Этот пример показывает, как сгенерировать HDL-код из проекта MATLAB®, который реализует фильтр LMS. Это также показывает, как разработать испытательный стенд, который реализует подавление помех с помощью этого фильтра.
Алгоритм вычисляет отфильтрованный вывод, ошибку и веса фильтра для данного входа и желал сигнала с помощью алгоритма Наименьшее количество средних квадратичных (LMS). Фильтр LMS затем используется, чтобы идентифицировать КИХ-сигнал, встроенный в шум.
design_name = 'mlhdlc_lms_fcn'; testbench_name = 'mlhdlc_lms_noise_canceler_tb';
Давайте смотреть на проект MATLAB
type(design_name);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % MATLAB Design: Adaptive Noise Canceler algorithm using Least Mean Square % (LMS) filter implemented in MATLAB % % Key Design pattern covered in this example: % (1) Use of function calls % (2) Function inlining vs instantiation knobs available in the coder % (3) Use of system objects in the testbench to stream test vectors into the design %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %#codegen function [filtered_signal, y, fc] = mlhdlc_lms_fcn(input, desired, step_size, reset_weights) % % 'input' : The signal from Exterior Mic which records the ambient noise. % 'desired': The signal from Pilot's Mic which includes % original music signal and the noise signal % 'err_sig': The difference between the 'desired' and the filtered 'input' % It represents the estimated music signal (output of this block) % % The LMS filter is trying to retrieve the original music signal ('err_sig') % from Pilot's Mic by filtering the Exterior Mic's signal and using it to % cancel the noise in Pilot's Mic. The coefficients/weights of the filter % are updated(adapted) in real-time based on 'input' and 'err_sig'. % register filter coefficients persistent filter_coeff; if isempty(filter_coeff) filter_coeff = zeros(1, 40); end % Variable Filter % call 'tapped_delay_fcn' function on path to create 40-step tapped delay delayed_signal = mtapped_delay_fcn(input); % apply filter coefficients weight_applied = delayed_signal .* filter_coeff; % call 'treesum' function on matlab path to sum up the results filtered_signal = mtreesum_fcn(weight_applied); % Output estimated Original Signal td = desired; tf = filtered_signal; esig = td - tf; y = esig; % Update Weights % call 'update_weight_fcn' function on matlab path to % calculate the new weights updated_weight = update_weight_fcn(step_size, esig, delayed_signal, ... filter_coeff, reset_weights); % update filter coefficients register filter_coeff = updated_weight; fc = filter_coeff; function y = mtreesum_fcn(u) %Implement the 'sum' function without a for-loop % y = sum(u); % The loop based implementation of 'sum' function is not ideal for % HDL generation and results in a longer critical path. % A tree is more efficient as it results in % delay of log2(N) instead of a delay of N delay % This implementation shows how to explicitly implement the vector sum in % a tree shape to enable hardware optimizations. % The ideal way to code this generically for any length of 'u' is to use % recursion but it is not currently supported by MATLAB Coder % NOTE: To instruct MATLAB Coder to compile an external function, % add the following compilation directive or pragma to the function code %#codegen % This implementation is hardwired for a 40tap filter. level1 = vsum(u); level2 = vsum(level1); level3 = vsum(level2); level4 = vsum(level3); level5 = vsum(level4); level6 = vsum(level5); y = level6; function output = vsum(input) coder.inline('always'); vt = input(1:2:end); for i = int32(1:numel(input)/2) k = int32(i*2); vt(i) = vt(i) + input(k); end output = vt; function tap_delay = mtapped_delay_fcn(input) % The