The Rayleigh Quotient Quadratic Correlation Filter (RQQCF) has been used to achieve very good
performance for Automatic Target Detection/Recognition. The filter coefficients are obtained as the
solution that maximizes a class separation metric, thus resulting in optimal performance. Recently, a
transform domain approach was presented for ATR using the RQQCF called the Transform Domain
RQQCF (TDRQQCF). The TDRQQCF considerably reduced the computational complexity and storage
requirements, by compressing the target and clutter data used in designing the QCF. In addition, the
TDRQQCF approach was able to produce larger responses when the filter was correlated with target and
clutter images. This was achieved while maintaining the excellent recognition accuracy of the original
spatial domain RQQCF algorithm. The computation of the RQQCF and the TDRQQCF involve the inverse
of the term A1 = Rx + Ry where Rx and Ry are the sample autocorrelation matrices for targets and
clutter respectively. It can be conjectured that the TDRQQCF approach is equivalent to regularizing A1. A
common regularization approach involves performing the Eigenvalue Decomposition (EVD) of A1, setting
some small eigenvalues to zero, and then reconstructing A1, which is now expected to be better
conditioned. In this paper, this regularization approach is investigated, and compared to the TDRQQCF.
Quadratic Correlation Filters have recently been used for Automatic Target Recognition (ATR). Among these, the Rayleigh Quotient Quadratic Correlation Filter (RQQCF) was found to give excellent performance when tested extensively with Infrared imagery. In the RQQCF method, the filter coefficients are obtained, from a set of training images, such that the response to the filter is large when the input is a target and small when the input is clutter. The method explicitly maximizes a class separation metric to obtain optimal performance. In this paper, a novel transform domain approach is presented for ATR using the RQQCF. The proposed approach, called the Transform Domain RQQCF (TDRQQCF) considerably reduces the computational complexity and storage requirements, by compressing the target and clutter data used in designing the QCF. Since the dimensionality of the data points is reduced, this method also overcomes the common problem of dealing with low rank matrices arising from the lack of large training sets in practice. This is achieved while retaining the high recognition accuracy of the original RQQCF technique. The proposed method is tested using IR imagery, and sample results are presented which confirm its excellent properties.
The Modified Eigenvalue problem arises in many applications such as Array Processing, Automatic Target Recognition (ATR), etc. These applications usually involve the Eigenvalue Decomposition (EVD) of matrices that are time varying. It is desirable to have methods that eliminate the need to perform an EVD every time the matrix changes but instead update the EVD adaptively, starting from the initial EVD. In this paper, we propose a novel Optimal Adaptive Algorithm for the Modified EVD problem (OAMEVD). Sample results are presented for an ATR application, which uses Rayleigh Quotient Quadratic Correlation filters (RQQCF). Using a Infrared (IR) dataset, the effectiveness of this new technique as well as its advantages are illustrated.
Space-variant (SV) digital image restoration methods attempt to restore images degraded by blurs that vary over the image field. One specific source of SV blurs is that of geometrical optical aberrations, which divert light rays as they pass through the optical system away from an ideal focal point. For simple optical system, aberrations can become significant even at moderate field angles. Restoration methods have been developed for some space- variant aberrations when they are individually dominant, but such dominance is not typically characteristic of conventional optical systems. In this paper, an iterative method of restoration that is applicable to generalized, known space-variant blurs is applied to simulations of images generated with a spherical lines. The method is based on the Gauss-Seidel method of solution to systems of linear equations. The method is applied to sub-images having off- axis displacements of up to 453 pixels, and found to be superior in restoration effectiveness to Fourier methods in that range of field angles.
This paper presents a comparison of simultaneous suppression and pre-suppression of WNJ in conjunction with STAP. The comparison shows that: 1) under the ideal conditions and with the same number of spatial DOFs, the SINR performance of the two approach is similar, 2) the simultaneous suppression method is suitable for both element-space STAP and beamspace STAP, but the pre-suppression method is only suitable for beamspace STAP; 3) the simultaneous suppression method needs more samples for covariance matrix estimation, however, it is difficult for the pre-suppression method to obtain WNJ-only data, especially for medium PRF systems. In general, for medium PRF systems, the simultaneous suppression approach is recommended, and otherwise the pre- suppression approach is preferred.
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