Contrast maximisation based technique for 2-D ISAR autofocusing

Abstract

An image contrast based algorithm for 2-D ISAR image autofocusing is proposed. The problem of ISAR image autofocusing is formulated analytically by defining geometry and dynamics of the radar-target system and by assuming a mathematical model for the received signal. The image focusing is then achieved by estimating the model parameters through the maximisation of the image contrast. The problem of the maximum search is solved numerically by means of an iterative search method. An algorithm able to produce an accurate initial guess is also developed by using the radon transform. The good accuracy of the initial guess guarantees the convergence of the optimisation problem solution to the global maximum. The performance of the proposed autofocusing technique is tested by comparing it to the point prominent processing (PPP) algorithm, the phase gradient algorithm (PGA) and the image entropy based technique (IEBT), through the use of real data. Results confirm the effectiveness of the proposed algorithm. 2 Mathematical aspects of signal modelling and ISAR processing Let the system geometry be represented by Fig. 1 where the radar is located at ð0; 0; h r Þ in the system of coordinates

A Robust Sub-Integer Range Alignment Algorithm Against MTRC for Isar Imaging

“…(19) and (20), the only difference is the number of range cells. As for the range alignment among matrix blocks, suppose the M × N matrix is partitioned into N m matrix blocks.…”

“…Based on the adopted criterion, the already existed range alignment algorithms in open articles can be divided into four kinds, namely, cross-correlation [1,6,7], entropy minimization [8,9], contrast maximization [10] and frequency-domain method [1]. In terms of phase adjustment, the prominent scatterer algorithm [11], doppler centroid tracking [12], phase gradient autofocus [13], entropy minimization [14][15][16][17][18][19] and contrast maximization [18,20] are regarded as the outstanding representatives.…”

Precise Radial Velocity Estimation for Inverse Synthetic Aperture Radar

“…Phase adjustment is to remove the error phase after range alignment is done well. There have been a lot of schemes to carry out phase adjustment [28][29][30][31][32]. After phase adjustment, the phase error of two aligned adjacent echoes is achieved, assumed to be Δϕ(m) rad, which means that…”

Segment Noncoherent Integration Based Inverse Synthetic Aperture Radar Imaging Under Low Signal-to-Noise Ratio

“…Some robust optimization-based methods [3][4][5] still have not made a contribution to suppress the noise in the range cell. On the contrary, phase adjustment [6][7][8][9][10][11] contains the accumulation of the phase of the HRRPs and suppresses the noise in the Doppler cell. Hence, the improvement of range alignment is the bottleneck under low SNR condition.…”

“…Hence, the improvement of range alignment is the bottleneck under low SNR condition. Joint correction methods for simultaneous range alignment and phase adjustment have also been proposed based on a polynomial model of the translational range history, which have the advantage of high SNR gain from 2D coherent integration [11][12][13][14][15]. However, for long coherent processing interval (CPI), the order of the polynomial should be high enough to model the real motion, which causes high computational complexity.…”