Shuhua Xu

Identification of Individual Radio Transmitters Based on Selected Surrounding-line Integral Bispectra

A novel method of identifying individual radio transmitters with the same model is proposed in this paper. The selected surrounding-line integral bispectra are utilized as the main feature parameters, and they are interfused with parameters significant for classification of the received signal to form the identification feature vector. A support vector machine (SVM) based on Gaussian core-function is implemented to realize the individual identification. Experimental results demonstrate that the method is able to classify the same model radio transmitter with an accuracy rate under the environment where there is low SNR.

Radio Transmitter Classification using a New Method of Stray Features Analysis Combined with PCA

This paper introduces an efficient technique to design a classifier for identifying radio transmitters with the same model. A new method, square integral bispectra(SIB), is used to extract the unique stray features of individual transmitted signal, and the principal component analysis(PCA) method is utilized further to extract a low-dimensional classification vector. Then,the modulation parameters of individual transmitter significant to classification are combined with the low-dimensional stray features to form a single characteristic feature vector that can effectively represent the target of concern over a broad range of sample data. A support vector machine(SVM) based on Gaussian kernel-function is implemented to realize the individual transmitter classification. The experiments on FSK radio transmitters demonstrate that the suggested technique is highly accurate and robust even in the presence of excessive noise and can solve the problem of identifying individual transmitters with the same model and manufacturing lot.

Individual radio transmitter identification based on spurious modulation characteristics of signal envelop

This paper introduces a novel and efficient technique to identify individual radio transmitters with the same model and manufacturing lot. The spurious modulation characteristic of individual radio transmitted signal is used to reflect the unique stray features of individual transmitters, and fractal dimensions of individual signal envelop are utilized to extract the identification feature vector. The experiments on FM radio transmitters demonstrate that the suggested technique is more accurate than conventional methods such as high-order moments. Also, the new method is computationally efficient and robust in the presence of excessive noise.

Distributed Authentication and Key Agreement Protocol for Ad hoc network

Ad hoc networks have developed rapidly due to its self-organization and stability, but its openness and mobility brings some problems in security. We present a trusted-computing based distributed virtual security zone (TC-DVSZ) model to ensure the security in ad hoc network with less computation and bandwidth. In the model, distributed authority (DA) has been introduced into the network to act as a CA in each cluster. To keep the DA trusted and reliable, we propose a trust based DA decision arithmetic. At the same time, we use ECC instead of RSA, and provide two level key agreement scheme, that decreases the cost of computation and increases the adaptability to various nodes with different computation capability. Finally, we prove that TC-DVSZ model is much more suitable for ad hoc network, because the model reduces the requirements for bandwidth, computation capacity and storage.

Novel method for estimating the instantaneous parameters of radio transmitter signals

Practical signals such as communication signals and speech turn out to be non-stationary and nonlinear time series, and estimating instantaneous parameters of such signals in time domain will benefit detecting and identifying radio transmitters, such as radio stations and aerospace targets. However, conventional methods such as Hilbert transform (HT) and Teages-Kaiser(T-K) methods fail to deal with these transient signals when there is a heavy signal distortion or very low SNR. To provide more efficient approach for estimating instantaneous parameters of non-stationary and nonlinear signals with high accuracy, a novel method based on orthogonal component reconstruction(OCR) for analytic signal is proposed in this paper. Both the further analysis and simulation results are presented to demonstrate the power and effectiveness of this novel method.

A novel approach for optical minimum shift keying signal generation

A novel approach for the generation of optical minimum shift keying (MSK) signals is presented. The transmitter is based on merely one phase modulator (PM), which is driven by a phase controller. The receiver uses direct detection based on the Mach-Zehnder delay interferometer (MZI) and optical balanced receiver. In this paper, firstly the detailed principle on optical MSK generation and detection is derived and analyzed. Then, 40Gb/s optical MSK signal based on this scheme is successfully generated and transmitted over several fiber links (SMF+DCF) with only the first order dispersion compensation. Finally, the performance of the MSK based on PM is compared with that of the MSK based on other two known schemes, in terms of waveform, phase and spectrum. Simulation results show that not only this approach has a simpler architecture for the optical MSK transmitter, and but also the optical MSK generated has better performance.

Classification and Diagnosis of Mechanical Faults Using the RBF Network Based on the Local Bispectra

This paper introduces an efficient technique to design a classifier for classifying and diagnosing mechanical faults. The bispectra features of vibration signals resulting from mechanical faults are extracted and then evaluated using the Fishers class-separability discriminant measure. The local bispectra with the most discriminant power and sensitivity are selected as the classification feature vector that can effectively represent the fault class of concern over a broad range of sample data. A RBF neural network is implemented to realize identification and diagnosis for different mechanical faults. The suggested technique is demonstrated to design a classifier for fault signals of rolling bearings that is verified to be highly accurate and robust even in the presence of excessive noise.

Optical CPFSK with an arbitrary modulation index

This paper proposes an optical continuous-phase frequency shift keying (CPFSK) modulation scheme with an arbitrary modulation index. Simulations on the performances of CPFSK and differential phase shift keying (DPSK) show that CPFSK of modulation index is 2/3 performs best on receiver sensitivity, dispersion penalty and nonlinearity in the balanced receiving system of 40G.

A quaternary minimum shift keying for high-speed optical communication

This paper presents the generation and the performance evaluation of an optical quadrature minimum shift keying (QMSK). The performance of our optical QMSK modulated system is evaluated and compared with those of traditional phase shift keying (PSK) and minimum shift keying (MSK) modulated systems, via simulation, in terms of receiver sensitivity, dispersion tolerance and self-phase modulation (SPM) penalty. The proposed scheme shows that the QMSK format offers a few transmission advantages comparing with than that of the other traditional modulation formats under SPM penalty.

Feature Bispectra and RBF Based FM Signal Recognition

Automatic communication signal (e.g., FM signal) classification and identification focus on finding the fine feature contained in the almost approximate noisy communication signal comprehensively identifying the the same or different version of transmitters in modern electronic warfare. Direct use of HOS becomes unavailable for on-line application because of its huge computation time and memory space especially in the case of high frequency FM signal. This paper presents a novel view to improve the HOS analysis efficiency by sub-sampling while preserving the noise-contaminated fine feature and eliminating the random Gaussian noise. FM signal-related feature bispectra are also introduced to translate the 2-D feature matching pattern to a 1-D one applicable for an optimal adaptive k-means iterative RBF classifier. Computer simulations show that this novel feature bispectra outperform AIB and SB in terms of computation time and recognition rate for on-line steady FM signal recognition.