Zhendong Yin

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.

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.

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.

Novel Spectrum Sensing Algorithms for OFDM Cognitive Radio Networks

Spectrum sensing technology plays an increasingly important role in cognitive radio networks. Consequently, several spectrum sensing algorithms have been proposed in the literature. In this paper, we present a new spectrum sensing algorithm “Differential Characteristics-Based OFDM (DC-OFDM)” for detecting OFDM signal on account of differential characteristics. We put the primary value on channel gain θ around zero to detect the presence of primary user. Furthermore, utilizing the same method of differential operation, we improve two traditional OFDM sensing algorithms (cyclic prefix and pilot tones detecting algorithms), and propose a “Differential Characteristics-Based Cyclic Prefix (DC-CP)” detector and a “Differential Characteristics-Based Pilot Tones (DC-PT)” detector, respectively. DC-CP detector is based on auto-correlation vector to sense the spectrum, while the DC-PT detector takes the frequency-domain cross-correlation of PT as the test statistic to detect the primary user. Moreover, the distributions of the test statistics of the three proposed methods have been derived. Simulation results illustrate that all of the three proposed methods can achieve good performance under low signal to noise ratio (SNR) with the presence of timing delay. Specifically, the DC-OFDM detector gets the best performance among the presented detectors. Moreover, both of the DC-CP and DC-PT detector achieve significant improvements compared with their corresponding original detectors.

A Hybrid Multiuser Detection Algorithm for Outer Space DS-UWB Ad-hoc Network with Strong Narrowband Interference

Formation flying is an important technology that enables high cost-effective organization of outer space aircrafts. The ad-hoc wireless network based on direct-sequence ultra-wideband (DS-UWB) techniques is seen as an effective means of establishing wireless communication links between aircrafts. In this paper, based on the theory of matched filter and error bits correction, a hybrid detection algorithm is proposed for realizing multiuser detection (MUD) when the DS-UWB technique is used in the ad-hoc wireless network. The matched filter is used to generate a candidate code set which may contain several error bits. The error bits are then recognized and corrected by an novel error-bit corrector, which consists of two steps: code mapping and clustering. In the former step, based on the modified optimum MUD decision function, a novel mapping function is presented that maps the output candidate codes into a feature space for differentiating the right and wrong codes. In the latter step, the codes are clustered into the right and wrong sets by using the K-means clustering approach. Additionally, in order to prevent some right codes being wrongly classified, a sign judgment method is proposed that reduces the bit error rate (BER) of the system. Compared with the traditional detection approaches, e.g., matched filter, minimum mean square error (MMSE) and decorrelation receiver (DEC), the proposed algorithm can considerably improve the BER performance of the system because of its high probability of recognizing wrong codes. Simulation results show that the proposed algorithm can almost achieve the BER performance of the optimum MUD (OMD). Furthermore, compared with OMD, the proposed algorithm has lower computational complexity, and its BER performance is less sensitive to the number of users.

A new optimal MUD algorithm based on searching in the local space of sub-optimal MMSE solutions for DS-UWB communication systems

In this paper, we have studied the multiuser detection (MUD) technology applied in multiaccess (MA) direct sequence ultrawideband (DS-UWB) systems. Some typical MUD algorithms were analyzed and a new approximate optimal MUD algorithm based on searching in the local space of suboptimal minimum mean-squared error (MMSE) solution was proposed. In this new algorithm, the suboptimal solution of MMSE is considered as the initial value for the local search, and then a local solution space of this suboptimal solution is constructed by Euclidean distance. Within this space, some likely solutions for the classical optimal MUD algorithm (OMUD) are searched and the optimal one of them is chosen as the solution of this new optimal MUD algorithm. Theoretical analysis and simulation results indicate that, the BER performance and the Near-far Effect resistant ability of this proposed algorithm are much better than those of MMSE and adaptive MMSE detections, which are very close to those of OMUD. Furthermore, its time complexity is much lower than OMUDs. Compared to adaptive MMSE, this algorithm has no difficulty in choosing the suitable parameters for adaptive MMSE. Besides these, we examine the application of this new algorithm to the problem of suppressing the digital narrowband interference (NBI). It is clearly seen that this approximate optimal algorithm has perfect performance under strong NBI circumstance and coincides with the classical OMUD algorithm bound in the same condition.

A new design of pulse waveform for waveform division multiple access UWB wireless communication system

Waveform Division Multiple Access (WDMA) based on the orthogonal wavelet function is recently presented due to its admirable characteristics. However, the communication performance of the existing waveform of the system breaks down drastically as the number of users increases. In order to meet the communication requirement for WDMA-UWB system with massive users, a new design of pulse waveform is proposed in this paper. The correlation property and Bit Error Rate (BER) performance is raised apparently and close to that of the single-user system.

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.

Dynamic Relay Satellite Scheduling Based on ABC-TOPSIS Algorithm

Tracking and Data Relay Satellite System (TDRSS) is a space-based telemetry, tracking, and command system, which represents a research field of the international communication. The issue of the dynamic relay satellite scheduling, which focuses on assigning time resource to user tasks, has been an important concern in the TDRSS system. In this paper, the focus of study is on the dynamic relay satellite scheduling, whose detailed process consists of two steps: the initial relay satellite scheduling and the selection of dynamic scheduling schemes. To solve the dynamic scheduling problem, a new scheduling algorithm ABC-TOPSIS is proposed, which combines artificial bee colony (ABC) and technique for order preference by similarity to ideal solution (TOPSIS). The artificial bee colony algorithm is performed to solve the initial relay satellite scheduling. In addition, the technique for order preference by similarity to ideal solution is adopted for the selection of dynamic scheduling schemes. Plenty of simulation results are presented. The simulation results demonstrate that the proposed method provides better performance in solving the dynamic relay satellite scheduling problem in the TDRSS system.

The relay satellite scheduling based on artificial bee colony algorithm

The relay satellite scheduling is a main content in Tracking and Data Relay Satellite System (TDRSS). How to build and solve the scheduling models of the relay satellite is the key to the relay satellite scheduling problem. In this paper, a relay satellite scheduling based on artificial bee colony algorithm is proposed. Firstly, scheduling model of the relay satellite daily task is proposed which is NP-hard as one of combinatorial optimization problems. Then the artificial bee colony (ABC) algorithm is given and is used to solve the relay scheduling problem. Finally, some simulation results are presented. In comparison with other swarm intelligence algorithms, the artificial bee colony algorithm provides better fits to solve relay satellite scheduling problem.