This letter presents a novel scheme for moving target deception jamming (MTDJ) against multichannel synthetic aperture radar (SAR)-ground moving target indication (GMTI) based on multiple jammers. The multichannel SAR signal models for the real and false moving targets are established first. Then, the proposed MTDJ method based on multiple jammers is derived according to the principle that the interferometric phase matches the across-track velocity of the moving target. To ensure efficient deception jamming and to resolve the accurate scattering coefficient modulated in each jammer, the interferometric phase of the false moving target is decomposed into two parts, one is the term that matches the across-track velocity, while the other is the additional term generated by each jammer. Hence, the expected modulation coefficient in each jammer can be determined based on the additional interference phase cancellation. Finally, the proposed MTDJ is realized by generating the jamming signals in each jammer via the efficient deception jamming technique. The simulation results demonstrate the effectiveness of the proposed MTDJ method.

This paper is concerned with the development and analysis of a nonlinear predictor for a non-Gaussian process using a zero-memory nonlinearity (ZMNL) followed by a second order Volterra filter (SVF). The processor exploits partial statistical information such as marginal probability density function (PDF) and the covariance structure. The ZMNL transforms the process into a Gaussian process. The SVF can be implemented as a parallel combination of linear and quadratic filters. Another equivalent structure is to carry out a linear prediction in the transformed Gaussian domain and pass the linear predicted samples through a second order polynomial.>

An algorithm has been developed to provide the uplink received antenna with beamforming capability for selective earth coverage and jamming protection for military satellite communications applications. The problem can be formulated as to determine the control weight to meet the user gain and jammer null requirements in their known directions subject to the norm constraint. A recursive procedure has been developed which provides flexibility in the determination of the constraint vector, the constraint order and the constraint phase optimization. The recursion can also be implemented as updating of the null space.>

There is a compromise between noise removal and texture preservation in image enhancement. It is difficult to perform image enhancement, using only one simple filter, for a real world image which may consist of many different regions. This article studies the intelligent aspect of filtering algorithms and describe a multi-threshold adaptive filter (MTA filter) for solving this problem. the MTA filter uses a generalized gradient function and a local variance function, which provides the local contextual information as evidence to determine the nature of the filtering for each local neighborhood. A knowledge-based presegmentation procedure is presented. It applies a threshold operation to extract the local evidence. A belief function is used to combine different evidence and to determine the local filtering strategies. In this way, several simple filters can be combined to form a more efficient and more flexible context dependent filter. As a result, specific filtering is only applied to the region for which it is suitable. Thus, a balanced texture preserving and noise removal effect can be simultaneously achieved.

An efficient algorithm for successive eigenvalue decomposition (EVD) with adding new data to, and deleting old data from, the data matrix simultaneously is proposed. The algorithm involves converting a full-scale EVD problem by using Householder transformation twice. Some approaches to solve the resulting EVD problem, including a direct method to compute the eigenvector directly, are discussed. This algorithm can be useful in adaptive array processing and tracking of nonstationary sinusoids. >

This paper is concerned with mixed time-frequency signal processing using Wigner Distribution Function (WDF). This approach is based upon the generation of the mixed time-frequency representation (MTFR) of a signal, processing of that representation in the mixed time-frequency domain, and obtaining a filtered output by an inverse operation or approximation procedure. Various signal processing operations are formulated. Validity condition for the resulting MTFR are investigated. An inverse operation can be applied as an exact procedure if the output MTFR is a valid WDF, it can be regarded as an approximation procedure if the resulting MTFR is not admissible. Other approximations can be formulated. One scheme depends on the projection of the MTFR onto the space of valid WDF. The other scheme depends on the modification of the filtering function in a minimal way such that the resulting MTFR is valid.

There is a compromise between noise removal and texture preservation in image enhancement. It is difficult to do an image enhancement task by using only one simple filter for a real world image which may consist of regions of various local activities. We describe a multi-threshold adaptive filter (MTA filter) for solving this problem in this paper. It uses a generalized gradient function which reflects the local contextual information as a cue to determine the nature of the filtering for each local neighborhood. In this way, several simple filters can be combined to form a more efficient and more flexible context dependent filter. As a result, specific filter is only applied to the region which is suitable for it. Thus, a balanced texture preserving and noise removal effect can be simultaneously achieved.

The resolution of the closely spaced sinusoids becomes poor as the SNR of the received signal reduces. This resolution can be improved by using the Total Least Squares (TLS) method in solving the linear prediction (LP) equation. This approach makes use of the singular value decomposition (SVD) of the augmented matrix to reduce the noise effect from both the observation vector and the LP data matrix. At low SNR, the performance of this frequency estimator is found to be better than the frequency estimator using the Principal Eigenvector (PE) method which is proposed by Tufts and Kumaresan. They used the SVD of the LP data matrix to reduce the noise effect from the data matrix only.

