C#计算模数转换器（ADC）的参数DNL、INL、SNR等

本文主要是介绍C#计算模数转换器（ADC）的参数DNL、INL、SNR等，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

1、ADC参数计算类

/// <summary>/// ADC参数（SNR、THD、SFDR、ENOB）计算/// </summary>/// <param name="source"></param>/// <param name="snr"></param>/// <param name="thd"></param>/// <param name="sfdr"></param>/// <param name="enob"></param>public static void AdcParameterCalculate(double[] source,out double snr,out double thd,out double sfdr,out double enob){double P0 = 0;double P1 = 0;double PD = 0;double PN = 0;double PS = 0;for(int i=0; i<source.Length; i++){if (i == 0){P0 = source[i];}else if (i == 1){P1 = source[i];}else if(i>1 && i<11){PD += source[i];if (source[i] > PS){PS = source[i];}}else{PN += source[i];if (source[i] > PS){PS = source[i];}}}snr = 10 * Math.Log10(P1 / PN);thd = 10 * Math.Log10(PD / P1);sfdr = 10 * Math.Log10(P1 / PS);double sinad = 10 * Math.Log10(P1 / (PN + PD));enob = (sinad - 1.76) / 6.02;}/// <summary>/// ADC参数（DNL、INL）计算/// </summary>/// <param name="x"></param>/// <param name="y"></param>/// <param name="bits"></param>/// <param name="scope"></param>/// <param name="dnl"></param>/// <param name="inl"></param>public static void AdcDnlAndInlCalculate(List<double> x,List<double> y, int bits,double scope,out double dnl,out double inl){List<double> data = LinearRegression(x, y);double vlsb = GetVlsb(bits, scope);dnl = AdcDnl(x, y, vlsb);inl = AdcInl(data, y, vlsb);}/// <summary>/// 求和/// </summary>/// <param name="source"></param>/// <returns></returns>public static double Sum(List<double> source){double sum = 0;foreach(double d in source){sum+= d;}return sum;}/// <summary>/// 平方和/// </summary>/// <param name="source"></param>/// <returns></returns>public static double SumOfSquare(List<double> source){double sum = 0;foreach (double d in source){sum+= d*d;}return sum;}/// <summary>/// 交叉积和/// </summary>/// <param name="x"></param>/// <param name="y"></param>/// <returns></returns>public static double SumOfCross(List<double> x, List<double> y){double sum = 0;for(int i=0; i<x.Count; i++){double d = x[i] * y[i];sum+= d;}return sum;}/// <summary>/// 用最小二乘法计算一元一次函数的参数/// </summary>/// <param name="x"></param>/// <param name="y"></param>/// <param name="a"></param>/// <param name="b"></param>public static void LeastSquareMethod(List<double> x, List<double> y,out  double a,out double b){a = (SumOfSquare(x) * Sum(y) - Sum(x) * SumOfCross(x, y)) / (x.Count * SumOfSquare(x) - Sum(x) * Sum(x));b = (x.Count * SumOfCross(x, y) - Sum(x) * Sum(y)) / (x.Count * SumOfSquare(x) - Sum(x) * Sum(x));}/// <summary>/// 线性回归/// </summary>/// <param name="x"></param>/// <param name="y"></param>/// <returns></returns>public static List<double> LinearRegression(List<double> x, List<double> y){LeastSquareMethod(x, y, out double a, out double b);List<double> data = new List<double>();foreach (double d in x){double z = a + b * d;data.Add(z);}return data;}/// <summary>/// 计算VLSB/// </summary>/// <param name="bits"></param>/// <param name="scope"></param>/// <returns></returns>public static double GetVlsb(int bits, double scope){int count = (int)Math.Pow(2, bits);double d = (scope - 0) / (count - 1);//Console.WriteLine($"VLSB:{d}");return d;}/// <summary>/// 计算INL/// </summary>/// <param name="y1"></param>/// <param name="y"></param>/// <param name="vlsb"></param>/// <returns></returns>public static double AdcInl(List<double> y1, List<double> y, double vlsb){double max = 0;for (int i = 0; i < y1.Count; i++){double x = Math.Abs(y1[i] - y[i]);if (x > max) max = x;}double inl = max / vlsb;//Console.WriteLine($"INL:{inl}");return inl;}/// <summary>/// 计算DNL/// </summary>/// <param name="x"></param>/// <param name="y"></param>/// <param name="vlsb"></param>/// <returns></returns>public static double AdcDnl(List<double> x, List<double> y, double vlsb){double max = 0;for (int i = 1; i < x.Count; i++){double d = Math.Abs(y[i] - y[i - 1]) / Math.Abs(x[i] - x[i - 1]);if (d > max) max = d;}double dnl = (max - vlsb) / vlsb;//Console.WriteLine($"DNL:{dnl}");return dnl;}

2、应用

private void AdcInitialize(){List<List<double>> idealData = IdealAdcData(4, 100);AdcDnlAndInlCalculate(idealData[0], idealData[1], 4, 100, out double dnl, out double inl);Console.WriteLine($"DNL:{dnl}");Console.WriteLine($"INL:{inl}");easyChartX2.Plot(idealData[1], 0, 1);}

double[] res = FFT.ButterflyFFT(data);FFT.AdcParameterCalculate(res, out SNR, out THD, out SFDR, out ENOB);

其中data为数据

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