A Study on Radar Target Detection using Space Time Adaptive Processing Algorithm and LCMV Algorithm

In this paper, we study the directionof arrival estimation of the desired target in adaptive array MV algorithm to update the weight, and the optimized weight removes the interference signal. The target signal is estimated using the optimized weight vector and the high resolution the direction of arrival estimation MUSIC algorithm. We calculate the inverse of the correlation matrix using the QR method to reduce the processing power consumption of the optimized weight. The optimal weight vector is applied to the proposed algorithm to estimate the desired target direction from the output power spectrum. The performance of the proposed method is compared with the existing method by simulation. The experimental method estimates three targets from the antenna received signal. The existing method did not estimate the three desired targets at [-30o,-20, -10o]. The proposed method accurately estimates the desired three targets at [-30o,-20, -10o]. In the [-10o, 0, 10o] target estimation, the existing method reduces the estimated resolution of the target, but the proposed method accurately estimates the target. We proved that the proposed method in the simulation was superior to the existing method.

A complementary webometric ranking system based on the website quality and traffic measures: A study focusing on top-ranked world universities

Ranking of universities regarding their web-based activities plays a pivotal role in promoting scientific advancement since it motivates the open access accessibility to scientific results. In this study, a new ranking system based on the website quality factors and traffic evaluation was proposed. Since top-ranked universities are usually considered as the standard models for lower ranked ones, the focus of this study was on top-ranked universities. The proposed ranking was compared with well-known Webometrics ranking system. The website traffic and quality assessment were acquired for websites of top-ranked world universities and the correlation between these indices and the Webometrics ranking was evaluated. The summation of the weighted value of obtained measures according to an optimal weight vector obtained by a genetic algorithm framework was used for ranking purposes. The results showed that the website total traffic size was correlated with Webometrics rank (R≈-0.6, p< 0.01). Also, using the weighted value of website quality and traffic measures, the proposed ranking system could predict Webometrics ranking by the accuracy of up to 69%. Even though the method was proposed for universities, it could be applied for ranking other types of centers or companies, provided that the suitable cost function for the genetics algorithm framework was defined.

Mitigation of narrowband stationary interference in the telemetry complex based on an autocompensator with remote channels.

The problem of combating stationary interference in the areas of operation of ground-based telemetry systems is considered. One of the most annoying types of interference is high-power narrowband TV interference. They are emitted from television towers and their position is known in advance. A solution to this problem is proposed by using a multichannel auto-compensator with a non-standard arrangement of compensation channels. An analytical solution is obtained for the optimal weight vector of the auto-compensator of interference, based on the method of power vectors. This method does not require direct inversion of the interference correlation matrix. The computational complexity of the proposed method is estimated and it is shown that it has a much lower computational complexity compared to the method of direct inversion of the interference correlation matrix. The results of numerical simulation of the interference suppression coefficient are presented. Its effectiveness has been shown.

IMAGE RESTORATION METHOD, INVARIANT TO SIGNAL CORRELATION IN THE INFORMATION SYSTEM WITH ADAPTIVE ANTENNA ARRAY

The problem of image signal processing in the information system with adaptive antenna array based on the inversion of sample estimates of correlation matrix of observations is considered. The example of the maximum signal-to-noise ratio criterion shows the problem, inherent in classical methods of finding the optimal weight vector under a priori uncertainty conditions when detecting correlated image signals. It has been concluded that the dependence of these methods on the inverse of estimation of the correlation matrix of observations leads to the impossibility of separating correlated image signals. As a consequence, the use of classical methods of finding the optimal weight vector in the information system with adaptive antenna array is effective only in the case of image restoration from a single signal source, with the signal received on the set of independent jamming background. A novel method, invariant to the correlation of image signals, has been developed for finding the optimal weight vector without the usage of correlation matrix of observations. An image restoration algorithm invariant to correlation of image signals in the information system with adaptive antenna array is proposed. Statistical models have been constructed for the classical method based on the criterion of maximum signal-to-noise ratio and invariant to correlation method of image restoration in following cases: a single source against the jamming background of two independent sources; two independent sources against the jamming background. Simulation results in the information system with adaptive antenna array are presented, showing to visually assess efficiency of proposed methods of image signal restoration using optimal weight vector. Detailed analysis of the results obtained is carried out.

A New Method for Active Cancellation of Engine Order Noise in a Passenger Car

This paper presents a novel active noise cancellation (ANC) method to reduce the engine noise inside the cabin of a car. During the last three decades, many methods have been developed for the active control of a quasi-stationary narrowband sinusoidal signal. However, since the interior noise signal is non-stationary with a fast frequency variation when the car accelerates rapidly, these methods cannot stably reduce the interior noise. The proposed method can reduce the interior noise stably even if the speed of the car is changed quickly. The method uses an adaptive filter with an optimal weight vector for the active control of such an engine noise. The method of determining the optimal weight vector of an adaptive filter is demonstrated. In order to validate the advantages of the proposed method, a conventional method and the proposed method are simulated with three synthesized signals. Finally, the proposed method is applied to the cancellation of booming noise in a sport utility vehicle. We demonstrate that the performance of the ANC system with the proposed algorithm is excellent for the attenuation of engine noise inside the cabin of a car.

Research on Combined Weight of Target Threat Index of Information Fusion

To the characteristics of index weight, using minimal deviation method as the theoretical tools to create index portfolio weight optimization model. Using genetic algorithms to solve the optimal weight vector model which has the capability of global searching . Through example demonstrate, the right combination to get the weight vector of the model is more reasonable and scientific, and genetic algorithm process a simpler and easier way to understand than traditional methods.

Combination Weighting Method Based on Generalized Mahalanobis Distance and Weighting Relative Entropy

Aimed at combination weighting in multiple attribute decision making, a new approach for combining different weighting vectors is proposed. The proposed approach considers the randomicity of weights themselves and the consistency among weighting vectors, constructs a constrained weighted relative entropy model. Aimed at the disadvantage in the TOPSIS based on Euclidean distance, the TOPSIS based on Mahalanobis distance is adopted to solve the coefficients of optimal weight vector. Finally, an example is conducted and the results show the proposed approach is effective and is more reasonable than three other combination approaches.

A Method of Multi-Classifier Combination Based on Dempster-Shafer Evidence Theory and the Application in the Fault Diagnosis

A problem is aroused in multi-classifier system that normally each of the classifiers is considered equally important in evidences’ combination, which gone against with the knowledge that different classifier has various performance due to diversity of classifiers. Therefore, how to determine the weights of individual classifier in order to get more accurate results becomes a question need to be solved. An optimal weight learning method is presented in this paper. First, the training samples are respectively input into the multi-classifier system based on Dempster-Shafer theory in order to obtain the output vector. Then the error is calculated by means of figuring up the distance between the output vector and class vector of corresponding training sample, and the objective function is defined as mean-square error of all the training samples. The optimal weight vector is obtained by means of minimizing the objective function. Finally, new samples are classified according to the optimal weight vector. The effectiveness of this method is illustrated by the UCI standard data set and electric actuator fault diagnostic experiment.