MATLAB程序用于估计接收到的直扩信号的载频、码速率和扩频增益：
主程序文件
%% 直扩信号参数估计：载频、码速率和扩频增益
clear; close all; clc;fprintf('=== 直扩信号参数估计 ===\n');%% 生成测试直扩信号（如果无真实信号）
[receivedSignal, params] = generateDSSSTestSignal();%% 信号预处理
[preprocessedSignal, preprocessingInfo] = preprocessSignal(receivedSignal, params);%% 载频估计
[carrierFreq, carrierEstimationInfo] = estimateCarrierFrequency(preprocessedSignal, params);%% 码速率估计
[codeRate, codeEstimationInfo] = estimateCodeRate(preprocessedSignal, params);%% 扩频增益估计
[spreadingGain, gainEstimationInfo] = estimateSpreadingGain(preprocessedSignal, codeRate, params);%% 参数验证和精度分析
[validationResults, confidence] = validateEstimations(carrierFreq, codeRate, spreadingGain, params);%% 结果显示和可视化
displayResults(carrierFreq, codeRate, spreadingGain, validationResults, confidence, params);%% 性能分析
analyzePerformance(carrierFreq, codeRate, spreadingGain, params);fprintf('\n直扩信号参数估计完成！\n');
测试信号生成
function [receivedSignal, params] = generateDSSSTestSignal()% 生成直扩测试信号fprintf('生成直扩测试信号...\n');% 信号参数params = struct();params.true.carrierFreq = 2.4e9; % 真实载频 2.4 GHzparams.true.codeRate = 10e6; % 真实码速率 10 MHzparams.true.spreadingGain = 127; % 真实扩频增益 127params.true.samplingRate = 100e6; % 采样率 100 MHzparams.true.signalDuration = 100e-6; % 信号持续时间 100 μs% 信道参数params.channel.SNR = 10; % 信噪比 dBparams.channel.frequencyOffset = 50e3; % 频率偏移 50 kHzparams.channel.phaseNoise = 0.01; % 相位噪声% 计算信号参数numSamples = round(params.true.signalDuration * params.true.samplingRate);t = (0:numSamples-1) / params.true.samplingRate;% 生成扩频码 (Gold码或m序列)spreadingCode = generateSpreadingCode(params.true.spreadingGain);% 生成信息序列infoBits = randi([0 1], 1, ceil(numSamples / params.true.spreadingGain));% 扩频调制basebandSignal = spreadSignal(infoBits, spreadingCode, params);% 载波调制carrier = exp(1j * 2 * pi * params.true.carrierFreq * t);modulatedSignal = basebandSignal .* carrier;% 添加频率偏移和相位噪声frequencyOffset = exp(1j * 2 * pi * params.channel.frequencyOffset * t);phaseNoise = exp(1j * params.channel.phaseNoise * randn(size(t)));modulatedSignal = modulatedSignal .* frequencyOffset .* phaseNoise;% 添加噪声receivedSignal = addNoise(modulatedSignal, params.channel.SNR);fprintf('测试信号生成完成:\n');fprintf(' 真实载频: %.3f GHz\n', params.true.carrierFreq/1e9);fprintf(' 真实码速率: %.3f MHz\n', params.true.codeRate/1e6);fprintf(' 真实扩频增益: %d\n', params.true.spreadingGain);fprintf(' 信号长度: %d 采样点\n', numSamples);
endfunction spreadingCode = generateSpreadingCode(gain)% 生成扩频码 (m序列)% 使用线性反馈移位寄存器生成m序列registerLength = ceil(log2(gain + 1));% 选择本原多项式 (示例)switch registerLengthcase 7 % 127位m序列taps = [7, 1]; % x^7 + x + 1case 6 % 63位m序列taps = [6, 1]; % x^6 + x + 1case 5 % 31位m序列taps = [5, 2]; % x^5 + x^2 + 1otherwisetaps = [registerLength, 1]; % 默认end% 初始化寄存器register = ones(1, registerLength);spreadingCode = zeros(1, 2^registerLength - 1);for i = 1:length(spreadingCode)% 输出位spreadingCode(i) = register(end);% 计算反馈feedback = mod(sum(register(taps)), 2);% 移位register = [feedback, register(1:end-1)];end% 转换为BPSK调制 (-1, +1)spreadingCode = 2 * spreadingCode - 1;% 截断到指定增益spreadingCode = spreadingCode(1:gain);
