# KrakenSDR Signal Processor
#
# Copyright (C) 2018-2021 Carl Laufer, Tamás Pető
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#
#
# - coding: utf-8 -*-
# Import built-in modules
import sys
import os
import time
import logging
import threading
import queue
import math
# Import optimization modules
import numba as nb
from numba import jit, njit
from functools import lru_cache
# Math support
import numpy as np
import numpy.linalg as lin
#from numba import jit
import pyfftw
# Signal processing support
import scipy
from scipy import fft
from scipy import signal
from scipy.signal import correlate
from scipy.signal import convolve
from pyapril import channelPreparation as cp
from pyapril import clutterCancellation as cc
from pyapril import detector as det
c_dtype = np.complex64
#import socket
# UDP is useless to us because it cannot work over mobile internet
# Init UDP
#server = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP)
#server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)
# Enable broadcasting mode
#server.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)
# Set a timeout so the socket does not block
# indefinitely when trying to receive data.
#server.settimeout(0.2)
class SignalProcessor(threading.Thread):
def __init__(self, data_que, module_receiver, logging_level=10):
"""
Parameters:
-----------
:param: data_que: Que to communicate with the UI (web iface/Qt GUI)
:param: module_receiver: Kraken SDR DoA DSP receiver modules
"""
super(SignalProcessor, self).__init__()
self.logger = logging.getLogger(__name__)
self.logger.setLevel(logging_level)
root_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
doa_res_file_path = os.path.join(os.path.join(root_path,"_android_web","DOA_value.html"))
self.DOA_res_fd = open(doa_res_file_path,"w+")
self.module_receiver = module_receiver
self.data_que = data_que
self.en_spectrum = False
self.en_record = False
self.en_DOA_estimation = True
self.first_frame = 1 # Used to configure local variables from the header fields
self.processed_signal = np.empty(0)
# Squelch feature
self.data_ready = False
self.en_squelch = False
self.squelch_threshold = 0.1
self.squelch_trigger_channel = 0
self.raw_signal_amplitude = np.empty(0)
self.filt_signal = np.empty(0)
self.squelch_mask = np.empty(0)
# DOA processing options
self.en_DOA_Bartlett = False
self.en_DOA_Capon = False
self.en_DOA_MEM = False
self.en_DOA_MUSIC = False
self.en_DOA_FB_avg = False
self.DOA_offset = 0
self.DOA_inter_elem_space = 0.5
self.DOA_ant_alignment = "ULA"
self.DOA_theta = np.linspace(0,359,360)
# PR processing options
self.PR_clutter_cancellation = "Wiener MRE"
self.max_bistatic_range = 128
self.max_doppler = 256
self.en_PR = True
# Processing parameters
self.spectrum_window_size = 2048 #1024
self.spectrum_window = "hann"
self.run_processing = False
self.is_running = False
self.channel_number = 4 # Update from header
# Result vectors
self.DOA_Bartlett_res = np.ones(181)
self.DOA_Capon_res = np.ones(181)
self.DOA_MEM_res = np.ones(181)
self.DOA_MUSIC_res = np.ones(181)
self.DOA_theta = np.arange(0,181,1)
self.max_index = 0
self.max_frequency = 0
self.fft_signal_width = 0
self.DOA_theta = np.linspace(0,359,360)
self.spectrum = None #np.ones((self.channel_number+2,N), dtype=np.float32)
self.spectrum_upd_counter = 0
def run(self):
"""
Main processing thread
"""
pyfftw.config.NUM_THREADS = 4
scipy.fft.set_backend(pyfftw.interfaces.scipy_fft)
pyfftw.interfaces.cache.enable()
while True:
self.is_running = False
time.sleep(1)
while self.run_processing:
self.is_running = True
que_data_packet = []
start_time = time.time()
#-----> ACQUIRE NEW DATA FRAME <-----
self.module_receiver.get_iq_online()
# Check frame type for processing
en_proc = (self.module_receiver.iq_header.frame_type == self.module_receiver.iq_header.FRAME_TYPE_DATA)# or \
