This paper considers target localization using time delay (TD) and angle of arrival (AOA) measurements in distributed multiple-input multiple-output (MIMO) radar. Aiming at the problem that the localization performance of existing algorithms degrades sharply in the presence of impulsive noise, we propose a novel localization algorithm based on ℓ p -norm minimization and iteratively reweighted least squares (IRLS). Firstly, the TD and AOA measurement equations are established in the presence of zero-mean symmetric α-stable noise; then, the localization problem is transformed to a ℓ p -norm minimization problem by linearizing the measurement equations; and finally, the ℓ p -norm minimization problem is solved using IRLS by which the target position estimate is obtained, and the optimal choice of norm order p is deduced. Moreover, the Cramér–Rao bound (CRB) for target position estimation in impulsive noise is also derived, generalizing the Gaussian CRB. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in terms of localization accuracy and robustness in impulsive noise.
The conventional methods for vibration fault detection and diagnosis relies on feature extraction from the waveforms of the vibration signals. This article exploits the scope of image recognition application for the detection and diagnosis of fan vibration faults. In this paper, a novel image recognition technique is proposed for vibration-based fault diagnosis using the spectrum images of the vibration signals. 1D vibration signal spectrum is initially computed using Fast Fourier Transform (FFT) and the FFT frequencies are adjusted such that it captures a vibration spectrum diagram as 2D image representation. FFT based vibration analysis is done and the image recognition concept is utilized for feature extraction and a machine learning classification module is used for fault analysis and diagnosis. Effective feature generation is done using Principal Component Analysis (PCA) by removing the redundancy from the feature vectors and machine learning classifiers are used to obtain improved image recognition and classification performance. Artificial Neural Network (ANN) classifier yields better performance in terms of various performance parameters and percentage improvement in terms of accuracy for ANN classification methods over Support Vector Machine (SVM), k-Nearest Neighbours (kNN) and Random Forest Ensemble (RFE) methods are 10.01 %, 4.51 % and 2.01 % respectively. Comparative scenarios are considered in this work for fan vibration fault detection as well as diagnosis based on the image features for various realistic vibration fault conditions. Effectiveness of the proposed image recognition-based technique is compared with the state-of-the-art methods, justifying its outperformance for fan fault detection and diagnosis using the combination of spectrum adjustment, PCA and ANN classification method.
Collective behaviors such as synchronization, consensus, and flocking have been extensively investigated over the past decades. Many important results have been disseminated concerning the properties of complex networks. Recent technological development requires performance distribution, and this motivates the resolution to the issue of performance distributability. Albeit in a simple setup, this paper presents an attempt to attacking this problem. Important results are obtained for performance redistribution under both unitary and specified specifications. Constraints are also considered revealing the tight bounds on both nodes dynamics and graph elements for fulfilling the performance distribution and redistribution requirements. Examples are presented for verification of the claims.
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