Object detection is a basic part in remote sensing image processing. At present, it is more common to conduct the topic based on deep learning, however the volume of remote sensing images has become ...a limitation. In order to solve the problem of small sample of remote sensing image, transfer learning is combined with deep learning in the research. First, the detection problem is caused by insufficient data, such as over-fitting, which is solved by model-based transfer learning. The structure of models and parameters obtained based on natural images are transferred to the detection task in remote sensing target domain. In addition, it is usually assumed that the distribution of training data and the testing data are the same in detection, but this is not the case. Therefore, how to improve the robustness of training models and widen the scope of application should be taken into consideration. In the research, Domain Adaptation Faster R-CNN (DA Faster R-CNN) algorithm is proposed for detecting aircraft in remote sensing images. Two domain adaptation structures are designed and selected as the criterion of similarity measurement between domains. Adversarial training is applied to alleviate the domain shift. Finally, the effectiveness of the algorithm is certified in the low brightness experiment. DA Faster R-CNN detection algorithm improves the accuracy of the original algorithm for low quality images. It is worth noting that the DA Faster R-CNN algorithm is a kind of unsupervised transfer learning method for remote sensing object detection.
Intra-pulse modulation recognition of radar signals is an important part of modern electronic intelligence reconnaissance and electronic support systems. With the increasing density of radar signals, ...the analysis and processing of multi-component radar signals have become an urgent problem in the current radar reconnaissance system. In this paper, an intra-pulse modulation recognition approach for single-component and dual-component radar signals is proposed. First, in order to adapt to the time-frequency energy distribution characteristics of various radar signals, we propose to extract the time-frequency images (TFIs) of received signals by Cohen class time-frequency distribution (CTFD) with multiple kernel functions. Besides, the image processing methods are used to suppress noise and adjust the size and amplitude of the TFIs. Second, we design and pre-train a TFI feature extraction network for radar signals based on a convolutional neural network (CNN). Finally, to improve the probability of successful recognition (PSR) of the recognition system in the pulse overlapping environment, a multi-label classification network based on a deep Q-learning network (DQN) is explored. Besides, two sub-networks take TFIs based on special kernel functions as input and re-judge the recognition results of some specific signals to further enhance the recognition effect of the recognition system. The proposed approach can identify 8 kinds of random overlapping radar signals. The simulation results show that the overall PSR of dual-component radar signals and single-component radar signals can reach 94.83% and 94.43%, respectively, when the signal-to-noise ratio (SNR) is -6 dB.
In response to the demand for high-quality electronic information talents in the mobile network industry, in the situation of artificial intelligence (AI) to promote technological innovation, this ...paper conducts an overall design in the target system, curriculum system, teaching platform, teaching mode and teaching case. The practice education mode of teaching practice, engineering practice, innovation practice, and enterprise practice, which aims to improve students’ ability to solve complex engineering problems, is constructed. The mode breaks geographical boundaries between schools and enterprises to build the through-through experimental teaching course system based on artificial intelligence and edge computing and design a medical image intelligent analysis system project case based on Mobile Edge Computing (MEC), which improves students’ practical ability, engineering design ability, scientific research innovation ability, enterprise practice ability and mobile network application capabilities. At the same time, the hardware portability of the edge computing platform provides good conditions for long-distance education and the mobile network. This method is a beneficial attempt to cultivate high-level, diversified, and creative electronic information talents.
With the increasing application of computer vision technology in autonomous driving, robot, and other mobile devices, more and more attention has been paid to the implementation of target detection ...and tracking algorithms on embedded platforms. The real-time performance and robustness of algorithms are two hot research topics and challenges in this field. In order to solve the problems of poor real-time tracking performance of embedded systems using convolutional neural networks and low robustness of tracking algorithms for complex scenes, this paper proposes a fast and accurate real-time video detection and tracking algorithm suitable for embedded systems. The algorithm combines the object detection model of single-shot multibox detection in deep convolution networks and the kernel correlation filters tracking algorithm, what is more, it accelerates the single-shot multibox detection model using field-programmable gate arrays, which satisfies the real-time performance of the algorithm on the embedded platform. To solve the problem of model contamination after the kernel correlation filters algorithm fails to track in complex scenes, an improvement in the validity detection mechanism of tracking results is proposed that solves the problem of the traditional kernel correlation filters algorithm not being able to robustly track for a long time. In order to solve the problem that the missed rate of the single-shot multibox detection model is high under the conditions of motion blur or illumination variation, a strategy to reduce missed rate is proposed that effectively reduces the missed detection. The experimental results on the embedded platform show that the algorithm can achieve real-time tracking of the object in the video and can automatically reposition the object to continue tracking after the object tracking fails.