#(self.module_receiver.iq_header.frame_type == self.module_receiver.iq_header.FRAME_TYPE_CAL)# For debug purposes
"""
You can enable here to process other frame types (such as call type frames)
"""
que_data_packet.append(['iq_header',self.module_receiver.iq_header])
self.logger.debug("IQ header has been put into the data que entity")
# Configure processing parameteres based on the settings of the DAQ chain
if self.first_frame:
self.channel_number = self.module_receiver.iq_header.active_ant_chs
self.spectrum_upd_counter = 0
self.spectrum = np.ones((self.channel_number+2, self.spectrum_window_size), dtype=np.float32)
self.first_frame = 0
decimation_factor = 1
self.data_ready = False
if en_proc:
self.processed_signal = self.module_receiver.iq_samples
self.data_ready = True
first_decimation_factor = 1 #480
# TESTING: DSP side main decimation - significantly slower than NE10 but it works ok-ish
#decimated_signal = signal.decimate(self.processed_signal, first_decimation_factor, n = 584, ftype='fir', zero_phase=True) #first_decimation_factor * 2, ftype='fir')
#self.processed_signal = decimated_signal #.copy()
#spectrum_signal = decimated_signal.copy()
max_amplitude = -100
#max_ch = np.argmax(np.max(self.spectrum[1:self.module_receiver.iq_header.active_ant_chs+1,:], axis=1)) # Find the channel that had the max amplitude
max_amplitude = 0 #np.max(self.spectrum[1+max_ch, :]) #Max amplitude out of all 5 channels
#max_spectrum = self.spectrum[1+max_ch, :] #Send max ch to channel centering
que_data_packet.append(['max_amplitude',max_amplitude])
#-----> SQUELCH PROCESSING <-----
if self.en_squelch:
self.data_ready = False
self.processed_signal, decimation_factor, self.fft_signal_width, self.max_index = \
center_max_signal(self.processed_signal, self.spectrum[0,:], max_spectrum, self.module_receiver.daq_squelch_th_dB, self.module_receiver.iq_header.sampling_freq)
#decimated_signal = []
#if(decimation_factor > 1):
# decimated_signal = signal.decimate(self.processed_signal, decimation_factor, n = decimation_factor * 2, ftype='fir')
# self.processed_signal = decimated_signal #.copy()
#Only update if we're above the threshold
if max_amplitude > self.module_receiver.daq_squelch_th_dB:
self.data_ready = True
#-----> SPECTRUM PROCESSING <-----
if self.en_spectrum and self.data_ready:
spectrum_samples = self.module_receiver.iq_samples #spectrum_signal #self.processed_signal #self.module_receiver.iq_samples #self.processed_signal
N = self.spectrum_window_size
N_perseg = 0
N_perseg = min(N, len(self.processed_signal[0,:])//25)
N_perseg = N_perseg // 1
# Get power spectrum
f, Pxx_den = signal.welch(self.processed_signal, self.module_receiver.iq_header.sampling_freq//first_decimation_factor,
nperseg=N_perseg,
nfft=N,
noverlap=int(N_perseg*0.25),
detrend=False,
return_onesided=False,
window= ('tukey', 0.25), #tukey window gives better time resolution for squelching #self.spectrum_window, #('tukey', 0.25), #self.spectrum_window,
#window=self.spectrum_window,
scaling="spectrum")
self.spectrum[1:self.module_receiver.iq_header.active_ant_chs+1,:] = np.fft.fftshift(10*np.log10(Pxx_den))
self.spectrum[0,:] = np.fft.fftshift(f)
# Create signal window for plot
# signal_window = np.ones(len(self.spectrum[1,:])) * -100
# signal_window[max(self.max_index - self.fft_signal_width//2, 0) : min(self.max_index + self.fft_signal_width//2, len(self.spectrum[1,:]))] = max(self.spectrum[1,:])
#signal_window = np.ones(len(max_spectrum)) * -100
#signal_window[max(self.max_index - self.fft_signal_width//2, 0) : min(self.max_index + self.fft_signal_width//2, len(max_spectrum))] = max(max_spectrum)
#self.spectrum[self.channel_number+1, :] = signal_window #np.ones(len(spectrum[1,:])) * self.module_receiver.daq_squelch_th_dB # Plot threshold line
que_data_packet.append(['spectrum', self.spectrum])
#-----> Passive Radar <-----
conf_val = 0
theta_0 = 0
if self.en_PR and self.data_ready and self.channel_number > 1:
ref_ch = self.module_receiver.iq_samples[0,:]
surv_ch = self.module_receiver.iq_samples[1,:]
td_filter_dimension = self.max_bistatic_range
start = time.time()
if self.PR_clutter_cancellation == "Wiener MRE":
surv_ch, w = Wiener_SMI_MRE(ref_ch, surv_ch, td_filter_dimension)
#surv_ch, w = cc.Wiener_SMI_MRE(ref_ch, surv_ch, td_filter_dimension)
surv_ch = det.windowing(surv_ch, "Hamming") #surv_ch * signal.tukey(surv_ch.size, alpha=0.25) #det.windowing(surv_ch, "hamming")
max_Doppler = self.max_doppler #256
max_range = self.max_bistatic_range
#RD_matrix = det.cc_detector_ons(ref_ch, surv_ch, self.module_receiver.iq_header.sampling_freq, max_Doppler, max_range, verbose=0, Qt_obj=None)
RD_matrix = cc_detector_ons(ref_ch, surv_ch, self.module_receiver.iq_header.sampling_freq, max_Doppler, max_range)
end = time.time()
print("Time: " + str((end-start) * 1000))
que_data_packet.append(['RD_matrix', RD_matrix])
# Record IQ samples
if self.en_record:
# TODO: Implement IQ frame recording
self.logger.error("Saving IQ samples to npy is obsolete, IQ Frame saving is currently not implemented")
stop_time = time.time()
que_data_packet.append(['update_rate', stop_time-start_time])
que_data_packet.append(['latency', int(stop_time*10**3)-self.module_receiver.iq_header.time_stamp])
# If the que is full, and data is ready (from squelching), clear the buffer immediately so that useful data has the priority
if self.data_que.full() and self.data_ready:
try:
#self.logger.info("BUFFER WAS NOT EMPTY, EMPTYING NOW")
self.data_que.get(False) #empty que if not taken yet so fresh data is put in
except queue.Empty:
#self.logger.info("DIDNT EMPTY")
pass
# Put data into buffer, but if there is no data because its a cal/trig wait frame etc, then only write if the buffer is empty
# Otherwise just discard the data so that we don't overwrite good DATA frames.
try:
self.data_que.put(que_data_packet, False) # Must be non-blocking so DOA can update when dash browser window is closed
except:
# Discard data, UI couldn't consume fast enough
pass
"""
start = time.time()
end = time.time()
thetime = ((end - start) * 1000)
print ("Time elapsed: ", thetime)
"""
@jit(fastmath=True)
def Wiener_SMI_MRE(ref_ch, surv_ch, K):
"""
Description:
------------
Performs Wiener filtering with applying the Minimum Redundance Estimation (MRE) technique.
When using MRE, the autocorrelation matrix is not fully estimated, but only the first column.
With this modification the required calculations can be reduced from KxK to K element.
Parameters:
-----------
:param K : Filter tap number
:param ref_ch : Reference signal array
:param surv_ch: Surveillance signal array
:type K : int
:type ref_ch : 1 x N complex numpy array
:type surv_ch: 1 x N complex numpy array
Return values:
--------------
:return filt: Filtered surveillance channel
:rtype filt: 1 x N complex numpy array
:return None: Input parameters are not consistent
"""
N = ref_ch.shape[0] # Number of time samples
R, r = pruned_correlation(ref_ch, surv_ch, K, N)
R_mult = R_eye_memoize(K)
w = fast_w(R, r, K, R_mult)
#return surv_ch - np.convolve(ref_ch, w)[0:N], w # subtract the zero doppler clutter
return surv_ch - signal.oaconvolve(ref_ch, w)[0:N], w # subtract the zero doppler clutter #oaconvolve saves us about 100-200 ms
@njit(fastmath=True, parallel=True, cache=True)
def fast_w(R, r, K, R_mult):
# Complete the R matrix based on its Hermitian and Toeplitz property
for k in range(1, K):
R[:, k] = shift(R[:, 0], k)
#R[:, K] = shift(R[:,0], K)
R += np.transpose(np.conjugate(R))
R *= R_mult #(np.ones(K) - np.eye(K) * 0.5)
#w = np.dot(lin.inv(R), r) # weight vector
w = lin.inv(R) @ r #np.dot(lin.inv(R), r) # weight vector #matmul (@) may be slightly faster that np.dot for 1D, 2D arrays.
