Estimation of Doppler Spectrum Parameters Comparison between FFT-based processing and Adaptive Filtering Processing J. Figueras i Ventura 1, M. Pinsky.

Slides:



Advertisements
Similar presentations
Eigen-decomposition Techniques for Skywave Interference Detection in Loran-C Receivers Abbas Mohammed, Fernand Le Roux and David Last Dept. of Telecommunications.
Advertisements

Acoustic Echo Cancellation for Low Cost Applications
MEDT8007 Simulering av ultralydsignal fra spredere i bevegelse Hans Torp Institutt for sirkulasjon og medisinsk bildediagnostikk Hans Torp NTNU, Norway.
ECE 8443 – Pattern Recognition ECE 8423 – Adaptive Signal Processing Objectives: The Linear Prediction Model The Autocorrelation Method Levinson and Durbin.
Speech Enhancement through Noise Reduction By Yating & Kundan.
Fourier series With coefficients:. Complex Fourier series Fourier transform (transforms series from time to frequency domain) Discrete Fourier transform.
PACE: An Autofocus Algorithm For SAR
Your Name Your Title Your Organization (Line #1) Your Organization (Line #2) Semester 2 Update Joe Hoatam Josh Merritt Aaron Nielsen.
1 Easy Does It: User Parameter Free Dense and Sparse Methods for Spectral Estimation Jian Li Department of Electrical and Computer Engineering University.
March 29, 2010 RFI Mitigation Workshop, Groningen The Netherlands 1 Statistics of the Spectral Kurtosis Estimator Gelu M. Nita and Dale E. Gary New Jersey.
Carlos A. Rodríguez Rivera Mentor: Dr. Robert Palmer Carlos A. Rodríguez Rivera Mentor: Dr. Robert Palmer Is Spectral Processing Important for Future WSR-88D.
Spectrum Sensing Based on Cyclostationarity In the name of Allah Spectrum Sensing Based on Cyclostationarity Presented by: Eniseh Berenjkoub Summer 2009.
Single-Channel Speech Enhancement in Both White and Colored Noise Xin Lei Xiao Li Han Yan June 5, 2002.
1 Adaptive computer-based spatial -filtering method for more accurate estimation of the surface velocity of debris flow APPLIED OPTICS M. Shorif, Hiroyuki.
TR32 time series comparison Victor Venema. Content  Jan Schween –Wind game: measurement and synthetic –Temporal resolution of 0.1 seconds  Heye Bogena.
“Questions in peak bagging” 1)Two components: leakage matrix & spectral line profile - do we have sufficient accuracy? (simulated data sufficiently real?)
Despeckle Filtering in Medical Ultrasound Imaging
Inputs to Signal Generation.vi: -Initial Distance (m) -Velocity (m/s) -Chirp Duration (s) -Sampling Info (Sampling Frequency, Window Size) -Original Signal.
1 Rising Noise Level Simulation Henry Skiba. 2 Sinusoid Signal to noise level –SNRdB = 10log 10 SNR Tested range was -60dB to 90db with stepping of 1.
11/18/02Technical Interchange Meeting Progress in FY-02 Research RDA –Capability to collect time series data –Control of phase shifter Phase coding –Sigmet’s.
Normalization of the Speech Modulation Spectra for Robust Speech Recognition Xiong Xiao, Eng Siong Chng, and Haizhou Li Wen-Yi Chu Department of Computer.
ElectroScience Lab IGARSS 2011 Vancouver Jul 26th, 2011 Chun-Sik Chae and Joel T. Johnson ElectroScience Laboratory Department of Electrical and Computer.
Delft University of Technology 1 Do eddy dissipation rate retrievals work for precipitation profiling Doppler radar?, CESAR Science Day, June 18th, 2014.
On Estimation of Surface Soil Moisture from SAR Jiancheng Shi Institute for Computational Earth System Science University of California, Santa Barbara.
EFFECTS OF MUTUAL COUPLING AND DIRECTIVITY ON DOA ESTIMATION USING MUSIC LOPAMUDRA KUNDU & ZHE ZHANG.
CloudNet: TARA status and database H. Russchenberg, O. Krasnov Delft University of Technology – IRCTR, The Netherlands.
International Research Centre for Telecommunications and Radar High resolution 3D wind profiling using an S-band polarimetric FM-CW radar: dealiasing techniques.
Speech Enhancement Using Spectral Subtraction
Acoustic impulse response measurement using speech and music signals John Usher Barcelona Media – Innovation Centre | Av. Diagonal, 177, planta 9,
Ping Zhang, Zhen Li,Jianmin Zhou, Quan Chen, Bangsen Tian
WAVELET TRANSFORM.
Status of VIRGO Sipho van der Putten. 2 Contents Introduction to gravitational waves VIRGO Pulsars: gravitational waves from periodic sources Pulsars.
Extracting Barcodes from a Camera-Shaken Image on Camera Phones Graduate Institute of Communication Engineering National Taiwan University Chung-Hua Chu,
Simulation Of A Cooperative Protocol For Common Control Channel Implementation Prepared by: Aishah Thaher Shymaa Khalaf Supervisor: Dr.Ahmed Al-Masri.
Advanced Digital Signal Processing
Technical Interchange Meeting Spring 2008: Status and Accomplishments.
CEPSTRAL ANALYSIS Cepstral analysis synthesis on the mel frequency scale, and an adaptative algorithm for it. Cecilia Caruncho Llaguno.
Independent Component Analysis Algorithm for Adaptive Noise Cancelling 적응 잡음 제거를 위한 독립 성분 분석 알고리즘 Hyung-Min Park, Sang-Hoon Oh, and Soo-Young Lee Brain.
1 RADAR OPERATIONS CENTER (ROC) EVALUATION OF THE WSR-88D OPEN RADAR DATA ACQUISITION (ORDA) SYSTEM SIGNAL PROCESSING WSR-88D Radar Operations Center Engineering.
WEATHER SIGNALS Chapter 4 (Focus is on weather signals or echoes from radar resolution volumes filled with countless discrete scatterers---rain, insects,
0 - 1 © 2007 Texas Instruments Inc, Content developed in partnership with Tel-Aviv University From MATLAB ® and Simulink ® to Real Time with TI DSPs Spectrum.
1 Spectral identification & suppression of ground clutter contributions for phased array radar Spectral identification of ground clutter Spectral identification.
Background 2 Outline 3 Scopus publications 4 Goal and a signal model 5Harmonic signal parameters estimation.
DOPPLER SPECTRA OF WEATHER SIGNALS (Chapter 5; examples from chapter 9)
SUB-NYQUIST DOPPLER RADAR WITH UNKNOWN NUMBER OF TARGETS A project by: Gil Ilan & Alex Dikopoltsev Guided by: Yonina Eldar & Omer Bar-Ilan Project #: 1489.
Frequency-Wavenumber Domain EECS 800 – Patrick McCormick.
Tracking Mobile Nodes Using RF Doppler Shifts
Suppression of Musical Noise Artifacts in Audio Noise Reduction by Adaptive 2D filtering Alexey Lukin AES Member Moscow State University, Moscow, Russia.
Air Systems Division Definition of anisotropic denoising operators via sectional curvature Stanley Durrleman September 19, 2006.
Jun Li 1, Zhongdong Yang 1, W. Paul Menzel 2, and H.-L. Huang 1 1 Cooperative Institute for Meteorological Satellite Studies (CIMSS), UW-Madison 2 NOAA/NESDIS/ORA.
BRAIN TISSUE IMPEDANCE ESTIMATION Improve the Brain’s Evoked Potential’s source Temporal and Spatial Inverse Problem Improve the Brain Tissue Impedance.
A radar data simulator for SuperDARN A.J. Ribeiro, P.V. Ponomarenko, J.M. Ruohoniemi, J.B.H. Baker, L.B.N. Clausen, R.A. Greenwald /29/2012 SuperDARN.
Improved Direction of Arrival Methods for Oceanographic HF Radars Brian Emery Department of Mechanical Engineering, and Marine Science Institute University.
V4 – Video Tracker for Extremely Hard Night Conditions
Fourier series With coefficients:.
k is the frequency index
Eng.: Ahmed Abdo AbouelFadl
Adnan Quadri & Dr. Naima Kaabouch Optimization Efficiency
Doppler Radar Basics Pulsed radar
the University of Oklahoma
Examples of spectral fields
Convolution and Deconvolution
k is the frequency index
Submission Title: [Robust Ranging Algorithm for UWB radio]
Submission Title: [Robust Ranging Algorithm for UWB radio]
TARA data for CloudNet H. Russchenberg , O. Krasnov Delft University of Technology – IRCTR, The Netherlands.
Submission Title: [Robust Ranging Algorithm for UWB radio]
Presenter: Shih-Hsiang(士翔)
Real-time Uncertainty Output for MBES Systems
A Multi-Channel Partial-Update Algorithm for
Presentation transcript:

Estimation of Doppler Spectrum Parameters Comparison between FFT-based processing and Adaptive Filtering Processing J. Figueras i Ventura 1, M. Pinsky 2, A. Sterking 3, A. Khain 2, H.W.J. Russchenberg 1 1 IRCTR-TU Delft 2 Institute of Earth Sciences-The Hebrew University of Jerusalem 3 Weizmann Institute of Science

ERAD 2006: Sept Contents Signal and noise models of the adaptive processing Adaptive filter estimation Implementation of the two algorithms Comparison of the two algorithms

ERAD 2006: Sept Model of the atmospheric radar signal For each range cell: Complex autoregressive series of first order with unknown coefficient Spectrum symmetric relative to average Doppler frequency

ERAD 2006: Sept Calculation of Doppler spectrum parameters Doppler spectrum parameters Mean Doppler frequency: Doppler spectrum width:

ERAD 2006: Sept Simulated Signal Simulated signal: Noise: uncorrelated additive normal complex white noise

ERAD 2006: Sept Adaptive filter estimation Objective: Suppress noise Accurately estimate a

ERAD 2006: Sept Adaptive filter estimation Recurrent estimation of a

ERAD 2006: Sept Implementation of the algorithms

ERAD 2006: Sept Comparison: Processing speed Number of operations: FFT: 6N log 2 N Adaptive: 41 N Advantage in real time operations !

ERAD 2006: Sept Convergence of spectral moments Overestimation of Doppler spectrum width Mean Frequency: 0.5 Doppler width: 0.4

ERAD 2006: Sept Estimation of weak signals ParameterGivenAdaptiveFFT 6 dB Clipping Signal Power NaN Mean Velocity 11.03NaN Doppler width NaN SNR=0.09

ERAD 2006: Sept Comparison: Real Data Clipping:

ERAD 2006: Sept Conclusion Adaptive Doppler processing: Good estimation of the Reflectivity and mean Doppler velocity Overestimation of Doppler spectrum width Faster signal processing than FFT processing Estimation of weaker signals Simple signal model: Bi-modal atmospheric signal?