scholarly journals Non-Cooperative Passive Direct Localization Based on Waveform Estimation

2021 ◽  
Vol 13 (2) ◽  
pp. 264
Author(s):  
Tao Zhou ◽  
Wei Yi ◽  
Lingjiang Kong
Keyword(s):  
Objective Function ◽  
Signal To Noise Ratio ◽  
Algorithm Design ◽  
Rayleigh Quotient ◽  
Least Square ◽  
Pulse Modulation ◽  
Theoretical Derivation ◽  
Direct Localization ◽  
Localization Performance ◽  
Passive Localization

This paper considers a non-cooperative passive localization system wherein widely distributed receivers are used to localize a transmitter radiating a periodical pulse pair signal. Two possible pulse modulation models, noncoherent and coherent pulses, are fully considered for practical application, and are effectively unified as a general model for the algorithm design. To achieve highly accurate and robust localization performance, an enhanced direct position determination (DPD) algorithm based on waveform estimation (WE) is devised to jointly estimate the transmitter position and the waveform profile. The optimal objective function based on a least square (LS) principle is first derived to directly determine the position of the transmitter. Due to the complete lack of knowledge on the transmitted signal, the processing center calculates the objective function at each searched grid of interest by using estimated pulses instead of the real ones, while extraction of pulse samples and estimation of waveform are executed. Theoretical derivation gives the solution to estimate the non-parameterized waveform with a structure of maximum Rayleigh quotient. Additionally, simulation results verify the effectiveness of the proposed algorithm for many common waveform types in the cases of transmitting noncoherent and coherent pulses, and also show the excellent advantage over the classical DPD algorithm at low signal-to-noise ratio (SNR).

scholarly journals Study on Time Reversal Maximum Ratio Combining in Underwater Acoustic Communications

2021 ◽  
Vol 11 (4) ◽  
pp. 1509
Author(s):  
Anbang Zhao ◽  
Caigao Zeng ◽  
Juan Hui ◽  
Keren Wang ◽  
Kaiyu Tang
Keyword(s):  
Time Reversal ◽  
Signal To Noise Ratio ◽  
Spatial Diversity ◽  
Single Element ◽  
Noise Power ◽  
Theoretical Derivation ◽  
Underwater Acoustic ◽  
Maximum Ratio Combining ◽  
Weight Coefficients ◽  
Output Snr

Time reversal (TR) can achieve temporal and spatial focusing by exploiting spatial diversity in complex underwater environments with significant multipath. This property makes TR useful for underwater acoustic (UWA) communications. Conventional TR is realized by performing equal gain combining (EGC) on the single element TR output signals of each element of the vertical receive array (VRA). However, in the actual environment, the signal-to-noise ratio (SNR) and the received noise power of each element are different, which leads to the reduction of the focusing gain. This paper proposes a time reversal maximum ratio combining (TR-MRC) method to process the received signals of the VRA, so that a higher output SNR can be obtained. The theoretical derivation of the TR-MRC weight coefficients indicates that the weight coefficients are only related to the input noise power of each element, and are not affected by the multipath structure. The correctness of the derivation is demonstrated with the experimental data of the long-range UWA communications conducted in the South China Sea. In addition, the experimental results illustrate that compared to the conventional TR, TR-MRC can provide better performance in terms of output SNR and bit error rate (BER) in UWA communications.

Model Optimization and Stability of Hemoglobin Analysis in Human Soluble Blood Samples by FTIR/ATR Spectroscopy

2011 ◽  
Vol 55-57 ◽  
pp. 1168-1171
Author(s):  
Tao Pan ◽  
Ai Hong Peng ◽  
Wen Jie Huang
Keyword(s):  
Signal To Noise Ratio ◽  
Attenuated Total Reflection ◽  
Partial Least Square ◽  
Least Square ◽  
Model Optimization ◽  
Optimal Model ◽  
Blood Samples ◽  
Quantification Method ◽  
Atr Spectroscopy ◽  
Pls Method

Using Fourier transform infrared spectroscopy (FTIR), attenuated total reflection (ATR) technology and partial least square (PLS) method, the rapid quantification method of hemoglobin (HGB) in human soluble blood samples was established. Based on the distribution of samples’ HGB chemical value and absorbance on 1543 cm-1 which had the highest signal to noise ratio for HGB, all samples were divided into calibration set and prediction set for 50 times. PLS models were established for all divisions, based on the average data RMSEPAve, the stable optimal model was selected, the corresponding PLS factor, RMSEPAve and RP,Ave were 2, 6.81 g/L and 0.943 respectively.