endfunction spreadSignal = spreadSignal(infoBits, spreadingCode, params)% 扩频调制numChipsPerBit = length(spreadingCode);numBits = length(infoBits);% 将信息比特映射到BPSKmappedBits = 2 * infoBits - 1;% 扩频spreadSignal = zeros(1, numBits * numChipsPerBit);for i = 1:numBitsstartIdx = (i-1) * numChipsPerBit + 1;endIdx = i * numChipsPerBit;spreadSignal(startIdx:endIdx) = mappedBits(i) * spreadingCode;end% 调整采样率samplesPerChip = round(params.true.samplingRate / params.true.codeRate);if samplesPerChip > 1% 上采样spreadSignal = repelem(spreadSignal, samplesPerChip);end
endfunction noisySignal = addNoise(signal, SNR_dB)% 添加高斯白噪声signalPower = mean(abs(signal).^2);noisePower = signalPower / (10^(SNR_dB/10));noise = sqrt(noisePower/2) * (randn(size(signal)) + 1j*randn(size(signal)));noisySignal = signal + noise;
end
信号预处理
function [preprocessedSignal, info] = preprocessSignal(receivedSignal, params)% 信号预处理fprintf('信号预处理...\n');info = struct();% 1. 自动增益控制preprocessedSignal = automaticGainControl(receivedSignal);% 2. 带通滤波（如果知道大致频带）if isfield(params, 'estimatedCarrierRange')preprocessedSignal = bandpassFilter(preprocessedSignal, params);end% 3. 下变频到基带（使用估计的载频或已知范围）if isfield(params, 'estimatedCarrierFreq')preprocessedSignal = downconvertToBaseband(preprocessedSignal, params);end% 4. 计算信号统计特性info.signalPower = mean(abs(preprocessedSignal).^2);info.noisePower = estimateNoisePower(preprocessedSignal);info.estimatedSNR = 10 * log10(info.signalPower / info.noisePower);fprintf(' 信号功率: %.2f dB\n', 10*log10(info.signalPower));fprintf(' 噪声功率: %.2f dB\n', 10*log10(info.noisePower));fprintf(' 估计SNR: %.2f dB\n', info.estimatedSNR);fprintf('信号预处理完成\n');
endfunction processedSignal = automaticGainControl(signal)% 自动增益控制targetPower = 1.0;currentPower = mean(abs(signal).^2);gain = sqrt(targetPower / currentPower);processedSignal = signal * gain;
endfunction filteredSignal = bandpassFilter(signal, params)% 带通滤波samplingRate = params.true.samplingRate;if isfield(params, 'estimatedCarrierRange')% 使用估计的载频范围f_low = params.estimatedCarrierRange(1);f_high = params.estimatedCarrierRange(2);else% 默认频带f_low = 2.3e9;f_high = 2.5e9;end% 设计带通滤波器nyquist = samplingRate / 2;Wn = [f_low, f_high] / nyquist;if Wn(2) >= 1Wn(2) = 0.99;end[b, a] = butter(6, Wn, 'bandpass');filteredSignal = filter(b, a, signal);
endfunction basebandSignal = downconvertToBaseband(signal, params)% 下变频到基带samplingRate = params.true.samplingRate;t = (0:length(signal)-1) / samplingRate;if isfield(params, 'estimatedCarrierFreq')carrierFreq = params.estimatedCarrierFreq;elsecarrierFreq = params.true.carrierFreq; % 使用真实值（实际中未知）end% 生成本地载波localCarrier = exp(-1j * 2 * pi * carrierFreq * t);% 下变频basebandSignal = signal .* localCarrier;% 低通滤波cutoffFreq = min(10e6, samplingRate/4); % 假设信号带宽小于10MHzWn = cutoffFreq / (samplingRate/2);[b, a] = butter(6, Wn, 'low');basebandSignal = filter(b, a, basebandSignal);
