Stopband constraint case and the ambiguity function Daniel Jansson.

Slides:



Advertisements
Similar presentations
Dates for term tests Friday, February 07 Friday, March 07
Advertisements

HILBERT TRANSFORM Fourier, Laplace, and z-transforms change from the time-domain representation of a signal to the frequency-domain representation of the.
Chapter 4 Retiming.
Totally Unimodular Matrices
EE513 Audio Signals and Systems Digital Signal Processing (Synthesis) Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
Cellular Communications
Filtering Filtering is one of the most widely used complex signal processing operations The system implementing this operation is called a filter A filter.
Digital Signal Processing
1.2 Row Reduction and Echelon Forms
Linear Equations in Linear Algebra
Chapter 8: The Discrete Fourier Transform
Totally Unimodular Matrices Lecture 11: Feb 23 Simplex Algorithm Elliposid Algorithm.
Lecture 9: Fourier Transform Properties and Examples
Image (and Video) Coding and Processing Lecture 2: Basic Filtering Wade Trappe.
Spectral Analysis Spectral analysis is concerned with the determination of the energy or power spectrum of a continuous-time signal It is assumed that.
AGC DSP AGC DSP Professor A G Constantinides 1 Digital Filter Specifications Only the magnitude approximation problem Four basic types of ideal filters.
EEE422 Signals and Systems Laboratory Filters (FIR) Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
Normalised Least Mean-Square Adaptive Filtering
Relationship between Magnitude and Phase (cf. Oppenheim, 1999)
Leakage & Hanning Windows
EE513 Audio Signals and Systems Statistical Pattern Classification Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
DTFT And Fourier Transform
1 Chapter 8 The Discrete Fourier Transform 2 Introduction  In Chapters 2 and 3 we discussed the representation of sequences and LTI systems in terms.
Rev.S08 MAC 1140 Module 10 System of Equations and Inequalities II.
SYSTEMS OF EQUATIONS MATRIX SOLUTIONS TO LINEAR SYSTEMS
1. The Simplex Method.
1 Waveform Design For Active Sensing Systems – A Computational Approach.
The Wavelet Tutorial: Part3 The Discrete Wavelet Transform
1 CS 551/651: Structure of Spoken Language Lecture 8: Mathematical Descriptions of the Speech Signal John-Paul Hosom Fall 2008.
Transmit beam-pattern synthesis Waveform design for active sensing Chapters 13 – 14.
Correlated and Uncorrelated Signals Problem: we have two signals and. How “close” are they to each other? Example: in a radar (or sonar) we transmit a.
EE Audio Signals and Systems Digital Signal Processing (Synthesis) Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
1 BIEN425 – Lecture 10 By the end of the lecture, you should be able to: –Describe the reason and remedy of DFT leakage –Design and implement FIR filters.
Radar Signals Tutorial II: The Ambiguity Function
The Physical Layer Lowest layer in Network Hierarchy. Physical transmission of data. –Various flavors Copper wire, fiber optic, etc... –Physical limits.
Signals & systems Ch.3 Fourier Transform of Signals and LTI System 5/30/2016.
Radar Signals Tutorial 3 LFM, Coherent Train and Frequency Coding.
Zhongguo Liu_Biomedical Engineering_Shandong Univ. Chapter 8 The Discrete Fourier Transform Zhongguo Liu Biomedical Engineering School of Control.
Wavelets and Multiresolution Processing (Wavelet Transforms)
Fourier Analysis of Discrete Time Signals
Fundamentals of Digital Signal Processing. Fourier Transform of continuous time signals with t in sec and F in Hz (1/sec). Examples:
Study of Broadband Postbeamformer Interference Canceler Antenna Array Processor using Orthogonal Interference Beamformer Lal C. Godara and Presila Israt.
Z Transform Primer. Basic Concepts Consider a sequence of values: {x k : k = 0,1,2,... } These may be samples of a function x(t), sampled at instants.
1 Conditions for Distortionless Transmission Transmission is said to be distortion less if the input and output have identical wave shapes within a multiplicative.
Doc.: IEEE /1398r0 Submission November 2014 Slide 1 Shiwen He, Haiming Wang Preamble Sequence for IEEE aj (45GHz) Authors/contributors:
Autoregressive (AR) Spectral Estimation
Discrete-time Random Signals
Lecture 5 – 6 Z - Transform By Dileep Kumar.
University of Ioannina - Department of Computer Science Filtering in the Frequency Domain (Circulant Matrices and Convolution) Digital Image Processing.
1 1.2 Linear Equations in Linear Algebra Row Reduction and Echelon Forms © 2016 Pearson Education, Ltd.
What is filter ? A filter is a circuit that passes certain frequencies and rejects all others. The passband is the range of frequencies allowed through.
Fourier Transform and Spectra
1 Chapter 8 The Discrete Fourier Transform (cont.)
Signals & systems Ch.3 Fourier Transform of Signals and LTI System
Properties of the power spectral density (1/4)
Integral Transform Method
EEE422 Signals and Systems Laboratory
Spectral Analysis Spectral analysis is concerned with the determination of the energy or power spectrum of a continuous-time signal It is assumed that.
The Discrete Fourier Transform
Waveform design course Chapters 7 & 8 from Waveform Design for Active Sensing Systems A computational approach.
DFT and FFT By using the complex roots of unity, we can evaluate and interpolate a polynomial in O(n lg n) An example, here are the solutions to 8 =
Chapter 8 The Discrete Fourier Transform
Sampling the Fourier Transform
Preamble Sequence for aj(45GHz)
EE513 Audio Signals and Systems
copyright Robert J. Marks II
Chapter 8 The Discrete Fourier Transform
Tania Stathaki 811b LTI Discrete-Time Systems in Transform Domain Ideal Filters Zero Phase Transfer Functions Linear Phase Transfer.
Chapter 8 The Discrete Fourier Transform
Presentation transcript:

Stopband constraint case and the ambiguity function Daniel Jansson

Informationsteknologi Institutionen för informationsteknologi | Stopband constraint case Goal Generate discrete, unimodular sequences with frequency notches and good correlation properties Why? Avoiding reserved frequency bands is important in many applications (communications, navigation..) Avoiding other interference How? SCAN (Stopband CAN) / WeSCAN (Weighted Stopband CAN)

Informationsteknologi Institutionen för informationsteknologi | Stopband CAN (SCAN) Let {x(n)}, n = 1...N be the sought sequence Express the bands to be avoided as Define the DFT matrix with elements Form matrix S from the columns of F Ñ corresponding to the frequencies in Ω We suppress the spectral power of {x(n)} in Ω by minimizing where

Informationsteknologi Institutionen för informationsteknologi | Stopband CAN (SCAN) The problem on the previous slide is equivalent to where G are the remaining columns of F Ñ. Suppressing the correlation sidelobes is done using the CAN formulation

Informationsteknologi Institutionen för informationsteknologi | Stopband CAN (SCAN) Combining the frequency band suppression and the correlation sidelobe suppression problems we get where 0 ≤ λ ≤ 1 controls the relative weight on the two penalty functions. The problem is solved by using the algorithm on the next slide

Informationsteknologi Institutionen för informationsteknologi |

Informationsteknologi Institutionen för informationsteknologi | Stopband CAN (SCAN) If a constrained PAR is preferable to unimodularity the problem can be solved in the same way except x for each iteration is given by the solution to

Informationsteknologi Institutionen för informationsteknologi | Weighted SCAN (WeSCAN) Minimization of J 2 is a way of minimizing the ISL The more general WISL (weighted ISL) is given by where are weights

Informationsteknologi Institutionen för informationsteknologi | Weighted SCAN (WeSCAN) Let and D be the square root of Γ. Then the WISL can be minimized by solving where and Replace in the SCAN problem with and perform the SCAN algorithm, but do necessary changes that are straightforward.