Missile Guidance Algorithm Design Using Particle Swarm Optimization

2013 ◽  
Vol 284-287 ◽  
pp. 2411-2415
Author(s):  
Chien Chun Kung ◽  
Kuei Yi Chen
Keyword(s):  
Objective Function ◽  
Fitness Function ◽  
Equations Of Motion ◽  
Algorithm Design ◽  
Solution Space ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Missile Guidance ◽  
Pursuit Evasion ◽  
Guidance Algorithm

This paper presents a technique to design a PSO guidance algorithm for the nonlinear and dynamic pursuit-evasion optimization problem. In the PSO guidance algorithm, the particle positions of the swarm are initialized randomly within the guidance command solution space. With the particle positions to be guidance commands, we predict and record missiles’ behavior by solving point-mass equations of motion during a defined short-range period. Taking relative distance to be the objective function, the fitness function is then evaluated according to the objective function. As the PSO algorithm proceeds, these guidance commands will migrate to a local optimum until the global optimum is reached. This paper implements the PSO guidance algorithm in two pursuit-evasion scenarios and the simulation results show that the proposed design technique is able to generate a missile guidance law which has satisfied performance in execution time, terminal miss distance, time of interception and robust pursuit capability.

Performance Quality, Sensitivity, and Tolerance Specification of Mechanisms

1994 ◽  
Author(s):  
Kwun-Lon Ting ◽  
Yufeng Long
Keyword(s):  
Signal To Noise Ratio ◽  
Rayleigh Quotient ◽  
Sensitivity Index ◽  
Signal To Noise ◽  
Mechanism Synthesis ◽  
Performance Quality ◽  
Mechanical Assemblies ◽  
Tolerance Specification ◽  
Position Synthesis ◽  
The Ideal

Abstract By employing Taguchi’s concept to mechanism synthesis, this paper presents the theory and technique to identify a robust design, which is the least sensitive to the tolerances, for mechanisms and to determine the tolerance specification for the best performance and manufacturability. The method is demonstrated in finite and infinitesimal position synthesis. The sensitivity Jacobian is first introduced to relate the performance tolerances and the dimensional tolerances. The Rayleigh quotient of the sensitivity Jacobian, which is equivalent to Taguchi’s signal to noise ratio, is then used to define the performance quality and a sensitivity index is introduced to measure the sensitivity of the performance quality to the dimensional tolerances for the whole system. The ideal tolerance specification is obtained in closed form. It shows how the tolerance specification affects the performance quality and that the performance quality can be significantly improved by tightening a key tolerance while loosening the others. The theory is general and the technique is readily adaptable to almost any form and type of mechanical system, including multiple-loop linkages and mechanical assemblies or even structures.

scholarly journals Smooth Signal Activity Detection in White Gaussian Noise: Application to P300 Detection

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Reza Boostani ◽  
Negar Kheirandish
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Hypothesis Test ◽  
White Gaussian Noise ◽  
Least Square ◽  
Unknown Parameters ◽  
Detection Scheme ◽  
Ml Estimation ◽  
Unknown Variance ◽  
Synthetic Datasets