This paper is concerned with filter design for enhancement of radar target characteristics. In high-resolution radar, the return can be considered as degraded response of the target. The degradation is modelled as interaction of the target with the transmitted pulse, and additive environmental and measurement noise. Our work is concerned with the application of a constrained filter design to coherent radar system, where complex envelope representation is used for underlying signal and system. The filter is designed such that the so-called Narrowness Measure of the magnitude of the system composite function is minimized subject to a specified output noise level. This development follows closely the procedure which is used in image enhancement. The advantage of the filter is that it is data independent and depends only on the transmitted pulse and the second order statistics of the noise.

This paper is concerned with the estimation of the target magnitude response given the non-coherent receiver measurements and the complex information of the transmitted signal. This is a signal reconstruction problem from partial information which involves phase retrieval as well as deconvolution. Nonlinear least squares and error reduction algorithm are formulated for solving this complex non-coherent deconvolution problem. Some preliminary experimental results are included.

A new, fast template-matching method using the Singular Value Decomposition (SVD) is presented. This approach involves a two-stage algorithm, which can be used to increase the speed of the matching process. In the first stage, the reference image is orthogonally separated by the SVD and then low-cost pseudo-correlation values are calculated. This reduces the number of computations to 2*N*L instead of N2L2, where L × L is the size of the reference image and N × N is the original image size. At the second stage, a small group of values near the maximum pseudo-correlation is selected. the true correlation for the small number of pixels in this group is them computed precisely in the second stage. Experimental and analytic results are presented to show how the computation complexity is greatly improved.

In this paper we report our investigation on zonal sampling, probability fitting tests, and bit allocation for Hadamard transform image data compression. The energy distribution of the transform coefficients suggests a sampling strategy which retains as much energy as possible for a fixed number of coefficients. The probability fitting tests indicate that Laplacian is a better model for non-dc coefficients, while Rayleigh or Gaussian fits better for the dc coefficient. Finally, the bit assignment is formulated as a constrained optimization problem. The mean square error performance is derived as a function of the number of retained coefficients for various average bit rates. The results are useful for designing a zonal coding system with optimum retained zone size and shape as well as their corresponding bit assignment.

The problem of synthesizing a signal from a modified or specified Wigner distribution function is considered. This problem arises in signal separation, time frequency filtering, window and filter designs. The modified or specified time frequency function may not correspond to a valid Wigner function, thus our synthesis problem is formulated as approximately a two-dimensional function as a product of two one-dimensional function by using least squares procedure. The first procedure involves expressing a time-frequency function as a bilinear combination of basis auto and cross Wigner functions. The least squares approximation leads to an eigenvalue-eigenvector decomposition of a symmetric matrix. The second procedure involves the approximation of a pre-computed matrix as an outer product of two vectors.

This paper addresses the problem of using frequency domain information for improved signal restoration. Specifically we want to estimate the underlying signal from degraded measurements when we are given (i) the distribution of the frequency components (ii) the bounds on the signal spectrum. We also asume that the energy of the noise is bounded by a known constant. The first problem is formulated as a constrained minimization of the windowed power in the frequency domain where the prior information is injected by the choice of window. The second problem is formulated as finding the feasible region which is the intersection of ellipsoids which are determined from the bounds on the signal spectrum and noise energy. This formulation is computationally very involved and we avoid this difficulty by finding an ellipsoid which will tightly bound the intersection. The geometric center of the resulting ellipsoid provides a good estimate of the underlying signal.

A computational method has been developed for solving the maximum entropy spectrum estimation with uncertainty in the correlation measurements This approach depends on first solving an unconstrained optimization problem and then an iterative search for the zero of the constraint equation along a well-defined trajectory. This trajectory is governed by a system of differential equations and the unconstrained optimal solution as the initial condition. The direction of search can be determined easily by evaluating the constraint equation at each iteration. In solving the trajectory, the Gauss-Seidel iterative algorithm is used to solve the symmetric system of equations. The choice of the previous estimate as the initial estimate greatly accelerates the convergence rate of this scheme.

A new algorithm is developed for solving the maximum entropy (ME) image reconstruction problem. This approach involves solving a system of ordinary differential equations with appropriate initial conditions which can be computed easily. Instead of searching in the n+1-dimensional space as required for most ME algorithms, our approach involves solving a 1-dimensional search problem along a well-defined path. Moreover an efficient algorithm is developed to handle this search.