# inverse and dot product run time : 1.1s for 2048*2048 matrix
return w
#Memoize ~50ms speedup?
@lru_cache(maxsize=2)
def R_eye_memoize(K):
return (np.ones(K) - np.eye(K) * 0.5)
#Modified pruned correlation, returns R and r directly and saves one FFT
@jit(fastmath=True, cache=True)
def pruned_correlation(ref_ch, surv_ch, clen, N):
"""
Description:
-----------
Calculates the part of the correlation function of arrays with same size
The total length of the cross-correlation function is 2*N-1, but this
function calculates the values of the cross-correlation between [N-1 : N+clen-1]
Parameters:
-----------
:param x : input array
:param y : input array
:param clen: correlation length
:type x: 1 x N complex numpy array
:type y: 1 x N complex numpy array
:type clen: int
Return values:
--------------
:return corr : part of the cross-correlation function
:rtype corr : 1 x clen complex numpy array
:return None : inconsistent array size
"""
R = np.zeros((clen, clen), dtype=c_dtype) # Autocorrelation mtx.
# --calculation--
# set up input matrices pad zeros if not multiply of the correlation length
cols = clen - 1 #(clen = Filter drowsimension)
rows = np.int32(N / (cols)) + 1
zeropads = cols * rows - N
x = np.pad(ref_ch, (0, zeropads))
# shaping inputs into matrices
xp = np.reshape(x, (rows, cols))
# padding matrices for FFT
ypp = np.vstack([xp[1:, :], np.zeros(cols, dtype=c_dtype)]) #vstack appears to be faster than pad
yp = np.concatenate([xp, ypp], axis=1)
# execute FFT on the matrices
xpw = fft.fft(xp, n = 2*cols, axis=1, workers=4, overwrite_x=True)
bpw = fft.fft(yp, axis=1, workers=4, overwrite_x=True)
# magic formula which describes the unified equation of the universe
# corr_batches = np.fliplr(fft.fftshift(fft.ifft(corr_mult(xpw, bpw), axis=1, workers=4, overwrite_x=True)).conj()[:, 0:clen])
corr_batches = fft.fftshift(fft.ifft(corr_mult(xpw, bpw), axis=1, workers=4, overwrite_x=True)).conj()[:, 0:clen]
# sum each value in a column of the batched correlation matrix
R[:,0] = np.fliplr([np.sum(corr_batches, axis=0)])[0]
#calc r
y = np.pad(surv_ch, (0, zeropads))
yp = np.reshape(y, (rows, cols))
ypp = np.vstack([yp[1:, :], np.zeros(cols, dtype=c_dtype)]) #vstack appears to be faster than pad
yp = np.concatenate([yp, ypp], axis=1)
bpw = fft.fft(yp, axis=1, workers=4, overwrite_x=True)
#corr_batches = np.fliplr(fft.fftshift(fft.ifft(corr_mult(xpw, bpw), axis=1, workers=4, overwrite_x=True)).conj()[:, 0:clen])
corr_batches = fft.fftshift(fft.ifft(corr_mult(xpw, bpw), axis=1, workers=4, overwrite_x=True)).conj()[:, 0:clen]
#r = np.sum(corr_batches, axis=0)
r = np.fliplr([np.sum(corr_batches, axis=0)])[0]
return R, r
@njit(fastmath=True, cache=True)
def shift(x, i):
"""
Description:
-----------
Similar to np.roll function, but not circularly shift values
Example:
x = |x0|x1|...|xN-1|
y = shift(x,2)
x --> y: |0|0|x0|x1|...|xN-3|
Parameters:
-----------
:param:x : input array on which the roll will be performed
:param i : delay value [sample]
:type i :int
:type x: N x 1 complex numpy array
Return values:
--------------
:return shifted : shifted version of x
:rtype shifted: N x 1 complex numpy array
"""
N = x.shape[0]
if np.abs(i) >= N:
return np.zeros(N, dtype=c_dtype)
if i == 0:
return x
shifted = np.roll(x, i)
if i < 0:
shifted[np.mod(N + i, N):] = np.zeros(np.abs(i), dtype=c_dtype)
if i > 0:
shifted[0:i] = np.zeros(np.abs(i), dtype=c_dtype)
return shifted
@njit(fastmath=True, parallel=True, cache=True)
def resize_and_align(no_sub_tasks, ref_ch, surv_ch, fs, fD_max, r_max):
surv_ch_align = np.reshape(surv_ch,(no_sub_tasks, r_max)) # shaping surveillance signal array into a matrix
pad_zeros = np.expand_dims(np.zeros(r_max, dtype=c_dtype), axis=0)
surv_ch_align = np.vstack((surv_ch_align, pad_zeros)) # padding one row of zeros into the surv matrix
surv_ch_align = np.concatenate((surv_ch_align[0 : no_sub_tasks,:], surv_ch_align[1 : no_sub_tasks +1, :]), axis = 1)
ref_ch_align = np.reshape(ref_ch, (no_sub_tasks, r_max)) # shaping reference signal array into a matrix
pad_zeros = np.zeros((no_sub_tasks, r_max),dtype = c_dtype)
ref_ch_align = np.concatenate((ref_ch_align, pad_zeros),axis = 1) # shaping
return ref_ch_align, surv_ch_align
@njit(fastmath=True, cache=True)
def corr_mult(surv_fft, ref_fft):
return np.multiply(surv_fft, ref_fft.conj())
@jit(fastmath=True, cache=True)
def cc_detector_ons(ref_ch, surv_ch, fs, fD_max, r_max):
"""
Parameters:
-----------
:param N: Range resolution - N must be a divisor of the input length
:param F: Doppler resolution, F has a theoretical limit. If you break the limit, the output may repeat
itself and get wrong results. F should be less than length/N otherwise use other method!
Return values:
--------------
:return None: Improper input parameters
"""
N = ref_ch.size
# --> Set processing parameters
fD_step = fs / (2 * N) # Doppler frequency step size (with zero padding)
Doppler_freqs_size = int(fD_max / fD_step)
no_sub_tasks = N // r_max
# Allocate range-Doppler maxtrix
mx = np.zeros((2*Doppler_freqs_size+1, r_max),dtype = c_dtype) #memoize_zeros((2*Doppler_freqs_size+1, r_max), c_dtype) #np.zeros((2*Doppler_freqs_size+1, r_max),dtype = nb.c8)
ref_ch_align, surv_ch_align = resize_and_align(no_sub_tasks, ref_ch, surv_ch, fs, fD_max, r_max)
# row wise fft on both channels
ref_fft = fft.fft(ref_ch_align, axis = 1, overwrite_x=True, workers=4) #pyfftw.interfaces.numpy_fft.fft(ref_ch_align_a, axis = 1, overwrite_input=True, threads=4) #fft.fft(ref_ch_align_a, axis = 1, overwrite_x=True, workers=4)
surv_fft = fft.fft(surv_ch_align, axis = 1, overwrite_x=True, workers=4) #pyfftw.interfaces.numpy_fft.fft(surv_ch_align_a, axis = 1, overwrite_input=True, threads=4) #fft.fft(surv_ch_align_a, axis = 1, overwrite_x=True, workers=4)
corr = corr_mult(surv_fft, ref_fft) #np.multiply(surv_fft, ref_fft.conj())
corr = fft.ifft(corr,axis = 1, workers=4, overwrite_x=True)
corr_a = pyfftw.empty_aligned(np.shape(corr), dtype=c_dtype)
corr_a[:] = corr #.copy()
# This is the most computationally intensive part ~120ms, overwrite_x=True gives a big speedup, not sure if it changes the result though...
corr = fft.fft(corr_a, n=2* no_sub_tasks, axis = 0, workers=4, overwrite_x=True) # Setting the output size with "n=.." is faster than doing a concat first.
# crop and fft shift
mx[ 0 : Doppler_freqs_size, 0 : r_max] = corr[2*no_sub_tasks - Doppler_freqs_size : 2*no_sub_tasks, 0 : r_max]
mx[Doppler_freqs_size : 2 * Doppler_freqs_size+1, 0 : r_max] = corr[ 0 : Doppler_freqs_size+1 , 0 : r_max]
return mx
#NUMBA optimized center tracking. Gives a mild speed boost ~25% faster.
@njit(fastmath=True, cache=True, parallel=True)
def center_max_signal(processed_signal, frequency, fft_spectrum, threshold, sample_freq):
# Where is the max frequency? e.g. where is the signal?
max_index = np.argmax(fft_spectrum)
max_frequency = frequency[max_index]
# Auto decimate down to exactly the max signal width
fft_signal_width = np.sum(fft_spectrum > threshold) + 25
decimation_factor = max((sample_freq // fft_signal_width) // 2, 1)
# Auto shift peak frequency center of spectrum, this frequency will be decimated:
# https://pysdr.org/content/filters.html
f0 = -max_frequency #+10
Ts = 1.0/sample_freq
t = np.arange(0.0, Ts*len(processed_signal[0, :]), Ts)
exponential = np.exp(2j*np.pi*f0*t) # this is essentially a complex sine wave
return processed_signal * exponential, decimation_factor, fft_signal_width, max_index
# NUMBA optimized MUSIC function. About 100x faster on the Pi 4
@njit(fastmath=True, cache=True)
def DOA_MUSIC(R, scanning_vectors, signal_dimension, angle_resolution=1):
# --> Input check
if R[:,0].size != R[0,:].size:
print("ERROR: Correlation matrix is not quadratic")
return np.ones(1, dtype=nb.c16)*-1 #[(-1, -1j)]
if R[:,0].size != scanning_vectors[:,0].size:
print("ERROR: Correlation matrix dimension does not match with the antenna array dimension")
return np.ones(1, dtype=nb.c16)*-2
#ADORT = np.zeros(scanning_vectors[0,:].size, dtype=np.complex) #CHANGE TO nb.c16 for NUMBA
ADORT = np.zeros(scanning_vectors[0,:].size, dtype=nb.c16)
M = R[:,0].size #np.size(R, 0)
# --- Calculation ---
# Determine eigenvectors and eigenvalues
sigmai, vi = lin.eig(R)
sigmai = np.abs(sigmai)
idx = sigmai.argsort()[::1] # Sort eigenvectors by eigenvalues, smallest to largest
#sigmai = sigmai[idx] # Eigenvalues not used again
vi = vi[:,idx]
# Generate noise subspace matrix
noise_dimension = M - signal_dimension
#E = np.zeros((M, noise_dimension),dtype=np.complex)
E = np.zeros((M, noise_dimension),dtype=nb.c16)
for i in range(noise_dimension):
E[:,i] = vi[:,i]
theta_index=0
for i in range(scanning_vectors[0,:].size):
S_theta_ = scanning_vectors[:, i]
S_theta_ = S_theta_.T
ADORT[theta_index] = 1/np.abs(S_theta_.conj().T @ (E @ E.conj().T) @ S_theta_)
theta_index += 1
return ADORT
# Numba optimized version of pyArgus corr_matrix_estimate with "fast". About 2x faster on Pi4
@njit(fastmath=True, cache=True) #(nb.c8[:,:](nb.c16[:,:]))
def corr_matrix(X):
M = X[:,0].size
N = X[0,:].size
#R = np.zeros((M, M), dtype=nb.c8)
R = np.dot(X, X.conj().T)
R = np.divide(R, N)