<div>Abstract—In this research, we have proposed a new scheme to detect and extract the activity of an unknown smooth template in presence of white Gaussian noise with unknown variance. In this regard, the problem is considered a binary hypothesis test, and it is solved employing the generalized likelihood ratio (GLR) method. GLR test uses the maximum likelihood (ML) estimation of unknown parameters under each hypothesis. The ML estimation of the desired signal yields an optimization problem with smoothness constraint which is in the form of a conventional least square error estimation problem and can be solved optimally. The proposed detection scheme is studied for P300 elicitation from the background electroencephalography signal. In addition, to assume the P300 smoothness, two prior knowledge are considered in terms of positivity and approximate occurrence time of P300. The performance of the method is assessed on both real and synthetic datasets in different noise levels and compared to a conventional signal detection scheme without considering smoothness priors, as well as state-of-theart linear and quadratic discriminant analysis. The results are illustrated in terms of detection probability, false alarm rate, and accuracy. The proposed method outperforms the counterparts in low signal-to-noise ratio situations.</div>

scholarly journals Blind Source Separation Based on Quantum Slime Mould Algorithm in Impulse Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zhiwei Zhang ◽  
Hongyuan Gao ◽  
Jingya Ma ◽  
Shihao Wang ◽  
Helin Sun
Keyword(s):  
Objective Function ◽  
Blind Source Separation ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Moving Average ◽  
Source Separation ◽  
Hybrid Optimization ◽  
Slime Mould ◽  
Stable Performance

In order to resolve engineering problems that the performance of the traditional blind source separation (BSS) methods deteriorates or even becomes invalid when the unknown source signals are interfered by impulse noise with a low signal-to-noise ratio (SNR), a more effective and robust BSS method is proposed. Based on dual-parameter variable tailing (DPVT) transformation function, moving average filtering (MAF), and median filtering (MF), a filtering system that can achieve noise suppression in an impulse noise environment is proposed, noted as MAF-DPVT-MF. A hybrid optimization objective function is designed based on the two independence criteria to achieve more effective and robust BSS. Meanwhile, combining quantum computation theory with slime mould algorithm (SMA), quantum slime mould algorithm (QSMA) is proposed and QSMA is used to solve the hybrid optimization objective function. The proposed method is called BSS based on QSMA (QSMA-BSS). The simulation results show that QSMA-BSS is superior to the traditional methods. Compared with previous BSS methods, QSMA-BSS has a wider applications range, more stable performance, and higher precision.

scholarly journals Performance enhancement of MPM using SNR boosting techniques

2016 ◽  
Vol 5 (4) ◽  
pp. 115
Author(s):  
Shimaa Mamdouh ◽  
Amr Hussein ◽  
Hamdy Elmekaty
Keyword(s):  
Signal Processing ◽  
Performance Enhancement ◽  
Mimo Systems ◽  
Signal To Noise Ratio ◽  
Matrix Pencil ◽  
Doa Estimation ◽  
Least Square ◽  
Test Case ◽  
Recursive Least Square ◽  
Low Snr

Signal to noise ratio (SNR) boosting is one of the most important research areas in signal processing. The effectiveness of SNR boosting is not limited to a specific application rather, it is widely used in image processing, signal processing, cognitive radio, MIMO systems, digital beam forming, and direction of arrival (DOA) estimation …etc. In this paper, the recursive least square (RLS) and wavelet based de-noising filters are exploited for SNR boosting in DOA estimation techniques for further performance enhancement. The matrix pencil method (MPM) as an effortlessness and high resolution DOA estimation technique is taken as a test case. That is because it suffers from performance deterioration under low SNR regimes. The simulation results reveal that the MPM based RLS de-noising filter outperforms the MPM based wavelet de-noising filter and the traditional MPM in terms of mean square error (MSE) especially at low SNR regimes.

Kinetic Study on a Commercial Amorphous Hydrocracking Catalyst by Weighted Lumping Strategy

2010 ◽  
Vol 8 (1) ◽  
Author(s):  
Sepehr Sadighi ◽  
Arshad Ahmad ◽  
Akbar Irandoukht
Keyword(s):  
Objective Function ◽  
Space Velocity ◽  
Reaction Rates ◽  
Pilot Scale ◽  
Least Square ◽  
Average Absolute Deviation ◽  
Absolute Deviation ◽  
Dual Functional ◽  
Hydrocracking Catalyst ◽  
Middle Distillates