With the rapid development of in-vehicle electronic technology and artificial intelligence, Internet of Vehicles (IoV) technology, as an effective integration of the two, greatly reduces the ...probability of road traffic accidents. However, the current IoV system is not perfect for the control process of abnormal vehicles. Therefore, to strengthen the management and control of abnormal vehicles in the IoV, it is extremely necessary to propose a method for interfering with IoV signals. Among the current popular intelligent interference methods, most of them rely on the prior knowledge of the signal. However, prior knowledge is difficult to obtain in practical applications. Therefore, in view of the shortcomings of the current communication interference technology, this study introduces intelligent interference into the signal processing technology of the IoV. When the service provider identifies abnormal signals, the intelligent interference method can be used to achieve precise interference for a single target and then realize the control of the IoV signals. This study proposes an interference waveform generation technology based on convolutional autoencoders. The convolutional autoencoder was used to change the features on the fully connected layer to generate an interference waveform that is very similar to the received signal waveform. The simulation results show that the interference waveform generation technology proposed in this study can make the bit error rate (BER) reach 38.4% within the signal-to-interference ratio (SIR) from − 10 to − 15 dB.
Radar signal intra-pulse modulation recognition is an important technology in electronic warfare. A radar signal intra-pulse modulation recognition method based on convolutional denoising autoencoder ...(CDAE) and deep convolutional neural network (DCNN) is proposed in this paper. First, we use Cohen's time-frequency distribution to convert radar signals into time-frequency images (TFIs). Then image preprocessing is applied to TFIs, including bilinear interpolation and amplitude normalization. Next, we design a CDAE to denoise and repair TFIs. Finally, we design a deep convolutional neural network based on Inception architecture to identify the processed TFIs. Simulation results demonstrate that CDAE effectively reduces the interference of noise on TFIs classification, and improves the classification performance at a low signal-to-noise ratio (SNR). The DCNN architecture we designed makes good use of computing resources and has a good classification effect. The approach has good noise immunity and generalization. It can classify twelve kinds of modulation signals and an overall probability of successful recognition is more than 95% when the SNR is −9 dB.
With the rapid development of wireless devices in recent years, the hardware tolerance of wireless devices has gradually become narrowed. Traditional radio frequency fingerprint(RF fingerprint) ...recognition methods are usually used based on single signal features, which will fail to characterize the subtle differences of wireless devices. Therefore, aiming at the shortcomings of traditional radio frequency fingerprint recognition methods, a multi-segment fusion recognition model is proposed based on D-S evidence theory. The fusion features of time-domain RF-DNA and high-order spectral features are used to obtain more accurate radio frequency fingerprint features. Simulation experiments show that the fusion method can significantly improve the recognition performance of traditional fingerprint recognition methods. When the SNR is higher than 5 dB, with the increasing number of signal fusion segment, the recognition rate of the proposed model will be higher than 99%, which prove that it has a better performance and can be used in practice.
Nested arrays are considered attractive due to their hole-free performance, and have the ability to resolve O ( N 2 ) sources with O ( N ) physical sensors. Inspired by nested arrays, two kinds of ...three-parallel nested subarrays (TPNAs), which are composed of three parallel sparse linear subarrays with different inter-element spacings, are proposed for two-dimensional (2-D) direction-of-arrival (DOA) estimation in this paper. We construct two cross-correlation matrices and combine them as one augmented matrix in the first step. Then, by vectorizing the augmented matrix, a hole-free difference coarray with larger degrees of freedom (DOFs) is achieved. Meanwhile, sparse representation and the total least squares technique are presented to transform the problem of 2-D DOA searching into 1-D searching. Accordingly, we can obtain the paired 2-D angles automatically and improve the 2-D DOA estimation performance. In addition, we derive closed form expressions of sensor positions, as well as number of sensors for different subarrays of two kinds of TPNA to maximize the DOFs. In the end, the simulation results verify the superiority of the proposed TPNAs and 2-D DOA estimation method.
Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from ...grid mismatch problem due to the discretization of the potential angle space, which will cause DOA estimation performance degradation when the target is off-grid. To this end, we proposed a sparse-based off-grid DOA estimation method for coprime arrays in this paper, which includes two parts: coarse estimation process and fine estimation process. In the coarse estimation process, the grid points closest to the true DOAs, named coarse DOAs, are derived by solving an optimization problem, which is constructed according to the statistical property of the vectorized covariance matrix estimation error. Meanwhile, we eliminate the unknown noise variance effectively through a linear transformation. Due to finite snapshots effect, some undesirable correlation terms between signal and noise vectors exist in the sample covariance matrix. In the fine estimation process, we therefore remove the undesirable correlation terms from the sample covariance matrix first, and then utilize a two-step iterative method to update the grid biases. Combining the coarse DOAs with the grid biases, the final DOAs can be obtained. In the end, simulation results verify the effectiveness of the proposed method.
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