Non-coherent integration¶
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats
import sdr
%config InlineBackend.print_figure_kwargs = {"facecolor" : "w"}
%matplotlib inline
# %matplotlib widget
sdr.plot.use_style()
rng = np.random.default_rng(0)
In this example, we will detect two signals, \(x_1[n]\) and \(x_2[n]\). The first signal \(x_1[n]\) will be detected as is. The second signal \(x_2[n]\) will be non-coherently integrated and then detected.
We will show that \(x_2[n]\) can have a lower SNR than \(x_1[n]\) and achieve equivalent detection performance.
The SNR reduction equals the non-coherent integration gain \(G_{nc}\), as calculated by sdr.non_coherent_gain().
if True:
# Calculate the non-coherent gain using the input SNR as a reference
snr2 = 3 # Input SNR
p_fa = 1e-3
n_nc = 10
g_nc = sdr.non_coherent_gain(n_nc, snr2, p_fa=p_fa, snr_ref="input")
snr1 = snr2 + g_nc # Output SNR
else:
# Calculate the non-coherent gain using the desired output SNR as a reference
snr1 = 3 # Output SNR
p_fa = 1e-3
n_nc = 10
g_nc = sdr.non_coherent_gain(n_nc, snr1, p_fa=p_fa, snr_ref="output")
snr2 = snr1 - g_nc # Input SNR
print(f"SNR 1: {snr1:.2f} dB")
print(f"Detection false alarm rate: {p_fa:1.2e}")
print(f"Non-coherently integrated samples: {n_nc}")
print(f"Non-coherent gain: {g_nc:.2f} dB")
print(f"SNR 2: {snr2:.2f} dB")
SNR 1: 10.32 dB
Detection false alarm rate: 1.00e-03
Non-coherently integrated samples: 10
Non-coherent gain: 7.32 dB
SNR 2: 3.00 dB
Detect \(x_1[n]\)¶
The complex time-domain signal \(x_1[n]\) is the sum of a phase-coherent signal and noise.
\[x_1[n] = s_1[n] + w_1[n]\]
The signal \(s_1[n]\) has constant amplitude \(A_1\). The noise \(w_1[n]\) is i.i.d. complex additive white Gaussian with variance \(\sigma_1^2\).
\[s_1[n] = A_1\]
\[w_1[n] \sim \mathcal{CN}(0, \sigma_1^2)\]
def create_x(snr):
sigma = 1 # Noise standard deviation, sigma
A = np.sqrt(sdr.linear(snr) * sigma**2) # Signal voltage, A
N = 100_000
s = A * np.ones(N)
w = rng.normal(0, np.sqrt(sigma**2 / 2), N) + 1j * rng.normal(0, np.sqrt(sigma**2 / 2), N)
x_h0 = w # x under the H0 hypothesis
x_h1 = s + w # x under the H1 hypothesis
return A, sigma, x_h0, x_h1
Generate the complex time-domain signal \(x_1[n]\) with 10.32-dB SNR.
A1, sigma1, x1_h0, x1_h1 = create_x(snr1)
plt.figure()
sdr.plot.time_domain(x1_h0.real, label="$\mathcal{H}_0$: Noise")
sdr.plot.time_domain(x1_h1.real, label="$\mathcal{H}_1$: Signal + Noise")
plt.title(f"$\mathrm{{Re}}\{{x_1[n]\}}$ with {snr1:1.2f}-dB SNR")
plt.show()
The signal \(x_1[n]\) is distributed as follows.
\[\begin{split}
\begin{align}
\mathcal{H}_0 &: x_1[n] \sim \mathcal{CN}(0, \sigma_1^2) \\
\mathcal{H}_1 &: x_1[n] \sim \mathcal{CN}(A_1, \sigma_1^2)
\end{align}
\end{split}\]
\(\mathcal{H}_0\) is the null hypothesis. It indicates the condition when only noise is present. \(\mathcal{H}_1\) is the alternative hypothesis. It indicates the condition when signal and noise are present.
x1_h0_real_theory = scipy.stats.norm(0, np.sqrt(sigma1**2 / 2))
x1_h1_real_theory = scipy.stats.norm(A1, np.sqrt(sigma1**2 / 2))
plt.figure()
plt.hist(x1_h0.real, bins=101, histtype="step", density=True)
plt.hist(x1_h1.real, bins=101, histtype="step", density=True)
plt.gca().set_prop_cycle(None)
axis = np.linspace(min(x1_h0.real), max(x1_h1.real), 101)
plt.plot(axis, x1_h0_real_theory.pdf(axis), label="$\mathcal{H}_0$: Noise")
plt.plot(axis, x1_h1_real_theory.pdf(axis), label="$\mathcal{H}_1$: Signal + Noise")
plt.legend()
plt.xlabel("$\mathrm{Re}\{x_1[n]\}$")
plt.ylabel("Probability density")
plt.title(f"PDF of $\mathrm{{Re}}\{{x_1[n]\}}$ with {snr1:1.2f}-dB SNR")
plt.show()
Determine the detection performance of \(x_1[n]\). If the absolute phase of \(x_1[n]\) is unknown, which is common, the signal must be detected by examining the power of the signal \(z_1[n]\).
