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Long-time coherent pulse integration method has been extensively used in detecting weak targets, but the detection performance is affected by range migration (RM) due to high radial velocity. This study proposes a new modulation format of chirp radars with stepped frequency and a corresponding coherent integration method based on adjacent cross-correlation operation (ACCO) to compensate RMs of moving targets with, respectively, arbitrary unknown constant velocity. Simulation results show that the method has higher range resolution compared with bandwidth variation modulation and shorter blind distance compared with duration variation modulation.

Spectrum prediction is a promising technique to release spectrum resources and plays an essential role in cognitive radio networks and spectrum situation generating. Traditional algorithms normally focus on one-dimensional or predict spectrum values in a slot-by-slot manner and thus cannot fully perceive the spectrum states in complex environments and lack timeliness. In this paper, a deep learning-based prediction method with a simple structure is developed for temporal–spectral and multi-slot spectrum prediction simultaneously. Specifically, we first analyze and construct spectrum data suitable for the model to simultaneously achieve long-term and multi-dimensional spectrum prediction. Then, a hierarchical spectrum prediction system is developed that takes advantage of the advanced Bi-ConvLSTM and the seq2seq framework. The Bi-ConvLSTM captures time–frequency characteristics of spectrum data, and the seq2seq framework is used for long-term spectrum prediction. Furthermore, the attention mechanism is used to address the limitations of the seq2seq framework that compresses all inputs into fixed-length vectors, resulting in information loss. Finally, the experimental results have shown that the proposed model has a significant advantage over the benchmark schemes. Especially, the proposed spectrum prediction model achieves 6.15%, 0.7749, 1.0978, and 0.9628 in MAPE, MAE, RMSE, and R 2 , respectively, which is better than all the baseline deep learning models. [ABSTRACT FROM AUTHOR]

With the rapid development of the internet of things (IoT), hundreds of millions of IoT devices, such as smart home appliances, intelligent-connected vehicles, and wearable devices, have been connected to the network. The open nature of IoT makes it vulnerable to cybersecurity threats. Traditional cryptography-based encryption methods are not suitable for IoT due to their complexity and high communication overhead requirements. By contrast, RF-fingerprint-based recognition is promising because it is rooted in the inherent non-reproducible hardware defects of the transmitter. However, it still faces the challenges of low inter-class variation and large intra-class variation among RF fingerprints. Inspired by fine-grained recognition in computer vision, we propose a fine-grained RF fingerprint recognition network (FGRFNet) in this article. The network consists of a top-down feature pathway hierarchy to generate pyramidal features, attention modules to locate discriminative regions, and a fusion module to adaptively integrate features from different scales. Experiments demonstrate that the proposed FGRFNet achieves recognition accuracies of 89.8% on 100 ADS-B devices, 99.5% on 54 Zigbee devices, and 83.0% on 25 LoRa devices. [ABSTRACT FROM AUTHOR]

Coix seeds from 9 different origins were taken as the research object. An attempt was made to achieve geographical origin traceability of Coix seeds through the combination of excitation-emission matrix (EEM) fluorescence spectroscopy with improved random forest algorithm. The improvements to the random forest algorithm mainly include two aspects, firstly, principal component analysis (PCA) was adopted to reduce the dimension of EEM fluorescent data; secondly, a grid search method was used to identify the optimal number of principal components(PCs) to retain and the hyperparameters of the discriminant model during the PCA dimension reduction process. The results showed that an improved random forest model, incorporating standard deviation normalization and PCA dimension reduction modules, based on Coix seeds EEM fluorescence spectroscopy data, accurately predicted the geographical origin of Coix seed samples from 9 different areas. The optimal model was constructed by combining 100 decision trees with a maximum depth of 3 and a minimum sample size of 1 at the leaf node, using 16 principal components (PCs). This model achieved 100% prediction accuracy for both the validation and test sets, which consisted of a total of 108 samples, outperforming the PLS-DA model constructed by the partial least squares method (96% prediction accuracy). [ABSTRACT FROM AUTHOR]

In order to recover the three‐dimensional (3D) structure of the target from sequential inverse synthetic aperture radar (ISAR) images, the factorisation method is generally used. It requires a large number of high‐quality matched feature points from different ISAR images. If the number of extracted feature points is insufficient, the restored 3D structure is not obvious. Furthermore, the mismatching of feature points will greatly affect the quality of target reconstruction. However, the factorisation method only uses the information from the ISAR images, while that from imaging geometry is not sufficiently considered. ISAR imaging is a kind of projection, and the projection plane information could be taken into account for the 3D reconstruction. Hence, a new 3D reconstruction method for stable targets from sequential ISAR images is proposed in this paper. Firstly, the ISAR images are preprocessed with the CLEAN algorithm. The maximum between‐cluster variance method is applied to extract feature points from the processed images. Moreover, the image projection plane and projection equation corresponding to different ISAR images are analysed by using imaging geometry information. According to the projection equation, the feature points are back‐projected (BP) to the 3D space. Finally, the 3D point clouds obtained by the BP from multiple radar images are fused by the iterative closest point algorithm to restore the 3D structure of the target. The simulation and experiment results show the effectiveness and robustness of this method. [ABSTRACT FROM AUTHOR]

The false‐target echo generation to Inverse Synthetic Aperture Radar (ISAR) is significant in jamming the enemy ISAR and promoting ISAR development. Generally, it requires false‐target echo coherent with the radar, real time and fine. However, conventional methods, such as digital image synthesizer (DIS), cannot meet those requirements. Moreover, existing methods do not consider the target's radial moving. To meet those demands, we propose an improved method in this study. We equivalently model echo formation as the synthesizer of two independent parts: (1) echo of remote target with radial moving and (2) echo of nearby extended target. In part one, accuracy is improved by utilising the Inner Pulse Motion (IPM) model and complexity is simplified by deducing it as a frequency offset modulation. In part two, the fine extended target echo is constructed by using convolution filtering whose resources consumption can be greatly reduced by separating it into an offline stage and a real‐time stage. Our method is verified by algorithm simulations and actual experiments. The results indicate that it can build the fine false‐target echo in real‐time and can adapt to the target's radial velocity, different resolution and size. Compared with the conventional DIS method, our method reduces the computational complexity significantly and has more comprehensive functions. [ABSTRACT FROM AUTHOR]