Traditional Super-resolution Algorithms Research Articles

DOA Estimation of Multiple Coherent Targets Using Weight Vector Orthogonal Decomposition in TDM-MIMO HF-Radar

In recent years, MIMO high-frequency surface wave radar (MIMO-HFSWR) plays an increasingly important role in sea surveillance because of its wide-area surveillance capabilities. In signal processing, the coherent targets with the same distance and speed can only be distinguished in the angle dimension. However, HF radar’s angular resolution is poor because of the restrictions of the aperture, which causes aliasing of targets on the range–angle (RA) spectrum. Traditional super-resolution algorithms, such as MUSIC, are also inapplicable because of the targets’ coherence. Therefore, the spatial smoothing algorithm is usually used to realize the decoherence of echo at the cost of array aperture. The loss of array aperture makes the algorithm fail when there are too many targets in the echo. Aiming at this problem, this paper proposes an iterative calculation method (iterative calculation via weight vector orthogonal decomposition, IC-WORD) to estimate the angle of multiple coherent targets. Through the verification of simulation, it is proved that IC-WORD has better performance than the traditional spatial smoothing algorithm. More specifically, IC-WORD can correct spectral peak shift, improve angle measurement accuracy, and still operate stably under the condition of multiple coherent targets. This paper also uses the measured data from one of the most advanced HFSWR stations in China to validate the algorithm, which makes the paper have strong practical significance.

A Review of Hyperspectral Image Super-Resolution Based on Deep Learning

Hyperspectral image (HSI) super-resolution (SR) is a classical computer vision task that aims to accomplish the conversion of images from lower to higher resolutions. With the booming development of deep learning (DL) technology, more and more researchers are dedicated to the research of image SR techniques based on DL and have made remarkable progress. However, no scholar has provided a comprehensive review of the field. As a response, in this paper we aim to supply a comprehensive summary of the DL-based SR techniques for HSI, including upsampling frameworks, upsampling methods, network design, loss functions, representative works with different strategies, and future directions, in which we design several sets of comparative experiments for the advantages and limitations of two-dimensional convolution and three-dimensional convolution in the field of HSI SR and analyze the experimental results in depth. In addition, the paper also briefly discusses the secondary foci such as common datasets, evaluation metrics, and traditional SR algorithms. To the best of our knowledge, this paper is the first review on DL-based HSI SR.

Super-resolution algorithm based on sparse representation and wavelet preprocessing for remote sensing imagery

An effective super-resolution (SR) algorithm is proposed for actual spectral remote sensing images based on sparse representation and wavelet preprocessing. The proposed SR algorithm mainly consists of dictionary training and image reconstruction. Wavelet preprocessing is used to establish four subbands, i.e., low frequency, horizontal, vertical, and diagonal high frequency, for an input image. As compared to the traditional approaches involving the direct training of image patches, the proposed approach focuses on the training of features derived from these four subbands. The proposed algorithm is verified using different spectral remote sensing images, e.g., moderate-resolution imaging spectroradiometer (MODIS) images with different bands, and the latest Chinese Jilin-1 satellite images with high spatial resolution. According to the visual experimental results obtained from the MODIS remote sensing data, the SR images using the proposed SR algorithm are superior to those using a conventional bicubic interpolation algorithm or traditional SR algorithms without preprocessing. Fusion algorithms, e.g., standard intensity-hue-saturation, principal component analysis, wavelet transform, and the proposed SR algorithms are utilized to merge the multispectral and panchromatic images acquired by the Jilin-1 satellite. The effectiveness of the proposed SR algorithm is assessed by parameters such as peak signal-to-noise ratio, structural similarity index, correlation coefficient, root-mean-square error, relative dimensionless global error in synthesis, relative average spectral error, spectral angle mapper, and the quality index Q4, and its performance is better than that of the standard image fusion algorithms.

Joint DOD and DOA Estimation for High Speed Target Using Bistatic MIMO Radar

In bistatic multiple-input multiple-output (MIMO) radar, range migration and invalidly synthesized virtual array resulting from the serious mismatch of matched filter make it difficult to estimate direction of departure (DOD) and direction of arrival (DOA) of high speed target using the traditional superresolution algorithms. In this study, a method for joint DOD and DOA estimation of high speed target using bistatic MIMO radar is proposed. After multiplying the received signals with the conjugate of the delayed versions of the transmitted signals, Fourier transform (FT) of the multiplied signals over both fast time and slow time is employed. Then, the target components of radar return corresponding to the different transmitted waveforms can be perfectly separated at the receivers by extracting the target frequency-domain data along slow-time frequency dimension when the delay between the transmitted signals and their subsequent returns is timed. By splicing the separated target components distributed along several range cells, the virtual array can be formed, and then DOD and DOA of high speed target can be estimated using the superresolution algorithm with the range migration and the mismatch of matched filter properly removed. Simulation results have proved the validity of the proposed algorithm.