Zhutian Yang

Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches.

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches.

Hybrid radar emitter recognition based on rough k-means classifier and SVM

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this article, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, i.e., the primary signal recognition and the advanced signal recognition. In the former step, the rough k-means classifier is proposed to cluster the samples of radar emitter signals by using the rough set theory. In the latter step, the samples within the rough boundary are used to train the support vector machine (SVM). Then SVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and has a lower time complexity than the traditional approaches.

CRB-RPL: A Receiver-Based Routing Protocol for Communications in Cognitive Radio Enabled Smart Grid

As a tool for overcoming radio spectrum shortages in wireless communications, cognitive radio technology plays a vital role in future smart grid applications, particularly in advanced metering infrastructure (AMI) networks with quality of service (QoS) requirements. This paper focuses on the investigation of the receiver-based routing protocol for enhancing QoS in cognitive radio-enabled AMI networks, due to their potentials of enhancing reliability and routing efficiency. In accordance with practical requirements of smart grid applications, a new routing protocol with two purposes is proposed: One is to address the real-time requirement while another protocol focuses on how to meet energy efficiency requirements. As a special feature of cognitive radio technology, the protocol has the mechanism for protecting primary (licensed) users while meeting the utility requirements of secondary (cognitive radio) users. System-level evaluation shows that the proposed routing protocol can achieve better performances compared with existing routing protocols for cognitive radio-enabled AMI networks.

A complexity-performance-balanced multiuser detector based on artificial fish swarm algorithm for DS-UWB systems in the AWGN and multipath environments

In this article, an efficient multiuser detector based on the artificial fish swarm algorithm (AFSA-MUD) is proposed and investigated for direct-sequence ultrawideband systems under different channels: the additive white Gaussian noise channel and the IEEE 802.15.3a multipath channel. From the literature review, the issues that the computational complexity of classical optimum multiuser detection (OMD) rises exponentially with the number of users and the bit error rate (BER) performance of other sub-optimal multiuser detectors is not satisfactory, still need to be solved. This proposed method can make a good tradeoff between complexity and performance through the various behaviors of artificial fishes in the simplified Euclidean solution space, which is constructed by the solutions of some sub-optimal multiuser detectors. Here, these sub-optimal detectors are minimum mean square error detector, decorrelating detector, and successive interference cancellation detector. As a result of this novel scheme, the convergence speed of AFSA-MUD is greatly accelerated and the number of iterations is also significantly reduced. The experimental results demonstrate that the BER performance and the near–far effect resistance of this proposed algorithm are quite close to those of OMD, while its computational complexity is much lower than the traditional OMD. Moreover, as the number of active users increases, the BER performance of AFSA-MUD is almost the same as that of OMD.

A Global Optimization-Based Routing Protocol for Cognitive-Radio-Enabled Smart Grid AMI Networks

Advanced metering infrastructure (AMI) networks, which are an integral component of the smart grid ecosystem, are practically deployed as a static multihop wireless mesh network. Recently, routing solutions for AMI networks have attracted a lot of attention in the literature. On the other hand, it is expected that the use of cognitive radio (CR) technology for AMI networks will be indispensable in near future. This paper investigates a global optimization-based routing protocol for enhancing quality of service in CR-enabled AMI networks. In accordance with practical requirements of smart grid applications, we propose a new RPL-based routing protocol, termed as directional mutation ant colony optimization-based cognitive RPL (DMACO-RPL), for CR-enabled AMI networks. This protocol utilizes a global optimization algorithm to select the best route from the whole network. In addition, DMACO-RPL explicitly protects primary (licensed) users while meeting the utility requirements of the secondary network. System-level simulations demonstrate that the proposed protocol enhances the performance of existing RPL-based routing protocols for CR-enabled AMI networks.

A Receiver-Based Routing Protocol for Cognitive Radio Enabled AMI Networks

It is expected that the use of cognitive radio for smart grid communication will be indispensable in near future. Recently, RPL for cognitive radio enabled Advanced Metering Infrastructure (AMI) networks is attractive. Our objective in this paper is to propose an enhance RPL to improve efficiency and reliability of cognitive radio enabled AMI networks. Our protocol is receiver-based in nature, which can achieve better reliability of the network along with protecting the primary users as well as meeting the utility requirements of secondary network. System level performance evaluation shows the effectiveness of proposed protocol as a viable solution for practical cognitive AMI networks.

A novel RBF neural network for radar emitter recognition based on Rough Sets

With the spreading of radar emitter technology, it is more difficult for traditional methods to recognize radar emitter signals. In this article, a new method is proposed to establish a novel radial basis function (RBF) neural network for radar emitter recognition based on Rough Sets theory. First of all, radar emitter signals describing words are processed by Rough Sets, and the importance weight of each attribute is obtained and the classification rules are extracted. The classification rules are the basis of initial centers of Rough k-means. These initial centers can reduce the computational complexity of Rough k-means efficiently because of a priori knowledge from Rough Sets. In addition, basis functions of neural units of an RBF neural network are improved with attribute importance weights based on Rough Sets theory. The novel network structure makes the RBF neural network more effective. The simulation results show that novel RBF neural network radar emitter recognition can recognize radar emitter signals more effectively than a traditional RBF neural network, because of the improved Rough k-means and the network structure with attribute importance weights.

Proactive jamming towards interference alignment networks : beneficial and adversarial aspects

Interference alignment (IA) is a prospective method to achieve interference management in wireless networks. On the other hand, jamming can be deemed either as a potential threat to degrade the performance of wireless networks, or as a helper to combat the eavesdropping for the legitimate networks. In this paper, we consider these two opposite scenarios, beneficial and adversarial jamming, toward IA networks, and based on which two proactive jamming schemes are proposed. In the first scheme, the jammer utilizes its precoding vector to constrain the jamming signal into the same subspace as the interference at each IA receiver, which will disrupt the potential eavesdropping significantly without affecting the transmission of IA users. Specifically, secure transmission can be guaranteed through the jamming without any additional cooperation with the IA users. In the second scheme, the jammer utilizes its precoding vector to project the jamming signal into the same subspace as that of the desired signal at each IA receiver secretly. Thus, the IA users cannot detect the concealed jamming signal, which will result in the performance degradation of the IA network. Extensive simulation results are presented to show the effectiveness of the two proposed jamming schemes toward IA networks.

Moving Target Recognition Based on Transfer Learning and Three-Dimensional Over-Complete Dictionary

In radar target recognition using high-resolution range profile, moving target recognition is a challenging issue, due to the target-aspect angle variation. To address the problem, two key issues need to be solved. First, we need to reflect the target moving status. Next, we need to find the common knowledge among different target-aspect angles. Accordingly, a novel moving target recognition based on three distribution over-complete dictionary in conjunction with transfer learning is proposed. Specifically, we propose a three distribution over-complete dictionary to represent the target and extract its moving status by dictionary learning. Moreover, we structure the feature set with generation among target-aspect angles by using a transfer learning method. This framework can be trained by using a small number of samples from limited target-aspect angles to recognize the targets of other target-aspect angles. Another advantage of this method is that it is robust against signal noise rate variation. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.