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Now using a truly normalized cross correlation

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David Protzman 2022-04-30 11:57:09 -04:00
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commit b442f73baf
1 zmienionych plików z 39 dodań i 22 usunięć
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matlab/updated_scripts/find_zc_indices_by_file.m
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@ -1,15 +1,6 @@
% Find all instances of the first ZC sequence in the provided file. Does not have to read the entire file in at once
%
% Uses a cross correlator to search for the first ZC sequence in DroneID.
%
% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
% !!! There is currently a bug in this script! The correlation results are NOT normalized and might require the !!!
% !!! the user to greatly increase or decrease the correlation threshold past the expected range of 0.0 - 1.0 !!!
% !!! In the code below there is a plotting function that is disabled by default. Enabling it will plot the !!!
% !!! correlation results so that you can better set the correlation treshold. !!!
% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
% Uses a normalized cross correlator to search for the first ZC sequence in DroneID.
%
% Varags:
% - SampleType: MATLAB numeric type that the samples in the provided file are stored as (ex: 'single', 'int16', etc)
@ -70,10 +61,9 @@ function [zc_indices] = find_zc_indices_by_file(file_path, sample_rate, frequenc
% Pre-calculate the frequency offset rotation
freq_offset_constant = 1j * pi * 2 * (frequency_offset / sample_rate);
% The output of the ZC sequence generator needs to be conjugated to be used in a filter. Also create a variable that
% can hold the filter state between chunks to prevent discontinuities
correlator_taps = conj(create_zc(fft_size, 4));
correlator_state = [];
% The correlator will be searching for the first ZC sequence (which resides in the 4th/3rd OFDM symbol depending on
% which drone model is in use.) No need to conjugate here as that will be done in the correlator
correlator_taps = create_zc(fft_size, 4);
% Figure out how many samples there are in the file
total_samples = get_sample_count_of_file(file_path, sample_type);
@ -84,28 +74,55 @@ function [zc_indices] = find_zc_indices_by_file(file_path, sample_rate, frequenc
% Really large array to store the cross correlation results from *all* samples
zc_scores = zeros(total_samples - length(correlator_taps), 1);
% Default to no leftover samples for the first iteration
leftover_samples = [];
sample_offset = 0;
zc_scores_ptr = 1;
while (~ feof(file_handle))
%% Read in the next buffer
% The `fread` command will return interleaved real, imag values, so pack those into complex samples making sure
% that the resulting complex values are double precision (this is to prevent functions from complaining later)
real_values = fread(file_handle, chunk_size * 2, sample_type);
samples = double(real_values(1:2:end) + 1j * real_values(2:2:end));
real_values = double(fread(file_handle, chunk_size * 2, sample_type));
% Don't continue processing if there aren't enough samples remaining keeping in mind that there might be some
% samples leftover from the last iteration of the loop
if ((length(real_values) / 2) + leftover_samples <= length(correlator_taps))
break;
end
% Convert from a vector of reals (I,Q,I,Q,I,Q,...) to complex
samples = real_values(1:2:end) + 1j * real_values(2:2:end);
%% Frequency shift the input
% This is somewhat optional, but the correlation scores will go down fast if the offset is > 1 MHz
rotation_vector = exp(freq_offset_constant * (sample_offset:(sample_offset+length(samples)-1)));
samples = samples .* reshape(rotation_vector, [], 1);
%% Handle unprocessed samples from last iteration
% The correlation function cannot process all samples as it looks forward in time. So, after each iteration of
% this loop some samples don't get processed, and those samples are added to the `leftover_samples` vector.
% Those samples need to be added to the beginning of the vector of samples that were read from the file.
% Additionally, the leftover samples have already been frequency corrected, so they must be ignored when
% applying frequency correction (done above).
% TODO(30April2022): This isn't ideal as resizing the sample vector each time is not good for performance
samples = [leftover_samples; samples];
% The end of the current sample vector will be the new leftover samples for the next iteration
leftover_samples = samples(end - length(correlator_taps) + 1:end);
%% Correlate for the ZC sequence
% Use a FIR filter to search for the ZC sequence. THIS WILL NOT NORMALIZE!!!
% TODO(9April2022): Would be nice to use the fftfilt function, but I don't know if it has issues with keeping state
% as it doesn't have a state input like the filter command. In testing the FFT filter is twice
% as fast
[correlation_values, correlator_state] = filter(correlator_taps, 1, samples, correlator_state);
zc_scores(sample_offset+1:sample_offset+length(correlation_values)) = correlation_values;
% Run a normalized cross correlation
correlation_values = normalized_xcorr_fast(samples, correlator_taps);
% Not all samples in the `samples` vector were processed by the correlator, so only update the samples that were
% processed by using the `zc_scores_ptr` which points to where in the `zc_scores` the next values should go.
zc_scores(zc_scores_ptr:zc_scores_ptr+length(correlation_values)-1) = correlation_values;
zc_scores_ptr = zc_scores_ptr + length(correlation_values);
% Move the sample counter forward so that the frequency offset adjustment logic works properly
sample_offset = sample_offset + length(samples);
end