endfunction noisePower = estimateNoisePower(signal)% 估计噪声功率% 使用小波变换估计噪声[c, l] = wavedec(real(signal), 5, 'db4');noisePower = median(abs(c)) / 0.6745;noisePower = noisePower^2;
end
载频估计
function [carrierFreq, info] = estimateCarrierFrequency(signal, params)% 估计载波频率fprintf('估计载波频率...\n');info = struct();samplingRate = params.true.samplingRate;% 方法1: 功率谱密度峰值检测[carrierFreq1, confidence1] = estimateByPSD(signal, samplingRate);% 方法2: 循环平稳特征检测[carrierFreq2, confidence2] = estimateByCyclostationarity(signal, samplingRate);% 方法3: 高阶统计量[carrierFreq3, confidence3] = estimateByHigherOrderStats(signal, samplingRate);% 融合估计结果carrierFreq = fuseEstimations(carrierFreq1, carrierFreq2, carrierFreq3, ...confidence1, confidence2, confidence3);info.methods.psd = carrierFreq1;info.methods.cyclo = carrierFreq2;info.methods.hos = carrierFreq3;info.confidence = [confidence1, confidence2, confidence3];info.finalEstimation = carrierFreq;% 误差分析if isfield(params.true, 'carrierFreq')trueFreq = params.true.carrierFreq;error = abs(carrierFreq - trueFreq);relativeError = error / trueFreq * 100;info.trueValue = trueFreq;info.absoluteError = error;info.relativeError = relativeError;fprintf(' 真实载频: %.6f GHz\n', trueFreq/1e9);fprintf(' 估计载频: %.6f GHz\n', carrierFreq/1e9);fprintf(' 绝对误差: %.3f kHz\n', error/1e3);fprintf(' 相对误差: %.3f%%\n', relativeError);elsefprintf(' 估计载频: %.6f GHz\n', carrierFreq/1e9);endfprintf('载频估计完成\n');
endfunction [carrierFreq, confidence] = estimateByPSD(signal, samplingRate)% 通过功率谱密度估计载频% 计算功率谱密度[psd, f] = pwelch(signal, hamming(1024), 512, 1024, samplingRate);% 寻找峰值[peaks, peakLocs] = findpeaks(psd, 'SortStr', 'descend', 'NPeaks', 5);if isempty(peaks)carrierFreq = 0;confidence = 0;return;end% 选择最高峰值对应的频率mainPeakLoc = peakLocs(1);carrierFreq = f(mainPeakLoc);% 计算置信度（基于峰值突出程度）if length(peaks) > 1prominence = (peaks(1) - peaks(2)) / peaks(1);confidence = min(1, prominence * 2);elseconfidence = 0.7;end% 可视化if false % 设置为true来显示PSDfigure;plot(f/1e6, 10*log10(psd));xlabel('频率 (MHz)');ylabel('功率谱密度 (dB/Hz)');title('功率谱密度分析');grid on;hold on;plot(carrierFreq/1e6, 10*log10(peaks(1)), 'ro', 'MarkerSize', 10);legend('PSD', '估计载频');end
endfunction [carrierFreq, confidence] = estimateByCyclostationarity(signal, samplingRate)% 通过循环平稳特征估计载频% 计算循环谱alpha = 0:0.01:0.5; % 循环频率范围cyclicSpectrum = zeros(length(alpha), 1024);for i = 1:length(alpha)% 简化的循环谱计算shiftedSignal = signal .* exp(-1j * 2 * pi * alpha(i) * (0:length(signal)-1));[psd, ~] = pwelch(shiftedSignal, hamming(256), 128, 256, samplingRate);cyclicSpectrum(i, :) = psd(1:1024);end% 寻找循环谱中的特征featureMap = sum(cyclicSpectrum, 2);[~, maxIdx] = max(featureMap);% 估计载频（基于循环频率）carrierFreq = alpha(maxIdx) * samplingRate;% 置信度基于特征突出程度sortedFeatures = sort(featureMap, 'descend');if length(sortedFeatures) > 1confidence = (sortedFeatures(1) - sortedFeatures(2)) / sortedFeatures(1);elseconfidence = 0.6;endconfidence = min(0.9, confidence);
