scholarly journals CC-MUSIC: An Optimization Estimator for Mutual Coupling Correction of L-Shaped Nonuniform Array with Single Snapshot

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yuguan Hou ◽  
Tongyu Zhang ◽  
Shaochuan Wu
Keyword(s):  
Global Optimization ◽  
Covariance Matrix ◽  
Mutual Coupling ◽  
Optimization Problem ◽  
Spatial Spectrum ◽  
Estimation Accuracy ◽  
Spectrum Estimation ◽  
Music Algorithm ◽  
Calibration Source ◽  
Single Snapshot

For the case of the single snapshot, the integrated SNR gain could not be obtained without the multiple snapshots, which degrades the mutual coupling correction performance under the lower SNR case. In this paper, a Convex Chain MUSIC (CC-MUSIC) algorithm is proposed for the mutual coupling correction of the L-shaped nonuniform array with single snapshot. It is an online self-calibration algorithm and does not require the prior knowledge of the correction matrix initialization and the calibration source with the known position. An optimization for the approximation between the no mutual coupling covariance matrix without the interpolated transformation and the covariance matrix with the mutual coupling and the interpolated transformation is derived. A global optimization problem is formed for the mutual coupling correction and the spatial spectrum estimation. Furthermore, the nonconvex optimization problem of this global optimization is transformed as a chain of the convex optimization, which is basically an alternating optimization routine. The simulation results demonstrate the effectiveness of the proposed method, which improve the resolution ability and the estimation accuracy of the multisources with the single snapshot.

scholarly journals An OPMA for Robust Mutual Coupling Coefficients Estimation of URA with Single Snapshot in MIMO HF Sky-Wave Radar

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Yuguan Hou ◽  
Qingguo Jin ◽  
Shaochuan Wu ◽  
Zhuoming Li
Keyword(s):  
Mutual Coupling ◽  
Signal To Noise Ratio ◽  
Mean Shift ◽  
Success Probability ◽  
Doa Estimation ◽  
Coupling Coefficients ◽  
Music Algorithm ◽  
Low Snr ◽  
Wave Radar ◽  
Single Snapshot

Due to the fluctuation of the signal-to-noise ratio (SNR) and the single snapshot case in the MIMO HF sky-wave radar system, the accuracy of the online estimation of the mutual coupling coefficients matrix of the uniform rectangle array (URA) might be degraded by the classical approach, especially in the case of low SNR. In this paper, an Online Particle Mean-Shift Approach (OPMA) is proposed, which is to get a relatively more effective estimation of the mutual coupling coefficients matrix with the low SNR. Firstly, the spatial smoothing technique combined with the MUSIC algorithm of URA is introduced for the DOA estimation of the multiple targets in the case of single snapshot which are taken as coherent sources. Then, based on the idea of the particle filter, the online particles with a moderate computational complexity are used to generate some different estimation results. Finally, the mean-shift algorithm is applied to get a more robust estimate of the equivalent mutual coupling coefficients matrix. The simulation results demonstrate the validity of the proposed approach in terms of the success probability, the statistics of bias, and the variance. The proposed approach is more robust and more accurate than the other two approaches.

scholarly journals Finding Global Minima with a Filled Function Approach for Non-Smooth Global Optimization

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Weixiang Wang ◽  
Youlin Shang ◽  
Ying Zhang
Keyword(s):  
Global Optimization ◽  
Optimization Problem ◽  
Numerical Test ◽  
Function Approach ◽  
Global Optimization Problem ◽  
Computational Results ◽  
Filled Function ◽  
Global Minima ◽  
Smooth Case ◽  
Definition Of

A filled function approach is proposed for solving a non-smooth unconstrained global optimization problem. First, the definition of filled function in Zhang (2009) for smooth global optimization is extended to non-smooth case and a new one is put forwarded. Then, a novel filled function is proposed for non-smooth the global optimization and a corresponding non-smooth algorithm based on the filled function is designed. At last, a numerical test is made. The computational results demonstrate that the proposed approach is effcient and reliable.

Study on the Improvement of MUSIC Algorithm Fire Wireless Communication

2014 ◽  
Vol 1049-1050 ◽  
pp. 1880-1884
Author(s):  
Bin Ni
Keyword(s):  
Covariance Matrix ◽  
Time Domain ◽  
Analytical Signal ◽  
Time Domain Analysis ◽  
Observation Time ◽  
Eigenvalue Decomposition ◽  
Target Range ◽  
Radio Communication ◽  
Music Algorithm ◽  
Good Spatial Resolution

Music algorithm has good spatial resolution, provides the possibility to further improve the performance of fire radio communication system, but the algorithm in the target range rapidly changing circumstances poor stability. Aiming at this problem, this paper proposes a MUSIC algorithm based on time domain analytical signals (TAMUSIC, Time-domain Analysis MUSIC). The TAMUISC algorithm first constructs analytical time-domain signal; then the time domain analytical signal covariance matrix; finally the covariance matrix eigenvalue decomposition, the noise subspace estimation results of spatial spectrum. The simulation results show that, TAMUSIC algorithm in target azimuth change quickly, compared with the conventional MUSIC algorithm, need a short observation time, observation has smaller variance.

scholarly journals Using a vision cognitive algorithm to schedule virtual machines

2014 ◽  
Vol 24 (3) ◽  
pp. 535-550 ◽  
Author(s):  
Jiaqi Zhao ◽  
Yousri Mhedheb ◽  
Jie Tao ◽  
Foued Jrad ◽  
Qinghuai Liu ◽  
...  
Keyword(s):  
Global Optimization ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Optimization Problem ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Primary Concern ◽  
Global Optimization Problem ◽  
Vm Scheduling ◽  
Operation Cost

Abstract Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption

scholarly journals The Square Root UKF-SLAM Algorithm Based on the Smallest Proportion of Skewness in Single Line Sampling

2018 ◽  
Vol 160 ◽  
pp. 06005
Author(s):  
MengYuan Chen ◽  
GuoWei Qin ◽  
Tong Xu
Keyword(s):  
Computational Complexity ◽  
Covariance Matrix ◽  
Sampling Strategy ◽  
Single Line ◽  
Estimation Accuracy ◽  
Square Root ◽  
Line Sampling ◽  
Algorithm Stability ◽  
Slam Algorithm ◽  
Symmetric Sampling

In view of the distortion in the filter gain matrix calculation as well as the high computational complexity and the nonlocal effect of symmetric sampling that exists in the UKF-SLAM algorithm, the square root UKF-SLAM algorithm based on the smallest proportion of skewness in single line sampling was proposed. According to the mended algorithm, the square root of covariance matrix is brought into iteration operation instead of covariance matrix, moreover, the smallest proportion of skewness in single line sampling is utilized for the optimization of sampling strategy. The results of simulation show that the algorithm can effectively improve the position accuracy in robot as well as the estimation accuracy of feature map. Furthermore, the computational complexity is reduced and the algorithm stability is improved.

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