Improved phase gradient autofocus algorithm based on segments of variable lengths and minimum‐entropy phase correction

Abstract

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is an essential tool for modern remote sensing applications. Owing to its size and weight constraints, UAV is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this study, an improved phase gradient autofocus (PGA) motion compensation approach is proposed for UAV-SAR imagery. The approach is implemented in two steps. The first step determines the length of each segment depending on number of good quality scatterers and motion errors obtained from navigation data. In the second step, a novel minimum-entropy phase correction based on the discrete cosine transform (DCT) coefficients is proposed. In this approach, transform phase error estimated by PGA to DCT-coefficients that represent the phase error in the frequency or time-frequency domain. The entropy of a focused image is utilised as the optimisation function of the DCT-coefficients to improve the final image quality. Finally, real-data experiments show that the proposed approach is appropriate for highly precise imaging of UAV-SAR equipped with only low-accuracy inertial navigation systems.

Range‐dependent semi‐parametric autofocusing for manoeuvring targets inverse synthetic aperture imagery

“…For algorithm comparison, typical autofocus algorithms in ISAR imagery are also used to process the data. MAMD algorithm [14], PGA algorithm [7], and the original semi‐parametric autofocus method without a range‐dependent model original semi‐parametric (OSP) [18] are chosen for comparing with the proposed algorithm.…”

“…Recently, semi‐parametric methods [17, 18] have been developed providing an essential manner to adjust the phase error model, which combines the advantages of parameterisation and non‐parameterisation. Semi‐parametric methods reduce the strong dependence on the scene, and it is capable of high‐order motion error compensation.…”

“…The method designed to the unmanned underwater vehicle (UUV)‐borne SAS system, and compensates fast oscillating navigation and vibration errors. In [18], the authors improve PGA by using discrete cosine transform (DCT) to descript phase errors. It utilises a linear combination of limited DCT kernel to reconstruct the phase errors, and do not directly estimate the phase errors of each azimuth moment.…”

Focusing High-Squint Synthetic Aperture Radar Data Based on Factorized Back-Projection and Precise Spectrum Fusion

“…In each image pair, the left image is obtained by AFBP, while the right one is generated by HS-AFBP. The image entropy values [31,32] are shown at the title of each image in Figure 14, where the right images always have smaller entropy values. Now it is more obvious that the left images are less focused than the right ones in Figure 14, which reflects that the proposed HS-AFBP algorithm outperforms the conventional AFBP in terms of focusing ability.…”

Precise aperture‐dependent motion compensation for high‐resolution synthetic aperture radar imaging

“…Precise motion compensation (MOCO) [1, 2] is a crucial task for synthetic aperture radar [3, 4] imaging. The trajectory of the platform deviates from pre‐determined flight course due to the unstable airflows [5, 6], which causes serious defocusing in the synthetic aperture radar (SAR) imagery.…”