endfunction [carrierFreq, confidence] = estimateByHigherOrderStats(signal, samplingRate)% 通过高阶统计量估计载频% 计算四阶累积量（对载频敏感）signalReal = real(signal);signalImag = imag(signal);% 简化的高阶统计量分析kurtosisReal = kurtosis(signalReal);kurtosisImag = kurtosis(signalImag);% 基于峰度的频率估计（启发式方法）freqShift = (kurtosisReal - 3) * 1e6; % 缩放因子% 初始频率估计[psd, f] = pwelch(signal, hamming(512), 256, 512, samplingRate);[~, initialFreqIdx] = max(psd);initialFreq = f(initialFreqIdx);carrierFreq = initialFreq + freqShift;% 置信度基于峰度值confidence = min(0.8, abs(kurtosisReal - 3) / 10);
endfunction fusedFreq = fuseEstimations(freq1, freq2, freq3, conf1, conf2, conf3)% 融合多个估计结果estimations = [freq1, freq2, freq3];confidences = [conf1, conf2, conf3];% 归一化置信度权重totalConfidence = sum(confidences);if totalConfidence == 0weights = [1/3, 1/3, 1/3];elseweights = confidences / totalConfidence;end% 加权平均fusedFreq = sum(estimations .* weights);% 如果估计值差异太大，选择最可信的stdEstimations = std(estimations);if stdEstimations > 0.1 * mean(estimations) % 差异超过10%[~, bestIdx] = max(confidences);fusedFreq = estimations(bestIdx);end
end
码速率估计
function [codeRate, info] = estimateCodeRate(signal, params)% 估计码速率fprintf('估计码速率...\n');info = struct();samplingRate = params.true.samplingRate;% 方法1: 自相关函数分析[codeRate1, confidence1] = estimateByAutocorrelation(signal, samplingRate);% 方法2: 小波变换分析[codeRate2, confidence2] = estimateByWavelet(signal, samplingRate);% 方法3: 循环自相关[codeRate3, confidence3] = estimateByCyclicAutocorrelation(signal, samplingRate);% 融合估计结果codeRate = fuseCodeRateEstimations(codeRate1, codeRate2, codeRate3, ...confidence1, confidence2, confidence3);info.methods.autocorr = codeRate1;info.methods.wavelet = codeRate2;info.methods.cyclic = codeRate3;info.confidence = [confidence1, confidence2, confidence3];info.finalEstimation = codeRate;% 误差分析if isfield(params.true, 'codeRate')trueRate = params.true.codeRate;error = abs(codeRate - trueRate);relativeError = error / trueRate * 100;info.trueValue = trueRate;info.absoluteError = error;info.relativeError = relativeError;fprintf(' 真实码速率: %.3f MHz\n', trueRate/1e6);fprintf(' 估计码速率: %.3f MHz\n', codeRate/1e6);fprintf(' 绝对误差: %.3f kHz\n', error/1e3);fprintf(' 相对误差: %.3f%%\n', relativeError);elsefprintf(' 估计码速率: %.3f MHz\n', codeRate/1e6);endfprintf('码速率估计完成\n');
endfunction [codeRate, confidence] = estimateByAutocorrelation(signal, samplingRate)% 通过自相关函数估计码速率% 计算自相关函数autocorr = xcorr(real(signal), 'coeff');autocorr = autocorr(length(signal):end); % 取正时延部分% 寻找周期性峰值[peaks, peakLocs] = findpeaks(autocorr, 'MinPeakHeight', 0.1, 'MinPeakDistance', 10);if length(peakLocs) < 2codeRate = samplingRate / 100; % 默认值confidence = 0.3;return;end% 计算峰值间隔（码片周期）peakIntervals = diff(peakLocs);chipPeriod = median(peakIntervals);codeRate = samplingRate / chipPeriod;% 置信度基于峰值规律性intervalStd = std(peakIntervals);confidence = max(0, 1 - intervalStd / chipPeriod);% 可视化if falsefigure;plot((0:length(autocorr)-1)/samplingRate*1e6, autocorr);xlabel('时延 (μs)');ylabel('自相关系数');title('自相关函数分析');grid on;hold on;plot(peakLocs/samplingRate*1e6, peaks, 'ro');legend('自相关函数', '峰值');end
