scholarly journals Velocity Measurement of Coherent Doppler Sonar Assisted by Frequency Shift, Kalman Filter and Linear Prediction

2021 ◽  
Vol 9 (2) ◽  
pp. 109
Author(s):  
Peng Liu ◽  
Nobuyoshi Kouguchi ◽  
Ying Li
Keyword(s):  
Kalman Filter ◽  
Measurement Error ◽  
Frequency Shift ◽  
Velocity Measurement ◽  
Linear Prediction ◽  
Impulsive Noise ◽  
Signal To Noise Ratio ◽  
Integer Ambiguity ◽  
Doppler Sonar ◽  
Short Time

Velocity is vital information for navigation and oceanic engineering. Coherent Doppler sonar is an accurate tool for velocity measurement, but its use is limited due to velocity ambiguity. Velocity measured by frequency shift has no velocity ambiguity, yet its measurement error is larger than that of coherent Doppler sonar. Therefore, coherent Doppler sonar assisted by frequency shift is used to accurately measure velocity without velocity ambiguity. However, the velocity measured by coherent Doppler sonar assisted by frequency shift is affected by impulsive noise. To decrease the impulsive noise, Kalman filter and linear prediction are proposed to improve the velocity sensing accuracy. In this method, the Kalman filter is used to decrease measurement error of velocity measured by frequency shift, and linear prediction is used to remove the impulsive noise generated by a wrong estimate of the integer ambiguity. Lab-based experiments were carried, and the results have shown that coherent Doppler sonar assisted by frequency shift, Kalman filter and linear prediction can provide accurate and precise velocity with short time delay in a large range of signal to noise ratio.

scholarly journals Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement

Signals ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 434-455
Author(s):  
Sujan Kumar Roy ◽  
Kuldip K. Paliwal
Keyword(s):  
Kalman Filter ◽  
Speech Enhancement ◽  
Linear Prediction ◽  
Real Life ◽  
Model Parameters ◽  
Noise Variance ◽  
Noisy Speech ◽  
Kalman Gain ◽  
Whitening Filter ◽  
Prediction Coefficient

Inaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance. The existing methods propose a tuning of the biased Kalman gain, particularly in stationary noise conditions. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then, we construct a whitening filter (with its coefficients computed from the estimated noise) to pre-whiten each noisy speech frame prior to computing the speech LPC parameters. We then construct the KF with the estimated parameters, where the robustness metric offsets the bias in KF gain during speech absence of noisy speech to that of the sensitivity metric during speech presence to achieve better noise reduction. The noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on the NOIZEUS corpus demonstrate that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

Multi-day flow forecasting using the Kalman filter

1991 ◽  
Vol 18 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Murray A. Fitch ◽  
Edward A. McBean
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Linear Prediction ◽  
Measured Data ◽  
System Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Forecasting Performance ◽  
Optimal Forecasting ◽  
Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

Rayleigh waves from high-gain long-period stations: Signal extraction, amplitude determination, and separation of overlapping wave trains

1975 ◽  
Vol 65 (6) ◽  
pp. 1761-1778 ◽  
Author(s):  
Eduard Berg
Keyword(s):  
Rayleigh Waves ◽  
Signal To Noise Ratio ◽  
High Gain ◽  
Clear Separation ◽  
Novaya Zemlya ◽  
Oceanic Surface ◽  
Single Station ◽  
Wave Trains ◽  
Short Time

abstract For a signal-to-noise ratio between 0.2 and 0.1 on the original single-component records, amplitudes for Rayleigh waves over oceanic paths of 155° at station MAT and 98° at station KIP have been determined as 12 mμ and 24 mμ peak-to-peak, respectively, with a standard error of less than 11 per cent. In each case the processed correlation signal is the highest in a half-hour record. The method makes use of preliminary high-pass filtering and normalized reference earthquake-matched filtering, and takes full advantage of the well-dispersed oceanic surface wave. The method also provides high resolution of co-located events with short time separation, or of widely spaced events with Rayleigh waves arriving nearly simultaneously at a single station, when the summed vertical and radial matched filtered components are used. Examples include: (1) clear separation and amplitude determination at stations KIP and MAT of two MS = 6.5 earthquakes located 0.7° and 145 sec apart off the coast of central Chile; (2) clear separation at station KIP of a Novaya Zemlya mb = 4.8 event from interfering Rayleigh waves of an mb = 5.0 Kermadec Island earthquake arriving 120 to 140 sec prior to the searched event, with almost complete elimination of interference on the summed vertical and radial processed components; and (3) clear separation at station KIP of two co-located mb = 4.4 and 4.5 earthquakes 6 min apart off the coast of Chile, with determination of their amplitudes in the presence of interfering Rayleigh waves from two central Alaska earthquakes, the first (mb = 4.1) arriving 15 min prior to the first Chile Rayleigh wave and the second between the two Chile arrivals. The single-station threshold reached (10 and 25 digital units, p-p) for stations MAT and KIP at 155° and 98°, respectively, corresponds to an MS = 3.3 and probably can be improved further.

