistical model assumed for the useful target. The following cases are covered: target known a priori, Swerling 0, 1 and 2 models, and partially fluctuating target. Attention is also paid to the interesting case of a target modelled as a coherent Weibull process. The detection of such a target against white Gaussian noise is worked out. The detection performance of the a

Theory of radar detection in coherent Weibull clutter

A novel scheme for detecting moving targets with synthetic aperture radar (SAR) is presented The proposed approach is based on the use of the Wigner-Ville distribution (WVD) for simultaneously detecting moving targets and estimating their motion kinematic parameters The estimation plays a key role for focusing the target and correctly locating it with respect to the stationary background The method has a number of advantages: the detection is efficiently performed on the samples in the time-frequency domain, provided the WVD, without resorting to the use of a bank of filters, each one matched to possible values of the unknown target motion parameters; and the estimation of the target motion parameters can be done on the same time-frequency domain by locating the line where the maximum energy of the WVD is concentrated A validation of the approach is given by both analytical and simulation means In addition, the estimation of the target kinematic parameters and the corresponding image focusing are also demonstrated >

A novel procedure for detecting and focusing moving objects with SAR based on the Wigner-Ville distribution

This paper defines an algorithm for the estimation of the radar target direction of arrival (DOA) when electromagnetic interferences impinge on an adaptive planar array. First, the case of a fully adaptive array is considered and the target DOA is estimated via the maximum-likelihood principle. The performance of the derived algorithm is also found. Subsequently, the case of adaptivity applied to subarrays is examined. The performance losses of the adaptive subarray configuration with respect to the fully adaptive array are determined. Also the distortions of the antenna patterns are assessed.

A. Farina

Papers

Detection and imaging of moving objects with synthetic aperture radar. Part 2: Joint time-frequency analysis by Wigner-Ville distribution

Space-time-frequency processing of synthetic aperture radar signals

Theory of radar detection in coherent Weibull clutter

A novel procedure for detecting and focusing moving objects with SAR based on the Wigner-Ville distribution

Monopulse estimation of target DOA in external noise fields with adaptive arrays