endfunction [codeRate, confidence] = estimateByWavelet(signal, samplingRate)% 通过小波变换估计码速率% 使用小波变换检测码片边界signalReal = real(signal);% 选择合适的小波基[c, l] = wavedec(signalReal, 5, 'db4');% 分析细节系数detailCoeffs = detcoef(c, l, 3); % 选择适当的分解层数% 检测细节系数中的跳变点（码片边界）threshold = 3 * median(abs(detailCoeffs)) / 0.6745;significantPoints = find(abs(detailCoeffs) > threshold);if length(significantPoints) < 2codeRate = samplingRate / 100;confidence = 0.3;return;end% 计算跳变点间隔intervals = diff(significantPoints);chipSamples = median(intervals);% 考虑小波分解的尺度scaleFactor = 2^3; % 第3层分解actualChipSamples = chipSamples * scaleFactor;codeRate = samplingRate / actualChipSamples;% 置信度基于跳变点的规律性confidence = max(0, 1 - std(intervals) / median(intervals));
endfunction [codeRate, confidence] = estimateByCyclicAutocorrelation(signal, samplingRate)% 通过循环自相关估计码速率signalReal = real(signal);N = length(signalReal);% 计算循环自相关maxLag = min(1000, round(N/10));cyclicCorr = zeros(1, maxLag);for tau = 1:maxLagif tau < Nproduct = signalReal(1:end-tau) .* signalReal(tau+1:end);cyclicCorr(tau) = mean(product);endend% 寻找周期性[peaks, peakLocs] = findpeaks(cyclicCorr, 'MinPeakHeight', 0.05*max(cyclicCorr));if length(peakLocs) < 2codeRate = samplingRate / 100;confidence = 0.3;return;end% 估计码片周期fundamentalPeriod = findFundamentalPeriod(peakLocs);codeRate = samplingRate / fundamentalPeriod;% 置信度confidence = min(0.8, length(peaks) / 10);
endfunction period = findFundamentalPeriod(peakLocs)% 寻找基本周期if length(peakLocs) < 2period = peakLocs(1);return;end% 计算所有峰值间隔intervals = diff(peakLocs);% 使用直方图找到最常见的间隔[counts, edges] = histcounts(intervals, 'BinMethod', 'integers');[~, maxIdx] = max(counts);period = round(mean([edges(maxIdx), edges(maxIdx+1)]));
endfunction fusedRate = fuseCodeRateEstimations(rate1, rate2, rate3, conf1, conf2, conf3)% 融合码速率估计结果estimations = [rate1, rate2, rate3];confidences = [conf1, conf2, conf3];% 去除异常值validMask = confidences > 0.2;if sum(validMask) == 0fusedRate = mean(estimations);return;endvalidEstimations = estimations(validMask);validConfidences = confidences(validMask);% 加权平均totalConfidence = sum(validConfidences);weights = validConfidences / totalConfidence;fusedRate = sum(validEstimations .* weights);
end
扩频增益估计
function [spreadingGain, info] = estimateSpreadingGain(signal, codeRate, params)% 估计扩频增益fprintf('估计扩频增益...\n');info = struct();samplingRate = params.true.samplingRate;% 方法1: 自相关函数分析[gain1, confidence1] = estimateGainByAutocorrelation(signal, codeRate, samplingRate);% 方法2: 功率谱分析[gain2, confidence2] = estimateGainByPowerSpectrum(signal, codeRate, samplingRate);% 方法3: 统计特性分析[gain3, confidence3] = estimateGainByStatistics(signal, codeRate, samplingRate);% 融合估计结果spreadingGain = fuseGainEstimations(gain1, gain2, gain3, confidence1, confidence2, confidence3);info.methods.autocorr = gain1;info.methods.spectrum = gain2;info.methods.stats = gain3;info.confidence = [confidence1, confidence2, confidence3];info.finalEstimation = spreadingGain;% 误差分析if isfield(params.true, 'spreadingGain')trueGain = params.true.spreadingGain;error = abs(spreadingGain - trueGain);relativeError = error / trueGain * 100;info.trueValue = trueGain;info.absoluteError = error;info.relativeError = relativeError;fprintf(' 真实扩频增益: %d\n', trueGain);fprintf(' 估计扩频增益: %d\n', spreadingGain);fprintf(' 绝对误差: %d\n', error);fprintf(' 相对误差: %.3f%%\n', relativeError);elsefprintf(' 估计扩频增益: %d\n', spreadingGain);endfprintf('扩频增益估计完成\n');
endfunction [gain, confidence] = estimateGainByAutocorrelation(signal, codeRate, samplingRate)% 通过自相关函数估计扩频增益% 计算自相关函数autocorr = xcorr(real(signal), 'coeff');autocorr = autocorr(length(signal):end);% 寻找周期性模式[peaks, peakLocs] = findpeaks(autocorr, 'MinPeakHeight', 0.05);if length(peakLocs) < 3gain = 63; % 默认值confidence = 0.3;return;end% 分析峰值间隔模式intervals = diff(peakLocs);chipSamples = round(median(intervals));% 估计信息符号周期（寻找更大的周期）symbolIntervals = findSymbolPeriod(autocorr, chipSamples);if isempty(symbolIntervals)gain = 63;confidence = 0.3;return;endsymbolSamples = median(symbolIntervals);gain = round(symbolSamples / chipSamples);% 置信度confidence = min(0.8, length(peaks) / 20);
endfunction symbolIntervals = findSymbolPeriod(autocorr, chipSamples)% 寻找符号周期% 在自相关函数中寻找比码片周期更长的周期searchRange = round(chipSamples * 2):round(chipSamples * 100);symbolCorr = zeros(size(searchRange));for i = 1:length(searchRange)tau = searchRange(i);if tau < length(autocorr)symbolCorr(i) = autocorr(tau);endend% 寻找局部最大值[peaks, locs] = findpeaks(symbolCorr, 'MinPeakHeight', 0.1);if isempty(peaks)symbolIntervals = [];return;endsymbolIntervals = searchRange(locs);
endfunction [gain, confidence] = estimateGainByPowerSpectrum(signal, codeRate, samplingRate)% 通过功率谱估计扩频增益% 计算功率谱[psd, f] = pwelch(real(signal), hamming(512), 256, 512, samplingRate);% 分析频谱特征bandwidth = estimateBandwidth(psd, f);% 估计处理增益estimatedGain = round(bandwidth / codeRate);% 常见的扩频增益值commonGains = [7, 15, 31, 63, 127, 255, 511, 1023];% 选择最接近的常见值[~, closestIdx] = min(abs(estimatedGain - commonGains));gain = commonGains(closestIdx);% 置信度confidence = 0.6;
endfunction bandwidth = estimateBandwidth(psd, f)% 估计信号带宽maxPSD = max(psd);threshold = maxPSD / 2; % -3dB带宽aboveThreshold = psd > threshold;if ~any(aboveThreshold)bandwidth = f(end) - f(1);return;endstartIdx = find(aboveThreshold, 1, 'first');endIdx = find(aboveThreshold, 1, 'last');bandwidth = f(endIdx) - f(startIdx);
endfunction [gain, confidence] = estimateGainByStatistics(signal, codeRate, samplingRate)% 通过统计特性估计扩频增益signalReal = real(signal);% 计算信号的峰度（对扩频信号有特征性）kurt = kurtosis(signalReal);% 估计码片数（启发式方法）estimatedChips = round(1000 / (kurt - 1)); % 经验公式% 映射到常见的扩频增益commonGains = [7, 15, 31, 63, 127, 255, 511, 1023];[~, closestIdx] = min(abs(estimatedChips - commonGains));gain = commonGains(closestIdx);% 置信度基于峰度值confidence = min(0.7, abs(kurt - 1.5) / 5);
endfunction fusedGain = fuseGainEstimations(gain1, gain2, gain3, conf1, conf2, conf3)% 融合扩频增益估计结果gains = [gain1, gain2, gain3];confidences = [conf1, conf2, conf3];% 选择最可信的估计[maxConf, bestIdx] = max(confidences);if maxConf > 0.5fusedGain = gains(bestIdx);else% 使用中位数fusedGain = median(gains);end% 取整到最接近的2^n-1值possibleGains = 2.^(3:10) - 1;[~, closestIdx] = min(abs(fusedGain - possibleGains));fusedGain = possibleGains(closestIdx);
end
参数验证和结果显示
function [validationResults, confidence] = validateEstimations(carrierFreq, codeRate, spreadingGain, params)% 参数验证和精度分析fprintf('参数验证和精度分析...\n');validationResults = struct();confidenceScores = zeros(1, 3);% 载频验证if isfield(params.true, 'carrierFreq')trueFreq = params.true.carrierFreq;freqError = abs(carrierFreq - trueFreq);freqAccuracy = max(0, 1 - freqError / trueFreq);confidenceScores(1) = freqAccuracy;validationResults.carrier.trueValue = trueFreq;validationResults.carrier.estimatedValue = carrierFreq;validationResults.carrier.error = freqError;validationResults.carrier.accuracy = freqAccuracy;elseconfidenceScores(1) = 0.7; % 默认置信度end% 码速率验证if isfield(params.true, 'codeRate')trueRate = params.true.codeRate;rateError = abs(codeRate - trueRate);rateAccuracy = max(0, 1 - rateError / trueRate);confidenceScores(2) = rateAccuracy;validationResults.codeRate.trueValue = trueRate;validationResults.codeRate.estimatedValue = codeRate;validationResults.codeRate.error = rateError;validationResults.codeRate.accuracy = rateAccuracy;elseconfidenceScores(2) = 0.7;end% 扩频增益验证if isfield(params.true, 'spreadingGain')trueGain = params.true.spreadingGain;gainError = abs(spreadingGain - trueGain);gainAccuracy = max(0, 1 - gainError / trueGain);confidenceScores(3) = gainAccuracy;validationResults.spreadingGain.trueValue = trueGain;validationResults.spreadingGain.estimatedValue = spreadingGain;validationResults.spreadingGain.error = gainError;validationResults.spreadingGain.accuracy = gainAccuracy;elseconfidenceScores(3) = 0.7;end% 总体置信度confidence = mean(confidenceScores);fprintf('参数验证完成 - 总体置信度: %.2f\n', confidence);
endfunction displayResults(carrierFreq, codeRate, spreadingGain, validationResults, confidence, params)% 显示估计结果fprintf('\n=== 直扩信号参数估计结果 ===\n');fprintf('载波频率: %.6f GHz\n', carrierFreq/1e9);fprintf('码速率: %.3f MHz\n', codeRate/1e6);fprintf('扩频增益: %d\n', spreadingGain);fprintf('总体估计置信度: %.2f\n', confidence);if isfield(validationResults, 'carrier')fprintf('\n--- 精度分析 ---\n');fprintf('载频误差: %.3f kHz\n', validationResults.carrier.error/1e3);fprintf('码速率误差: %.3f kHz\n', validationResults.codeRate.error/1e3);fprintf('扩频增益误差: %d\n', validationResults.spreadingGain.error);end% 可视化结果visualizeResults(carrierFreq, codeRate, spreadingGain, validationResults, confidence, params);
endfunction visualizeResults(carrierFreq, codeRate, spreadingGain, validationResults, confidence, params)% 可视化估计结果figure('Position', [100, 100, 1400, 900]);% 1. 参数估计结果对比subplot(2,3,1);if isfield(validationResults, 'carrier')trueValues = [validationResults.carrier.trueValue/1e9, ...validationResults.codeRate.trueValue/1e6, ...validationResults.spreadingGain.trueValue];estimatedValues = [carrierFreq/1e9, codeRate/1e6, spreadingGain];bar([trueValues; estimatedValues]');set(gca, 'XTickLabel', {'载频 (GHz)', '码速率 (MHz)', '扩频增益'});legend('真实值', '估计值', 'Location', 'best');title('参数估计对比');grid on;elseparameters = [carrierFreq/1e9, codeRate/1e6, spreadingGain];bar(parameters);set(gca, 'XTickLabel', {'载频 (GHz)', '码速率 (MHz)', '扩频增益'});title('参数估计结果');grid on;end% 2. 置信度显示subplot(2,3,2);confidenceValues = [confidence, 1-confidence];pie(confidenceValues, {'可信', '不确定'});title(sprintf('总体置信度: %.1f%%', confidence*100));% 3. 误差分析（如果有真实值）if isfield(validationResults, 'carrier')subplot(2,3,3);errors = [validationResults.carrier.error/validationResults.carrier.trueValue*100, ...validationResults.codeRate.error/validationResults.codeRate.trueValue*100, ...validationResults.spreadingGain.error/validationResults.spreadingGain.trueValue*100];bar(errors);ylabel('相对误差 (%)');set(gca, 'XTickLabel', {'载频', '码速率', '扩频增益'});title('参数估计相对误差');grid on;end% 4. 信号时域波形subplot(2,3,4);if isfield(params, 'testSignal')signal = params.testSignal;t = (0:length(signal)-1) / params.true.samplingRate * 1e6;plot(t, real(signal(1:min(10000, end))));xlabel('时间 (μs)');ylabel('幅度');title('接收信号时域波形');grid on;end% 5. 功率谱密度subplot(2,3,5);if isfield(params, 'testSignal')[psd, f] = pwelch(params.testSignal, hamming(1024), 512, 1024, params.true.samplingRate);plot(f/1e6, 10*log10(psd));xlabel('频率 (MHz)');ylabel('功率谱密度 (dB/Hz)');title('信号功率谱密度');grid on;hold on;plot(carrierFreq/1e6, max(10*log10(psd)), 'ro', 'MarkerSize', 8);legend('PSD', '估计载频');end% 6. 自相关函数subplot(2,3,6);if isfield(params, 'testSignal')autocorr = xcorr(real(params.testSignal), 'coeff');autocorr = autocorr(length(params.testSignal):end);tau = (0:length(autocorr)-1) / params.true.samplingRate * 1e6;plot(tau(1:min(1000, end)), autocorr(1:min(1000, end)));xlabel('时延 (μs)');ylabel('自相关系数');title('信号自相关函数');grid on;endsgtitle('直扩信号参数估计结果分析', 'FontSize', 14, 'FontWeight', 'bold');
endfunction analyzePerformance(carrierFreq, codeRate, spreadingGain, params)% 性能分析fprintf('\n=== 系统性能分析 ===\n');% 计算处理增益processingGain = spreadingGain;theoreticalGainDB = 10 * log10(processingGain);fprintf('处理增益: %.1f dB\n', theoreticalGainDB);% 抗干扰能力分析jammingMargin = theoreticalGainDB - 10; % 假设需要10dB信噪比fprintf('理论抗干扰容限: %.1f dB\n', jammingMargin);% 多址容量估计if spreadingGain > 0maxUsers = floor(spreadingGain / 10); % 简化的容量估计fprintf('估计多址容量: %d 用户\n', maxUsers);end% 建议的应用场景fprintf('\n--- 建议应用场景 ---\n');if codeRate > 50e6fprintf('高速数据传输系统\n');elseif spreadingGain > 100fprintf('高抗干扰军事通信\n');elsefprintf('常规扩频通信\n');endif carrierFreq > 1e9fprintf('微波频段通信系统\n');elsefprintf('低频段通信系统\n');end
end
参考代码 直扩信号盲识别 www.youwenfan.com/contentcnk/65594.html
系统特点
1. 完整参数估计流程
- 载频估计: 功率谱分析、循环平稳特征、高阶统计量
- 码速率估计: 自相关分析、小波变换、循环自相关
- 扩频增益估计: 周期分析、频谱分析、统计特性
2. 多方法融合
- 多种估计算法并行运行
- 置信度加权融合
- 异常值检测和处理
3. 性能评估
- 精度分析和误差统计
- 置信度评估
- 应用场景建议
4. 可视化分析
- 参数对比显示
- 信号特征可视化
- 误差分布分析
