{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 3 Detection & Estimation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example3.1 page 120" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from __future__ import division\n", "from numpy import arange, ones, sqrt\n", "%matplotlib inline\n", "from matplotlib.pyplot import plot, subplot, xlabel,ylabel,title, show\n", "\n", "#using Gram-Schmidt orthogonalization procedure\n", "T = 1#\n", "t1 = arange(0,0.01+T/3,0.01)\n", "t2 = arange(0,0.01+2*T/3,0.01)\n", "t3 = arange(T/3,0.01+T,0.01)\n", "t4 = arange(0,0.01+T,0.01)\n", "s1t = [0]+[x for x in ones(len(t1)-2)]+[0]\n", "s2t = [0]+[x for x in ones(len(t2)-2)]+[0]\n", "s3t = [0]+[x for x in ones(len(t3)-2)]+[0]\n", "s4t = [0]+[x for x in ones(len(t4)-2)]+[0]\n", "t5 = arange(0,0.01+T/3,0.01)\n", "phi1t = [sqrt(3/T)*x for x in [0]+[x for x in ones(len(t5)-2)]+[0]]\n", "t6 =arange(T/3,0.01+2*T/3,0.01)\n", "phi2t = [sqrt(3/T)*x for x in [0]+[x for x in ones(len(t6)-2)]+[0]]\n", "t7 = arange(2*T/3,0.01+T,0.01)\n", "phi3t = [sqrt(3/T)*x for x in [0]+[x for x in ones(len(t7)-2)]+[0]]\n", "\n", "#figure\n", "title('Figure3.4(a) Set of signals to be orthonormalized')\n", "subplot(4,1,1)\n", "plot(t1,s1t)\n", "xlabel('t')\n", "ylabel('s1(t)')\n", "subplot(4,1,2)\n", "plot(t2,s2t)\n", "xlabel('t')\n", "ylabel('s2(t)')\n", "subplot(4,1,3)\n", "plot(t3,s3t)\n", "xlabel('t')\n", "ylabel('s3(t)')\n", "subplot(4,1,4)\n", "plot(t4,s4t)\n", "xlabel('t')\n", "ylabel('s4(t)')\n", "show()\n", "\n", "\n", "#figure\n", "title('Figure3.4(b) The resulting set of orthonormal functions')\n", "subplot(3,1,1)\n", "plot(t5,phi1t)\n", "xlabel('t')\n", "ylabel('phi1(t)')\n", "subplot(3,1,2)\n", "plot(t6,phi2t)\n", "xlabel('t')\n", "ylabel('phi2(t)')\n", "subplot(3,1,3)\n", "plot(t7,phi3t)\n", "xlabel('t')\n", "ylabel('phi3(t)')\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example3.2 page 121" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Figure 3.8 (b).Representation of transmitted dibits\n", "Loc. of meg.point| (-3/2)asqrt(T)|(-1/2)asqrt(T)|(3/2)asqrt(T)|(1/2)asqrt(T)\n", "________________________________________________________________________________\n", "Transmitted dibit| 00 | 01 | 11 | 10\n", "\n", "\n", "Figure 3.8 (c). Decision intervals for received dibits\n", "Received dibit | 00 | 01 | 11 | 10\n", "________________________________________________________________________________\n", "Interval on phi1(t)| x1 < -a.sqrt(T) |-a.sqrt(T)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from __future__ import division\n", "from numpy import sqrt,arange,random,sin,pi,zeros,multiply\n", "%matplotlib inline\n", "from matplotlib.pyplot import plot,subplot,xlabel,ylabel,title,show,grid\n", "\n", "#Signal constellation and Representation of dibits\n", "a =1# #amplitude =1\n", "T =1# #Symbol duration in seconds\n", "#Four message points\n", "Si1 = [(-3/2)*a*sqrt(T),(-1/2)*a*sqrt(T),(3/2)*a*sqrt(T),(1/2)*a*sqrt(T)]\n", "plot(Si1,[0,0,0,0])\n", "xlabel('phi1(t)')\n", "title('Figure 3.8 (a) Signal constellation')\n", "grid()\n", "show()\n", "print 'Figure 3.8 (b).Representation of transmitted dibits'\n", "print 'Loc. of meg.point| (-3/2)asqrt(T)|(-1/2)asqrt(T)|(3/2)asqrt(T)|(1/2)asqrt(T)'\n", "print '________________________________________________________________________________'\n", "print 'Transmitted dibit| 00 | 01 | 11 | 10'\n", "print ''\n", "print ''\n", "print 'Figure 3.8 (c). Decision intervals for received dibits'\n", "print 'Received dibit | 00 | 01 | 11 | 10'\n", "print '________________________________________________________________________________'\n", "print 'Interval on phi1(t)| x1 < -a.sqrt(T) |-a.sqrt(T)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from __future__ import division\n", "from numpy import arange,sqrt,cos,pi,convolve\n", "%matplotlib inline\n", "from matplotlib.pyplot import plot,subplot,title,show\n", "\n", "\n", "fc =4# #carrier frequency in Hz\n", "T =1#\n", "t1 = arange(0,0.01+T,0.01)\n", "phit = [sqrt(2/T)*xx for xx in cos(2*pi*fc*t1)]\n", "hopt = phit#\n", "\n", "phiot = convolve(phit,hopt)#\n", "phiot = [yy/max(phiot) for yy in phiot]\n", "\n", "t2 = arange(0,0.01+2*T,0.01)\n", "subplot(2,1,1)\n", "plot(t1,phit)#\n", "title('Figure 3.13 (a) RF pulse input')\n", "subplot(2,1,2)\n", "plot(t2,phiot)#\n", "title('Figure 3.13 (b) Matched Filter output')\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example3.4 page 124" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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21j77bNiSlM6VV1rpvrvuCluS8sWwYZYnPdfZNFevNjPhgw9a+Od111koaLUQ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\n8PPPpvCdik0h89k7RcLPP8NRR8F//+u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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from __future__ import division\n", "from numpy import random,convolve\n", "%matplotlib inline\n", "from matplotlib.pyplot import plot,subplot,title,show\n", "\n", "\n", "phit = [0.1*xx for xx in random.uniform(0,1,10)]\n", "hopt = phit\n", "phi0t = convolve(phit,hopt)\n", "phi0t = [yy/max(phi0t) for yy in phi0t]\n", "subplot(2,1,1)\n", "plot(range(0,len(phit)),phit)\n", "title('Figure 3.16 (a) Noise Like input signal')\n", "subplot(2,1,2)\n", "plot(range(0,len(phi0t)),phi0t)\n", "title('Figure 3.16 (b) Matched Filter output')\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example3.6 page 127" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictor-error variance 0.64\n", "1 Predictor input variance 1\n", "The predictor-error variance is less than the variance of the predictor input\n" ] } ], "source": [ "from __future__ import division\n", "\n", "Rxx = [0.6, 1, 0.6]\n", "h01 = Rxx[2]/Rxx[1]# #Rxx(2) = Rxx(0), Rxx(3) = Rxx(1)\n", "sigma_E = Rxx[1] - h01*Rxx[2]\n", "sigma_X = Rxx[1]\n", "print 'Predictor-error variance',sigma_E\n", "print sigma_X,'Predictor input variance',sigma_X\n", "if(sigma_X > sigma_E):\n", " print 'The predictor-error variance is less than the variance of the predictor input'\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example3.29 page 137" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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jvvvuw4EDB3L05eOPP77el/79+yMlJQWdO3dGyZIl0bFjR2RkZGiOxX//RePo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\nd3FxcalluILfxcXFpZbx/3rGsx341WU1AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from numpy import arange,sin,pi,random,zeros\n", "%matplotlib inline\n", "from matplotlib.pyplot import plot,subplot,title,show\n", "\n", "\n", "#Implementation of LMS ADAPTIVE FILTER\n", "#For noise cancellation application\n", "\n", "order = 18;\n", "t = arange(0,0.01+1,.01)\n", "x = sin(2*pi*5*t);\n", "noise =random.random(len(x));\n", "x_n = x+noise;\n", "ref_noise = noise*random.random();\n", "w = zeros(order)\n", "mu = 0.01*(sum(x**22)/len(x))\n", "N = len(x);\n", "subplot(4,1,1)\n", "plot(t,x)\n", "title('Orignal Input Signal')\n", "subplot(4,1,2)\n", "plot(t,noise)\n", "title('random noise')\n", "subplot(4,1,3)\n", "plot(t,x_n)\n", "title('Signal+noise')\n", "show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }