✅作者简介热爱科研的Matlab仿真开发者擅长毕业设计辅导、数学建模、数据处理、算法改进、程序设计科研仿真。完整代码获取 定制创新 论文复现私信个人信条做科研博学之、审问之、慎思之、明辨之、笃行之是为博学慎思明辨笃行。1. 相关介绍一、项目整体概述本代码是学术论文《UAV-Based 3D Spectrum Sensing: Insights on Altitude, Bandwidth, Trajectory, and Effective Antenna Patterns on REM Reconstruction》的配套代码实现核心研究方向是‌系统评估物理感知属性、局域环境特征、无人机专属硬件效应对无线电环境地图REM重建精度的内在影响‌所有代码可以帮助用户完整复现论文中描述的全部实验自主探索不同飞行条件下的REM重建效果。二、研究核心目标项目的核心任务是基于无人机采集的稀疏信号强度采样数据反演构建高精度的密集无线电环境地图。研究重点聚焦于REM重建精度对各类关键参数的敏感性分析覆盖从地面感知高度、频谱带宽、相对于信号源的仰角等物理感知参数到无人机机身结构带来的专属硬件效应的全维度研究。研究的两大核心目标为在不同飞行动力学条件下对各类主流空间预测模型进行基准性能测试明确不同飞行场景下REM重建任务的固有难度边界通过主动学习补偿无人机机身带来的硬件干扰效应以及局域深度阴影等复杂环境特征针对性提升REM的重建精度三、四大核心研究方向本研究的所有实验围绕四个明确的核心问题展开无人机飞行高度、相对于信号源的仰角如何影响REM重建精度感知所用的频谱带宽如何作用于REM重建精度通过学习并建模无人机机身的结构电磁效应是否能够有效提升REM重建精度通过显式提取无线电地图内的深度阴影区域并定向传播该类特征是否能够进一步提升重建精度四、核心方案与亮点成果1. 技术方案框架本研究将主流基准算法和自研增强方案做了完整的对照测试‌基准对比算法‌双径路径损耗TRPL建模、三类Kriging插值算法简单Kriging、普通Kriging、跨高斯Kriging变体、高斯过程回归GPR‌自研增强方案‌专门针对深度阴影区域提取的新型矩阵补全MC辅助GPR框架以及面向实际飞行场景的机上天线方向图校准技术2. 实测数据集支撑所有实验完全基于真实外场采集的无人机实测数据数据集全部来自AERPAW公开项目的三个子数据集AERPAW LTE I/Q数据集、AERPAW Find-a-RoverAFAR数据集、AERPAW多频段数据集完全避免了仿真数据与实际场景偏差过大的问题。3. 四大核心研究结论研究通过系统性的对照测试得到了四项明确的创新性成果‌飞行几何特性洞察‌REM重建精度随无人机高度变化呈现清晰的三阶段变化趋势而阴影衰落的方差随仰角变化呈现非单调特性在极低仰角和中高仰角两个区间分别达到峰值‌带宽影响规律‌随着频谱带宽提升频率分集效应显著增强有效抵消了多径衰落的负面影响REM重建精度呈现单调提升的变化趋势‌深度阴影提取效果‌自研的矩阵补全辅助GPR框架通过对REM进行分层分解可以精准分离局域极端信号极值区域并定向传播该特征完全不会出现传统算法的过度平滑问题大幅提升深度阴影区域的重建精度‌天线校准增益‌直接基于外场实测数据校准得到的有效天线辐射方向图可以精准抵消无人机机身带来的电磁干扰效应进一步显著提升全场景下的REM重建精度2. 运行效果展示3. 部分代码呈现clc;clear;close all;addpath(functions\)addpath(functions\Inpaint_nans\)%load(filtererd_data_with_dist_ori_ num2str(30) _meas_rad .mat)%% this is for rx antennaaltitudes [110]; % change this and the next line as well {30, 50, 70, 90, 110}altitude_string num2str(altitudes(1))m;% change _smoothing in ant_place_effect_sung.m as well ALLLEEERT!!!!!!!!!is_smooth _smooth; % _smooth or % smooth for Krigingalpha 1.0;unknown_angle 0;mat_name patts/flipx_antenna_tx_rx_updated_sung_ altitude_string is_smooth .mat; % for Krigingprediction_err zeros(1,0);% with alpha 1.0, i_iter1 is enoughiter_end_at 1;for i_iter 1:iter_end_at1 %25% update ant patt for rxall_az [];all_el [];all_shadow [];for ii1:length(altitudes)altitude altitudes(ii);load(../data_gen/LTE_dataset/processed_data/filtererd_data_with_dist_ori_ num2str(altitude) _meas_rad .mat)if i_iter1[sha_two_s, sha_free_s] update_PL_sung_new_rad_pat(sha_two_s, sha_free_s, azim_s, elev_s, ori_s, mat_name);end% -(phi_t uav_yaw 180)% ori_s is the direction following trajectoryori_s ori_s - pi/2;ori_s ori_s (ori_s-pi).*pi*2;azim_wrt_uav_ori -azim_s - ori_s*pi/180;azim_wrt_uav_ori azim_wrt_uav_ori - (azim_wrt_uav_ori0).*pi (azim_wrt_uav_ori0).*pi;all_az [all_az; azim_wrt_uav_ori];all_el [all_el; elev_s];all_shadow [all_shadow; sha_free_s];endfig figure(40);fig.Position [20 500 * 2, 200, 500, 500];histogram(all_shadow);% Add labels and titlexlabel(Shadow Fading [dB]);ylabel(Frequency);title(Histogram of residuals)xlim([-40, 40])prediction_err(1,i_iter) sqrt(mean(all_shadow.