Estimating Optimum Filter Length in Linear Prediction

1997 ◽  
Vol 51 (5) ◽  
pp. 718-720 ◽  
Author(s):  
O.-P. Sievänen
Keyword(s):  
Raman Spectra ◽  
Prediction Error ◽  
Linear Prediction ◽  
Signal To Noise Ratio ◽  
New Method ◽  
Signal To Noise ◽  
Filter Length ◽  
Optimum Filter ◽  
Good Signal ◽  
Prediction Filter

In this article a new method to estimate optimum filter length in linear prediction is described. Linear prediction was used to enhance resolution of a spectrum. In particular, the dependence of prediction error on filter length has been studied. With calculations of simulated spectra it is shown that the prediction error falls rapidly when the filter length attains its optimum value. This effect is quite pronounced when the spectrum has a good signal-to-noise ratio and the modified covariance method is used to calculate prediction filter coefficients. The method is illustrated with applications to real Raman spectra.

scholarly journals Machine Learning Based Track Classification and Estimation using Kalman Filter

2020 ◽  
Vol 9 (1) ◽  
pp. 1700-1704
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Velocity Model ◽  
Supervised Machine Learning ◽  
Multiple Target ◽  
Low Snr

Classification of target from a mixture of multiple target information is quite challenging. In This paper we have used supervised Machine learning algorithm namely Linear Regression to classify the received data which is a mixture of target-return with the noise and clutter. Target state is estimated from the classified data using Kalman filter. Linear Kalman filter with constant velocity model is used in this paper. Minimum Mean Square Error (MMSE) analysis is used to measure the performance of the estimated track at various Signal to Noise Ratio (SNR) levels. The results state that the error is high for Low SNR, for High SNR the error is Low

scholarly journals 4D Flow MRI Pressure Estimation Using Velocity Measurement-Error-Based Weighted Least-Squares

2020 ◽  
Vol 39 (5) ◽  
pp. 1668-1680 ◽  
Author(s):  
Jiacheng Zhang ◽  
Melissa C. Brindise ◽  
Sean Rothenberger ◽  
Susanne Schnell ◽  
Michael Markl ◽  
...  
Keyword(s):  
Measurement Error ◽  
Least Squares ◽  
Velocity Measurement ◽  
Weighted Least Squares ◽  
4D Flow Mri ◽  
4D Flow ◽  
Flow Mri ◽  
Pressure Estimation

scholarly journals A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5459 ◽  
Author(s):  
Xuliang Lu ◽  
Zhongbin Wang ◽  
Chao Tan ◽  
Haifeng Yan ◽  
Lei Si ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Measurement Error ◽  
Unscented Kalman Filter ◽  
Attitude Estimation ◽  
Measurement Unit ◽  
Hydraulic Support ◽  
Sensing System ◽  
Rock Structure ◽  
Gradient Descent Algorithm ◽  
Yaw Angle

To measure the support attitude of hydraulic support, a support attitude sensing system composed of an inertial measurement unit with microelectromechanical system (MEMS) was designed in this study. Yaw angle estimation with magnetometers is disturbed by the perturbed magnetic field generated by coal rock structure and high-power equipment of shearer in automatic coal mining working face. Roll and pitch angles are estimated using the MEMS gyroscope and accelerometer, and the accuracy is not reliable with time. In order to eliminate the measurement error of the sensors and obtain the high-accuracy attitude estimation of the system, an unscented Kalman filter based on quaternion according to the characteristics of complementation of the magnetometer, accelerometer and gyroscope is applied to optimize the solution of sensor data. Then the gradient descent algorithm is used to optimize the key parameter of unscented Kalman filter, namely process noise covariance, to improve the accuracy of attitude calculation. Finally, an experiment and industrial application show that the average measurement error of yaw angle is less than 2° and that of pitch angle and roll angle is less than 1°, which proves the efficiency and feasibility of the proposed system and method.

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