Tapped Delay function delays its input by the specified number % of sample periods, and outputs all the delayed versions in a vector % form. The output includes current input % NOTE: To instruct MATLAB Coder to compile an external function, % add the following compilation directive or pragma to the function code %#codegen persistent u_d; if isempty(u_d) u_d = zeros(1,40); end u_d = [u_d(2:40), input]; tap_delay = u_d; function weights = update_weight_fcn(step_size, err_sig, delayed_signal, filter_coeff, reset_weights) % This function updates the adaptive filter weights based on LMS algorithm % Copyright 2007-2015 The MathWorks, Inc. % NOTE: To instruct MATLAB Coder to compile an external function, % add the following compilation directive or pragma to the function code %#codegen step_sig = step_size .* err_sig; correction_factor = delayed_signal .* step_sig; updated_weight = correction_factor + filter_coeff; if reset_weights weights = zeros(1,40); else weights = updated_weight; end
type(testbench_name);
% returns an adaptive FIR filter System object, % HLMS, that computes the filtered output, filter error and the filter % weights for a given input and desired signal using the Least Mean % Squares (LMS) algorithm. % Copyright 2011-2015 The MathWorks, Inc. clear('mlhdlc_lms_fcn'); hfilt2 = dsp.FIRFilter(... 'Numerator', fir1(10, [.5, .75])); rng('default'); % always default to known state x = randn(1000,1); % Noise d = step(hfilt2, x) + sin(0:.05:49.95)'; % Noise + Signal stepSize = 0.01; reset_weights =false; hSrc = dsp.SignalSource(x); hDesiredSrc = dsp.SignalSource(d); hOut = dsp.SignalSink; hErr = dsp.SignalSink; %%%%%%%%%%%%%%%%%%%%%%%%%%%% %Call to the design %%%%%%%%%%%%%%%%%%%%%%%%%%%% while (~isDone(hSrc)) [y, e] = mlhdlc_lms_fcn(step(hSrc), step(hDesiredSrc), stepSize, reset_weights); step(hOut, y); step(hErr, e); end figure('Name', [mfilename, '_signal_plot']); subplot(2,1,1), plot(hOut.Buffer), title('Noise + Signal'); subplot(2,1,2),plot(hErr.Buffer), title('Signal');
Выполните следующие строки кода, чтобы скопировать необходимые файлы в качестве примера во временную папку.
mlhdlc_demo_dir = fullfile(matlabroot, 'toolbox', 'hdlcoder', 'hdlcoderdemos', 'matlabhdlcoderdemos'); mlhdlc_temp_dir = [tempdir 'mlhdlc_lms_nc']; % create a temporary folder and copy the MATLAB files cd(tempdir); [~, ~, ~] = rmdir(mlhdlc_temp_dir, 's'); mkdir(mlhdlc_temp_dir); cd(mlhdlc_temp_dir); copyfile(fullfile(mlhdlc_demo_dir, [design_name,'.m*']), mlhdlc_temp_dir); copyfile(fullfile(mlhdlc_demo_dir, [testbench_name,'.m*']), mlhdlc_temp_dir);
Это всегда - хорошая практика, чтобы моделировать проект с испытательным стендом до генерации кода, чтобы убедиться, что нет никаких ошибок периода выполнения.
mlhdlc_lms_noise_canceler_tb
coder -hdlcoder -new mlhdlc_lms_nc
Затем, добавьте файл 'mlhdlc_lms_fcn.m' в проект как функция MATLAB и 'mlhdlc_lms_noise_canceler_tb.m' как Испытательный стенд MATLAB.
Можно отослать к Началу работы с MATLAB к примеру по Рабочему процессу HDL для более полного примера при создании и заполнении MATLAB проекты HDL Coder™.
Запустите Советника по вопросам Рабочего процесса от вкладки Build, и щелчок правой кнопкой по 'Генерации кода' продвигаются и выбирают опцию, 'Запущенную к выбранной задаче', чтобы запустить все шаги с начала через генерацию HDL-кода.
Исследуйте сгенерированный HDL-код путем нажатия на гиперссылки в окне Code Generation Log.
Можно запустить следующие команды, чтобы очистить временную папку проекта.
mlhdlc_demo_dir = fullfile(matlabroot, 'toolbox', 'hdlcoder', 'hdlcoderdemos', 'matlabhdlcoderdemos'); mlhdlc_temp_dir = [tempdir 'mlhdlc_lms_nc']; clear mex; cd (mlhdlc_demo_dir); rmdir(mlhdlc_temp_dir, 's');