^2)); % rmse%pause(1.0)if i_iteriter_end_atbreakend% for i_iter1, prev_patt and G_r_dB are same[Z, prev_patt, G_r_dB] ant_place_effect_sung2(all_az,all_el,all_shadow*alpha,1,1,i_iter,101*(i_iter2),102,1,1,mat_name);endfig figure(70);%fig.Position [20 500 * 2, 200, 500, 500];plot(1:length(prediction_err),prediction_err,-o);% Add labels and titlexlabel(Iteration Number);ylabel(RMSE of Prediction (dB));% title(Histogram of residuals)% xlim([-40, 40])%% show rad pat before and after attachment (Azimuth)% condition: iter_number must be 1, otherwise prev_patt will reflect% intermidiate patt, Z also will be delta% use [Z, prev_patt]offset 50;figure;placement_effect zeros(61,120)*NaN;placement_effect(1:31,:) Z;phi_grid (180:-3:-177)*pi/180;theta_grid (90:-3:0)*pi/180;theta_grid_full (90:-3:-90)*pi/180;% if altitude30% elMax 10;% elseif altitude70% elMax 20;% else% elMax 30;% endelMax 30;theta_filter (theta_grid_full0*pi/180) (theta_grid_fullelMax*pi/180); % 10 for 40m, 20 for 70m, 30 for 100mprev_patt_subset prev_patt(theta_filter,:);orig_patt_subset G_r_dB(theta_filter,:);placement_effect_subset placement_effect(theta_filter,:);phi_filter (phi_grid-145*pi/180) (phi_grid50*pi/180);% make this adaptivephi_filter ~isnan(nanmean(placement_effect_subset,1));polarplot(phi_grid,max(mean(orig_patt_subsetoffset,1),0),--, DisplayName, Anechoic Chamber, LineWidth,2)hold on;polarplot(phi_grid(phi_filter),max(nanmean(prev_patt_subset(:,phi_filter)offsetplacement_effect_subset(:,phi_filter),1),0), DisplayName, UAV-to-ground environment, LineWidth,2)hold on;set(gcf,color,w);rlim([0 60])%rticks([-35:10:5])legend(Location, best); % Adjust the label location as needed% Increase figure text sizeset(gca, FontSize, 14); % Set the axis labels font size%% show rad pat before and after attachment (Elevation)% condition: iter_number must be 1, otherwise prev_patt will reflect% intermidiate patt, Z also will be delta% use [Z, prev_patt]offset 50;figure;placement_effect zeros(61,120)*NaN;placement_effect(1:31,:) Z;phi_grid (180:-3:-177)*pi/180;theta_grid (90:-3:0)*pi/180;theta_grid_full (90:-3:-90)*pi/180;% if altitude40m% elMax 10;% elseif altitude70m% elMax 20;% else% elMax 30;% end%azim_filter phi_grid?prev_patt_subset prev_patt;orig_patt_subset G_r_dB;placement_effect_subset placement_effect;% make this adaptivetheta_filter ~isnan(nanmean(placement_effect_subset,2));polarplot(theta_grid_full,max(mean(orig_patt_subsetoffset,2),0),--, DisplayName, Anechoic Chamber, LineWidth,2)hold on;polarplot(theta_grid_full(theta_filter),max(nanmean(prev_patt_subset(theta_filter,:)offsetplacement_effect_subset(theta_filter,:),2),0), DisplayName, UAV-to-ground environment, LineWidth,2)hold on;set(gcf,color,w);rlim([0 70])%rticks([-35:10:5])legend(Location, best); % Adjust the label location as needed% Increase figure text sizeset(gca, FontSize, 14); % Set the axis labels font size4. 参考文献[1] Rahman M , Maeng S J , Guvenc I ,et al.UAV-Based 3D Spectrum Sensing: Insights on Altitude, Bandwidth, Trajectory, and Effective Antenna Patterns on REM Reconstruction[J]. 2026.DOI:10.1109/JSEN.2026.3696969.更多免费数学建模和仿真教程关注领取如果觉得内容不错那就请分享和点个“在看”呗