kopia lustrzana https://github.com/proto17/dji_droneid
98 wiersze
4.8 KiB
Matlab
98 wiersze
4.8 KiB
Matlab
% This script only works for MATLAB right now. Octave doesn't like something around the filtering section :(
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%% Signal parameters
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% file_path = '/opt/dji/collects/2437MHz_30.72MSPS.fc32';
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% file_sample_rate = 30.72e6; % Collected 2x oversampled
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% rough_frequency_offset = 7.5e6; % The collected signal is 7.5 MHz off center
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% correlation_threshold = 0.7; % Minimum correlation score to accept (0.0 - 1.0)
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function [zc_indices] = find_zc_indices_by_file(file_path, sample_rate, frequency_offset, correlation_threshold, chunk_size)
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%% LTE parameters
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carrier_spacing = 15e3;
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% Pre-calculate the frequency offset rotation
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freq_offset_constant = 1j * pi * 2 * (frequency_offset / sample_rate);
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% The output of the ZC sequence generator needs to be conjugated to be used in a filter. Also create a variable that
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% can hold the filter state between chunks to prevent discontinuities
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correlator_taps = conj(create_zc(sample_rate / carrier_spacing, 4));
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correlator_state = [];
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% Open the IQ recording
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file_handle = fopen(file_path, 'r');
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% Figure out how many samples there are in the file
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fseek(file_handle, 0, 'eof');
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total_samples = ftell(file_handle) / 4 / 2; % 4 bytes per float, 2 floats per complex sample
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fseek(file_handle, 0, 'bof');
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fprintf('There are %d samples in "%s"\n', total_samples, file_path);
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% Really large array to store the cross correlation results from *all* samples
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zc_scores = zeros(total_samples - length(correlator_taps), 1);
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sample_offset = 0;
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while (~ feof(file_handle))
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%% Read in the next buffer
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% The `fread` command will return interleaved real, imag values, so pack those into complex samples
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floats = fread(file_handle, chunk_size * 2, 'float');
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samples = floats(1:2:end) + 1j * floats(2:2:end);
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%% Frequency shift the input
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% This is somewhat optional, but the correlation scores will go down fast if the offset is > 1 MHz
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rotation_vector = exp(freq_offset_constant * (sample_offset:(sample_offset+length(samples)-1)));
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samples = samples .* reshape(rotation_vector, [], 1);
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%% Correlate for the ZC sequence
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% Use a FIR filter to search for the ZC sequence. The resulting values will be normalized to between 0 and 1.0 if
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% the abs^2 is taken
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% TODO(9April2022): Would be nice to use the fftfilt function, but I don't know if it has issues with keeping state
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% as it doesn't have a state input like the filter command. In testing the FFT filter is twice
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% as fast
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[correlation_values, correlator_state] = filter(correlator_taps, 1, samples, correlator_state);
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zc_scores(sample_offset+1:sample_offset+length(correlation_values)) = correlation_values;
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sample_offset = sample_offset + length(samples);
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end
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% Get the floating normalized correlation results
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abs_scores = abs(zc_scores).^2;
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% figure(1);
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% plot(abs_scores);
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% title('Correlation Scores (normalized)')
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% Find all places where the correlation result meets the specified threshold
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% This is going to find duplicates because there are very likely going to be two points right next to each other that
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% meet the required threshold. This will be dealt with later
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passing_scores = find(abs_scores > correlation_threshold);
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% Look through each element of the `passing_scores` vector (which is just indicies where the correlation threshold was
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% met) and pick just the highest value `search_window` elements around (`search_window/2` to the left and right) of each
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% value. The goal here is to only end up with the best score for the starting point of each burst instead of having
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% multiple starting points for each burst.
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true_peaks = [];
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search_window = 100;
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for idx = 1:length(passing_scores)
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% Calculate how far to the left and right to look for the highest peak
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left_idx = passing_scores(idx) - (search_window / 2);
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right_idx = left_idx + search_window - 1;
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if (left_idx < 1 || right_idx > length(abs_scores))
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warning("Had to abandon searching for burst '%d' as it was too close to the end/beginning of the window", idx);
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continue
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end
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% Get the correlation scores for the samples around the current point
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window = abs_scores(left_idx:right_idx);
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% Find the peak in the window and use that value as the actual peak
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[value, index] = max(window);
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true_peaks = [true_peaks, left_idx + index];
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end
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% There are going to be duplicates in the vector, so just take the unique elements. What's left should just be the
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% actual starting indices for each ZC sequence.
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zc_indices = unique(true_peaks);
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end
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