Search

Motion error compensation is a crucial aspect of processing inverse synthetic aperture light detection and ranging data. Motion phase error occurs mainly due to the relative motion between the target and the system, as well as vibration of either the system or the target, which significantly affects the image quality of optical synthetic aperture radar. Since spatial targets usually have a non-cooperative motion state with a high degree of motion parameter uncertainty, accurate estimation of cross-range phase error becomes challenging due to the presence of envelope tilt effect. We propose an adaptive compensation method that handles motion errors of maneuvering targets by estimating and compensating various types of errors introduced by the target motion process. Using the geometric and signal models to analyze error components, a compensation model is established that uses envelope contrast and image entropy as fitness functions. The bat algorithm is employed to solve this error model. Simulation and outdoor experimental results demonstrate that the proposed algorithm offers higher accuracy and better stability compared to traditional optimization algorithms. [ABSTRACT FROM AUTHOR]

With regard to inverse synthetic aperture radar imaging with limited bandwidth and sparse aperture, it is a challenge to traditional range-Doppler (RD) algorithm. We proposed an innovative two-dimensional (2D) joint sparse imaging algorithm, namely, 2D fast orthogonal matching pursuit (2D-FOMP) algorithm. In the proposed algorithm, one-dimensional OMP (1D-OMP) is extended to 2D-OMP in the complex domain from three aspects of atom recognition, projection update, and residual update. Then, the equivalence between 1D-OMP and 2D-OMP is analyzed theoretically. Meanwhile, two strategies that multi atom recognition and matrix recursive update are added in 2D-OMP to further improve the reconstruction speed of 2D-FOMP. Experimental results based on both simulated and measured data demonstrate that the proposed algorithm has good imaging performance under noisy and sparse conditions. [ABSTRACT FROM AUTHOR]

We explore the blending of model-based and deep learning approaches for target recognition in inverse synthetic aperture radar (ISAR) imagery. It evaluates five different approaches, namely, a model-based geometric hashing approach, a supervised deep learning approach, and three different blending models that fuse the model-based and deep learning approaches. The model-based approach extracts scattering centers as features and requires domain experts to identify and characterize important features of a target, which makes the training process very costly and hard to generalize when the image quality degrades in low signal-to-interference-plus-noise-ratio conditions. Next, a deep learning algorithm using a convolutional neural network is considered to extract the spatial features when raw ISAR data are used as input. This approach does not need an expert and only requires the labels of images for training. Finally, three model-based and deep learning approaches are blended together at the feature level and decision level to benefit from the advantages of both approaches, achieving a higher performance. The results show that the blending of the two approaches achieves a high performance while providing explainable inferences. The performance of the five different approaches is evaluated under varying conditions of occlusion, clutter, masking of the target, and adversarial attacks. It is empirically shown that the model-based and deep learning approaches are able to complement each other and can achieve better classification accuracy upon fusing the integrated approach. [ABSTRACT FROM AUTHOR]

Time–frequency analysis is a fundamental tool in many applications, such as inverse synthetic aperture radar (ISAR) imaging along the cross-range direction. Traditional time–frequency transformations designed for the general signal suffer low time–frequency resolution or cross-term interference. In this study, a cascaded UNet-like network is applied to the refinement of basic transformations, and another forward regression network is proposed to estimate the signal parameters directly. Both networks can incorporate a priori information and combine different time–frequency transformations efficiently. Several experiments, especially in the ISAR application, are presented to validate the methods. Through this research, the neural network is a promising approach to develop a customized method with high performance for a specific signal processing problem. [ABSTRACT FROM AUTHOR]