Laizhao Hu

Radar Emitter Signal Recognition Based on Resemblance Coefficient Features

Resemblance coefficient (RC) feature extraction approach for radar emitter signals was proposed. Definition and properties of RC were given. Feature extraction algorithm based on RC was described in detail and the performances of RC features were also analyzed. Neural network classifiers were designed. Theoretical analysis results and simulation experiments of 9 typical radar emitter signal feature extraction and recognition show that RC features are not sensitive to noise and average accurate recognition rate rises to 99.33%, which indicates that the proposed approach is effective.

A novel parallel quantum genetic algorithm

We propose a novel parallel evolutionary algorithm called coarse-grained parallel quantum genetic algorithm (CGPQGA). The main points of CGPQGA are that a new chromosome representation called qubit representation, a novel evolutionary strategy called qubit phase comparison approach and an extended version of coarse-grained model called hierarchical ring model are introduced. Based on the concepts and principles of quantum computing and quantum parallelism introduced, CGPQGA is characterized by rapid convergence, good global search capability and the ability of possessing exploration and exploitation simultaneously. In CGPQGA, the best individual can be easy to migrate to all processors and communication overhead is much less expensive. The experimental results of infinite impulse response digital filter design demonstrate that CGPQGA can speedup the migration of the top individuals of subpopulations and CGPQGA is superior to other several genetic algorithms greatly in quality and efficiency.

Resemblance Coefficient and a Quantum Genetic Algorithm for Feature Selection

Feature selection is always an important and difficult issue in pattern recognition, machine learning and data mining. In this paper, a novel approach called resemblance coefficient feature selection (RCFS) is proposed. Definition, properties of resemblance coefficient (RC) and the evaluation criterion of the optimal feature subset are given firstly. Feature selection algorithm using RC criterion and a quantum genetic algorithm is described in detail. RCFS can decide automatically the minimal dimension of good feature vector and can select the optimal feature subset reliably and effectively. Then the efficient classifiers are designed using neural network. Finally, to bring into comparison, 3 methods, including RCFS, sequential forward selection using distance criterion (SFSDC) and a new method of feature selection (NMFS) presented by Tiejun Lu are used respectively to select the optimal feature subset from original feature set (OFS) composed of 16 features of radar emitter signals. The feature subsets, obtained from RCFS, SFSDC and NMFS, and OFS are employed respectively to recognize 10 typical radar emitter signals in a wide range of signal-to-noise rate. Experiment results show that RCFS not only lowers the dimension of feature vector greatly and simplifies the classifier design, but also achieves higher accurate recognition rate than SFSDC, NMFS and OFS, respectively.

Quantum computing based machine learning method and its application in radar emitter signal recognition

Feature selection plays a central role in data analysis and is also a crucial step in machine learning, data mining and pattern recognition. Feature selection algorithm focuses mainly on the design of a criterion function and the selection of a search strategy. In this paper, a novel feature selection approach (NFSA) based on quantum genetic algorithm (QGA) and a good evaluation criterion is proposed to select the optimal feature subset from a large number of features extracted from radar emitter signals (RESs). The criterion function is given firstly. Then, detailed algorithm of QGA is described and its performances are analyzed. Finally, the best feature subset is selected from the original feature set (OFS) composed of 16 features of RESs. Experimental results show that the proposed approach reduces greatly the dimensions of OFS and heightens accurate recognition rate of RESs, which indicates that NFSA is feasible and effective.

A novel genetic algorithm and its application to digital filter design

When quantum-inspired genetic algorithm (QGA) is used to solve continuous function optimization problems, there are several shortcomings, such as non-determinability of lookup table of updating quantum gates, requiring prior knowledge of the best solution and premature phenomenon. So novel quantum genetic algorithm (NQGA) is proposed in this paper to solve continuous function optimization problems. The core of NQGA is that a new evolutionary strategy including qubit phase comparison approach to update quantum gates, adaptive search grid and catastrophe-mutation method is introduced. NQGA has good capability of balancing exploration and exploitation and has some excellent characteristics of both good global search capability and good local search capability, rapid convergence. And the convergence of NQGA is also analyzed in this paper. The results from the tests of several typically complex functions and experimental results of digital filter design demonstrate that NQGA is superior to several conventional genetic algorithms (CGAs) greatly in optimization quality and efficiency.

Discretization of continuous attributes in rough set theory and its application

Existing discretization methods cannot process continuous interval-valued attributes in rough set theory. This paper extended the existing definition of discretization based on cut-splitting and gave the definition of generalized discretization using class-separability criterion function firstly. Then, a new approach was proposed to discretize continuous interval-valued attributes. The introduced approach emphasized on the class-separability in the process of discretization of continuous attributes, so the approach helped to simplify the classifier design and to enhance accurate recognition rate in pattern recognition and machine learning. In the simulation experiment, the decision table was composed of 8 features and 10 radar emitter signals, and the results obtained from discretization of continuous interval-valued attributes, reduction of attributes and automatic recognition of 10 radar emitter signals show that the reduced attribute set achieves higher accurate recognition rate than the original attribute set, which verifies that the introduced approach is valid and feasible.

Quantum evolutionary algorithm for multi-objective optimization problems

In this paper, a novel evolutionary algorithm called new quantum evolutionary algorithm (NQEA) is proposed to solve a class of multi-objective optimization problems. The main point of NQEA is that a new quantum logic rotation gate is introduced. NQEA characterizes rapid convergence, good global search capability and short computing time. Then, the convergence of NQEA is also analyzed using random functional theory. The results from optimization design of IIR digital filters demonstrate that NQEA is superior to other several conventional evolutionary algorithms greatly in quality and efficiency.

Complexity feature extraction of radar emitter signals

Because the intra-pulse modulation laws of radar emitter signals reflect directly on the signal waveform, a novel approach is presented to extract the complexity features (CFs) of radar emitter signals. CFs, including Lempel-Ziv complexity (LZC) and correlation dimension (CD), can measure the complexity and irregularity of radar signals effectively. To show the effectiveness and feasibility of the introduced approach, CFs of 10 typical radar emitter signals are extracted in our experiments. The results demonstrate that the CFs have good characteristics for clustering the same radar signals and separating different radar signals, which indicates that CF can simplify the design procedure of classifiers and greatly heighten the identification accuracy rate of radar emitter signals.

Radar emitter signal recognition based on support vector machines

Radar emitter signal recognition plays an important role in electronic intelligence systems and electronic support measure systems. To heighten accurate recognition rate of radar emitter signals, this paper proposes a hierarchical classifier structure to recognize radar emitter signals. The proposed structure combines resemblance coefficient classifier, support vector machines with binary tree architecture and linear classifier based on Mahalanobis distance. Experimental results of recognizing multiple radar emitter signals show that the introduced classifier is simpler, consumes smaller training time and achieves higher accurate recognition rate and greater efficiency, in comparison with one-versus-rest support vector machines, one-versus-one support vector machines and binary-tree support vector machines.

A novel feature selection approach and its application

Feature selection is a satisfactory optimization problem. Most feature selection methods did not consider the cost of feature extraction and the automatic decision of feature subset dimension. So a novel approach called satisfactory feature selection method (SFSM) was proposed. SFSM integrated feature extraction with feature selection and considered classification performance of feature samples, the dimension of feature subset and the complexity of feature extraction simultaneously. Experimental results show that SFSM selects more satisfying feature subset than sequential forward selection using distance criterion (SFSDC) and the method presented by Tiejun Lu (GADC). Also, SFSM achieves higher accurate recognition rate than original feature set, SFSDC and GADC, which verifies the validity of the proposed method.