Informationsteknologi Institutionen för informationsteknologi | Numerical examples The spectral power of a SCAN sequence generated with parameters N = 100, Ñ = 1000, λ = 0.7 and Ω = [0.2,0.3] Hz. P stop = -8.3 dB (peak stopband power)

Informationsteknologi Institutionen för informationsteknologi | Numerical examples The autocorrelation of a SCAN sequence generated with parameters N = 100, Ñ = 1000, λ = 0.7 and Ω = [0.2,0.3] Hz, P corr = dB (peak sidelobe level)

Informationsteknologi Institutionen för informationsteknologi | Numerical examples P stop and P corr vs λ

Informationsteknologi Institutionen för informationsteknologi | Numerical examples The spectral power of a WeSCAN sequence generated with γ 1 =0, γ 2 =0 and γ k =1 for larger k. P stop = dB (peak stopband power)

Informationsteknologi Institutionen för informationsteknologi | Numerical examples The autocorrelation of the WeSCAN sequence

Informationsteknologi Institutionen för informationsteknologi | Numerical examples The spectral power of a SCAN sequence generated with PAR ≤ 2

Informationsteknologi Institutionen för informationsteknologi | The Ambiguity Function The response of a matched filter to a signal with various time delays and Doppler frequency shifts (extension of the correlation concept). The (narrowband) ambiguity function is where u(t) is a probing signal which is assumed to be zero outside [0,T], τ is the time delay and f is the Doppler frequency shift.

Informationsteknologi Institutionen för informationsteknologi | The Ambiguity Function Three properties worth noting 1. The maximum value of |χ(τ,f)| is achieved at | χ(0,0)| and is the energy of the signal, E 2. d |χ(τ,f)|= |χ(-τ,-f)| 3. D

Informationsteknologi Institutionen för informationsteknologi | The Ambiguity Function Proofs 1. Cauchy-Schwartz gives and since | χ(0,0)| = E, property 1 follows. 2. Use the variable change t -> t+ τ which implies property 2.

Informationsteknologi Institutionen för informationsteknologi | The Ambiguity Function Proofs 3. The volume of |χ(τ,f)| 2 is given by Let W τ (f) be the Fourier transform of u(t)u*(t- τ). Parseval gives therefore

Informationsteknologi Institutionen för informationsteknologi | The Ambiguity Function Ambiguity function of a chirp

Informationsteknologi Institutionen för informationsteknologi | The Ambiguity Function Ambiguity function of a Golomb sequence

Informationsteknologi Institutionen för informationsteknologi | The Ambiguity Function Ambiguity function of CAN generated sequences

Informationsteknologi Institutionen för informationsteknologi | The Ambiguity Function Why is there a vertical stripe at the zero delay cut? The ZDC is nothing but the Fourier transform of u(t)u*(t). Since u(t) is unimodular we get and the sinc-function decreases quickly as f increases. No universal method that can synthesize an arbirtrary ambiguity function.

Informationsteknologi Institutionen för informationsteknologi | The Discrete AF Assume u(t) is on the form where p n (t) is an ideal rectangular pulse of length t p The ambiguity function can be written as Inserting τ = kt p and f = p/(Nt p ) gives where is called the discrete AF. If |p|<<N then

Informationsteknologi Institutionen för informationsteknologi | The Discrete AF Minimizing the sidelobes of the discrete AF in a certain region where and are the index sets specifying the region. Define the set of sequences as

Informationsteknologi Institutionen för informationsteknologi | The Discrete AF Denote the correlation between {x m (n)} and {x l (n)} by All values of are contained in the set Minimizing the correlations is thus equivalent to minimizing the discrete AF sidelobes.

Informationsteknologi Institutionen för informationsteknologi | The Discrete AF Define where All elements of appear in We can thus minimize which as we saw before is almost equivalent to Minimize by using the cyclic algorithm on the next slide

Informationsteknologi Institutionen för informationsteknologi | The Discrete AF

Informationsteknologi Institutionen för informationsteknologi | The Discrete AF