return R
# Numba optimized scanning vectors generation for UCA arrays. About 10x faster on Pi4
# LRU cache memoize about 1000x faster.
@lru_cache(maxsize=8)
def uca_scanning_vectors(M, DOA_inter_elem_space):
thetas = np.linspace(0,359,360) # Remember to change self.DOA_thetas too, we didn't include that in this function due to memoization cannot work with arrays
x = DOA_inter_elem_space * np.cos(2*np.pi/M * np.arange(M))
y = -DOA_inter_elem_space * np.sin(2*np.pi/M * np.arange(M)) # For this specific array only
scanning_vectors = np.zeros((M, thetas.size), dtype=np.complex)
for i in range(thetas.size):
scanning_vectors[:,i] = np.exp(1j*2*np.pi* (x*np.cos(np.deg2rad(thetas[i])) + y*np.sin(np.deg2rad(thetas[i]))))
return scanning_vectors
# scanning_vectors = de.gen_scanning_vectors(M, x, y, self.DOA_theta)
@njit(fastmath=True, cache=True)
def DOA_plot_util(DOA_data, log_scale_min=-100):
"""
This function prepares the calulcated DoA estimation results for plotting.
- Noramlize DoA estimation results
- Changes to log scale
"""
DOA_data = np.divide(np.abs(DOA_data), np.max(np.abs(DOA_data))) # Normalization
DOA_data = 10*np.log10(DOA_data) # Change to logscale
for i in range(len(DOA_data)): # Remove extremely low values
if DOA_data[i] < log_scale_min:
DOA_data[i] = log_scale_min
return DOA_data
@njit(fastmath=True, cache=True)
def calculate_doa_papr(DOA_data):
return 10*np.log10(np.max(np.abs(DOA_data))/np.mean(np.abs(DOA_data)))
# Old time-domain squelch algorithm (Unused as freq domain FFT with overlaps gives significantly better sensitivity with acceptable time resolution expense
"""
K = 10
self.filtered_signal = self.raw_signal_amplitude #convolve(np.abs(self.raw_signal_amplitude),np.ones(K), mode = 'same')/K
# Burst is always started at the begining of the processed block, ensured by the squelch module in the DAQ FW
burst_stop_index = len(self.filtered_signal) # CARL FIX: Initialize this to the length of the signal, incase the signal is active the entire time
self.logger.info("Original burst stop index: {:d}".format(burst_stop_index))
min_burst_size = K
burst_stop_amp_val = 0
for n in np.arange(K, len(self.filtered_signal), 1):
if self.filtered_signal[n] < self.squelch_threshold:
burst_stop_amp_val = self.filtered_signal[n]
burst_stop_index = n
burst_stop_index-=K # Correction with the length of filter
break
#burst_stop_index-=K # Correction with the length of filter
self.logger.info("Burst stop index: {:d}".format(burst_stop_index))
self.logger.info("Burst stop ampl val: {:f}".format(burst_stop_amp_val))
self.logger.info("Processed signal length: {:d}".format(len(self.processed_signal[0,:])))
# If sign
if burst_stop_index < min_burst_size:
self.logger.debug("The length of the captured burst size is under the minimum: {:d}".format(burst_stop_index))
burst_stop_index = 0
if burst_stop_index !=0:
self.logger.info("INSIDE burst_stop_index != 0")
self.logger.debug("Burst stop index: {:d}".format(burst_stop_index))
self.logger.debug("Burst stop ampl val: {:f}".format(burst_stop_amp_val))
self.squelch_mask = np.zeros(len(self.filtered_signal))
self.squelch_mask[0 : burst_stop_index] = np.ones(burst_stop_index)*self.squelch_threshold
# Next line removes the end parts of the samples after where the signal ended, truncating the array
self.processed_signal = self.module_receiver.iq_samples[: burst_stop_index, self.squelch_mask == self.squelch_threshold]
self.logger.info("Raw signal length when burst_stop_index!=0: {:d}".format(len(self.module_receiver.iq_samples[0,:])))
self.logger.info("Processed signal length when burst_stop_index!=0: {:d}".format(len(self.processed_signal[0,:])))
#self.logger.info(' '.join(map(str, self.processed_signal)))
self.data_ready=True
else:
self.logger.info("Signal burst is not found, try to adjust the threshold levels")
#self.data_ready=True
self.squelch_mask = np.ones(len(self.filtered_signal))*self.squelch_threshold
self.processed_signal = np.zeros([self.channel_number, len(self.filtered_signal)])
"""
