Analysis of Multipath Component Parameter Estimation Accuracy in Directional Scanning Measurement

2018 ◽  
Vol 17 (1) ◽  
pp. 12-16 ◽  
Author(s):  
Minseok Kim
Keyword(s):  
Parameter Estimation ◽  
Estimation Accuracy ◽  
Component Parameter

scholarly journals JOINT DELAY AND AZIMUTH ESTIMATION OF COHERENT WAVES FOR MILLIMETER-WAVE BAND CHANNEL SOUNDING

2020 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Kentaro Saito ◽  
Ahmad Salaam Mirfananda ◽  
Jun-ichi Takada ◽  
Mitsuki Nakamura ◽  
Wataru Yamada ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Millimeter Wave ◽  
Mobile Communication ◽  
Channel Modeling ◽  
Cloud Services ◽  
Mimo Channel ◽  
Estimation Accuracy ◽  
Wave Band ◽  
Channel Sounding ◽  
Millimeter Wave Band

The user traffic in the mobile communication area has rapidly increased owing to the widespread of smartphones and various cloud services. To handle the increasing traffic, in the fifth-generation mobile communication system (5G), the millimeter-wave multiple-input and multiple-output (MIMO) communication technology is under development. Because the MIMO transmission performance heavily depends on the radio propagation characteristics, various MIMO channel measurements are needed for the performance evaluation and system design. The accurate and efficient parameter estimation algorithm which estimates the propagation delays and angle of arrivals (AoA) of radio waves is also indispensable for the purpose. In this paper, we extended the joint delay and azimuth estimation (JADE) method based on multiple signal classification (MUSIC) algorithm. In our proposal, the drawback of the MUSIC that the performance degrades for the estimation of coherent waves was solved by applying the smoothing technique in the frequency domain. It also makes the antenna calibration simpler. We implemented the proposed algorithm for the channel sounding system in the 66 GHz band, which is one of the candidate frequency bands for the International Mobile Telecommunications (IMT) system and evaluated the effectiveness through the experiment in an anechoic chamber. The result showed that our proposed method can de-correlate the signal components of coherent waves, and improved the parameter estimation accuracy significantly. The root means square error (RMSE) of the propagation delay estimation was improved from 2.7 ns to 0.9 ns, and the RMSE of the AoA estimation was improved from 20.3 deg. to 7.2 deg. The results are expected to be utilized for the millimeter wave band MIMO channel modeling.

scholarly journals A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Shaolong Chen ◽  
Renyu Yang ◽  
Renhuan Yang ◽  
Liu Yang ◽  
Xiuzeng Yang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Fitness Function ◽  
System Modeling ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Function Parameter ◽  
Swarm Optimization ◽  
Modeling And Control ◽  
Chicken Swarm Optimization

Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO) has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO) is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.

scholarly journals A Frequency-Time Algorithm of Parameter Estimation for Sinusoidal Signal in Noise

2021 ◽  
Vol 21 (1) ◽  
pp. 33-38
Author(s):  
Peng Chen ◽  
Qin Chen ◽  
Zhijun Xie ◽  
Xiaohui Chen ◽  
Shaomei Zhao
Keyword(s):  
Parameter Estimation ◽  
Time Domain ◽  
Initial Phase ◽  
Linear Prediction ◽  
Estimation Algorithm ◽  
Time Algorithm ◽  
Sinusoidal Signal ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Parameter Estimation Algorithm

Abstract In this paper, a computationally efficient and high precision parameter estimation algorithm with frequency-time combination is proposed to improve the estimation performance for sinusoidal signal in noise, which takes the advantages of frequency- and time-domain algorithms. The noise influence is suppressed through spectrum analysis to get coarse frequency, and the fine frequency is obtained by denoising filtering and using linear prediction property. Then, estimation values of the amplitude and initial phase are obtained. The numerical results indicate that the proposed algorithm makes up for the shortcomings of frequency- and time-domain algorithms and improves the anti-interference performance and parameter estimation accuracy for sinusoidal signal.

scholarly journals Chirp Signal Denoising Based on Convolution Neural Network

2020 ◽  
Author(s):  
Ben Guangli ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Xin Zhang ◽  
Ning Zhang
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Estimation Algorithm ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Signal Processing Algorithm ◽  
Low Snr ◽  
Result Show ◽  
Parameter Estimation Algorithm ◽  
Two Parameter

Abstract Many classical chirp signal processing algorithm may experience distinct performance decrease in noise circumstance. To address the problem, this paper proposes a deep learning based approach to filter noises in time domain. The proposed denoising convolutional neural network (DCNN) is trained to recover the original clean chirps from observation signals with noises. Following denosing, we employ two parameter estimation algorithm to DCNN output. Simulation result show that the proposed DCNN method improves the signal noise ratio (SNR) and parameter estimation accuracy to a great extent compared to the signals without denoising. And DCNN have a strong adaptability of low SNR input scenarios that never trained.

scholarly journals Parameter estimation and signal detection algorithm based on adaptive capture in non-cooperative communication

2021 ◽  
Vol 336 ◽  
pp. 04002
Author(s):  
Zilong He ◽  
Peng Sun ◽  
Kexian Gong ◽  
Hua Jiang
Keyword(s):  
Parameter Estimation ◽  
Signal Detection ◽  
Cooperative Communication ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Frequency Offset ◽  
Detection Algorithm ◽  
Estimation Accuracy ◽  
Signal Frequency ◽  
Signal Spectrum

Aiming at the problem that the frequency offset in the non-cooperative communication system causes the received signal spectrum to shift, which exceeds the passband of the matched filter and affects the subsequent demodulation, a parameter estimation and signal detection algorithm based on adaptive capture is proposed by this paper, which is more convenient for hardware implementation and consumes less resources. The algorithm is divided into three parts. Firstly, use the correlation value between the signal and the preamble sequence as the basis for frequency capture. Secondly, the frequency is accurately estimated based on the interpolation algorithm. Finally, the phase-locked loop structure is used to track the frequency according to the characteristics of the frequency gradually changing and the signal frequency offset is eliminated in the Digital Down Converter stage. It provides necessary conditions for accurate signal detection and phase estimation. The simulation results show that the algorithm has high estimation accuracy, wide esti-mation range and low complexity. It can also achieve better estimation accuracy and detection performance under low signal-to-noise ratio.

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