Hydrocracking is an important upgrading process in the petroleum refinery, and it is generally used to process feedstocks ranging from vacuum gas oil (VGO) to vacuum residue. In this work, hydrocracking of VGO using a dual functional amorphous catalyst was carried out at a pilot scale unit under the following reaction conditions: liquid hourly space velocity (LHSV) from 1 to 1.5 hr-1 and reaction temperatures of 360-440°C at the constant pressure and hydrogen to oil, 156 bar and 1780 Nm3/m3, respectively. The effluent of the reactor was characterized to dry gas, naphtha, kerosene, diesel and unconverted VGO or residue. The pilot tests demonstrated that performing experiments beyond the temperature, recommended by catalyst vendor, lead the process to unstable hydrocracking. To describe the yield of hydrocracking products a five-lump discrete lumping approach with ten reactions was proposed. At first, the kinetic model contained twenty kinetic constants which were estimated by using the conventional objective function. The estimated parameters showed that the tendency of the catalyst to convert VGO to gas and naphtha was negligible whilst rate constants for hydrocracking of VGO to middle distillates were considerably high which was compatible with the nature of amorphous hydrocracking catalysts. After evaluating the magnitude of reaction rates and eliminating the ignorable constants, the network was reduced to six reactions in which only nine parameters were needed. The predictions indicated that the latter network could fit the yield of products more acceptable as if the average absolute deviation between experimental and calculated yields was descended from 16.25% to 12.6%. Then, to have a better prediction, a weighted objective function was used in which weight factors were calculated by a proposed weighted least square expression. The results confirmed that this approach could reduce average absolute deviation of model to 10.75%, and it created a fairly even distribution of deviation between hydrocracking products.

An Improved Cuckoo Search based Optimal Ranged Brightness Preserved Histogram Equalization and Contrast Stretching Method

2017 ◽  
Vol 8 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Md. Iqbal Quraishi ◽  
Sanjoy Das
Keyword(s):  
Objective Function ◽  
Optimization Problems ◽  
Signal To Noise Ratio ◽  
Cuckoo Search ◽  
Histogram Equalization ◽  
Chaotic Sequence ◽  
Population Diversity ◽  
Optimal Range ◽  
Novel Variant ◽  
Contrast Stretching

This paper is organized into two main parts. In the first part, two methods have been discussed to preserve the original brightness of the image which are Parameterized transformation function and a novel variant of modified Histogram Equalization (HE) method. In this study both methods have been formulated as optimization problems to increase the efficiency of the corresponding methods within reasonable time. In the second part, a novel modified version of Cuckoo Search (CS) algorithm has been devised by using chaotic sequence, population diversity information etc to solve those formulated optimization problems. A new Co-occurrence matrix's features based objective function is also devised to preserve the original brightness. Peak-signal to noise ratio (PSNR) acts as objective function to find optimal range of enhanced images. Experimental results prove the supremacy of the proposed CS over traditional CS algorithm.

scholarly journals A Soft-Threshold Filtering Approach for Tomography Reconstruction from a Limited Number of Projections with Bilateral Edge Preservation

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2346
Author(s):  
Tiago Wirtti ◽  
Evandro Salles
Keyword(s):  
Objective Function ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Edge Preservation ◽  
Edge Preserving ◽  
Reconstruction Process ◽  
Regularization Term ◽  
Computational Performance

In X-ray tomography image reconstruction, one of the most successful approaches involves a statistical approach with l 2 norm for fidelity function and some regularization function with l p norm, 1 < p < 2 . Among them stands out, both for its results and the computational performance, a technique that involves the alternating minimization of an objective function with l 2 norm for fidelity and a regularization term that uses discrete gradient transform (DGT) sparse transformation minimized by total variation (TV). This work proposes an improvement to the reconstruction process by adding a bilateral edge-preserving (BEP) regularization term to the objective function. BEP is a noise reduction method and has the purpose of adaptively eliminating noise in the initial phase of reconstruction. The addition of BEP improves optimization of the fidelity term and, as a consequence, improves the result of DGT minimization by total variation. For reconstructions with a limited number of projections (low-dose reconstruction), the proposed method can achieve higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) results because it can better control the noise in the initial processing phase.

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