\[z_1[n] = \left| x_1[n] \right|^2\]
z1_h0 = np.abs(x1_h0) ** 2
z1_h1 = np.abs(x1_h1) ** 2
plt.figure()
sdr.plot.time_domain(z1_h0, label="$\mathcal{H}_0$: Noise")
sdr.plot.time_domain(z1_h1, label="$\mathcal{H}_1$: Signal + Noise")
plt.title(f"$z_1[n]$ with {snr1:1.2f}-dB SNR")
plt.show()
The signal power \(z_1[n]\) is distributed as follows.
\[\begin{split}
\begin{align}
\mathcal{H}_0 &: z_1[n] \sim \chi_{\nu}^2 \\
\mathcal{H}_1 &: z_1[n] \sim {\chi'}_{\nu}^2(\lambda)
\end{align}
\end{split}\]
\(\nu\) is the degrees of freedom and \(\lambda\) is the non-centrality parameter.
\[\nu = 2\]
\[\lambda = \frac{A_1^2}{\sigma_1^2 / 2}\]
nu = 2 # Degrees of freedom
lambda_ = A1**2 / (sigma1**2 / 2) # Non-centrality parameter
z1_h0_theory = scipy.stats.chi2(nu, scale=sigma1**2 / 2)
z1_h1_theory = scipy.stats.ncx2(nu, lambda_, scale=sigma1**2 / 2)
threshold = z1_h0_theory.isf(p_fa)
p_d = z1_h1_theory.sf(threshold)
plt.figure()
plt.hist(z1_h0, bins=101, histtype="step", density=True)
plt.hist(z1_h1, bins=101, histtype="step", density=True)
plt.gca().set_prop_cycle(None)
axis = np.linspace(min(z1_h0), max(z1_h1), 101)
plt.plot(axis, z1_h0_theory.pdf(axis), label="$\mathcal{H}_0$: Noise")
plt.plot(axis, z1_h1_theory.pdf(axis), label="$\mathcal{H}_1$: Signal + Noise")
plt.axvline(threshold, color="k", linestyle="--", label="Threshold")
plt.legend()
plt.xlabel("$z_1[n]$")
plt.ylabel("Probability density")
plt.title(f"PDF of $z_1[n]$ with {snr1:1.2f}-dB SNR\n$P_d =$ {p_d:1.2e}, $P_{{fa}} =$ {p_fa:1.2e}")
plt.show()
Measure the probability of detection and probability of false alarm. Compare the empirical results to theoretical.
p_d_meas = np.mean(z1_h1 > threshold)
print(f"Probability of detection: {p_d:1.2e} theory, {p_d_meas:1.2e} measured")
p_fa_meas = np.mean(z1_h0 > threshold)
print(f"Probability of false alarm: {p_fa:1.2e} theory, {p_fa_meas:1.2e} measured")
Probability of detection: 8.52e-01 theory, 8.52e-01 measured
Probability of false alarm: 1.00e-03 theory, 1.05e-03 measured
Detect \(x_2[n]\)¶
Generate the complex time-domain signal \(x_2[n]\) with 3-dB SNR.
A2, sigma2, x2_h0, x2_h1 = create_x(snr2)
plt.figure()
sdr.plot.time_domain(x2_h0.real, label="$\mathcal{H}_0$: Noise")
sdr.plot.time_domain(x2_h1.real, label="$\mathcal{H}_1$: Signal + Noise")
plt.title(f"$\mathrm{{Re}}\{{x_2[n]\}}$ with {snr2:1.2f}-dB SNR")
plt.show()
x2_h0_real_theory = scipy.stats.norm(0, np.sqrt(sigma2**2 / 2))
x2_h1_real_theory = scipy.stats.norm(A2, np.sqrt(sigma2**2 / 2))
plt.figure()
plt.hist(x2_h0.real, bins=101, histtype="step", density=True)
plt.hist(x2_h1.real, bins=101, histtype="step", density=True)
plt.gca().set_prop_cycle(None)
axis = np.linspace(min(x2_h0.real), max(x2_h1.real), 101)
plt.plot(axis, x2_h0_real_theory.pdf(axis), label="$\mathcal{H}_0$: Noise")
plt.plot(axis, x2_h1_real_theory.pdf(axis), label="$\mathcal{H}_1$: Signal + Noise")
plt.legend()
plt.xlabel("$\mathrm{Re}\{x_2[n]\}$")
plt.ylabel("Probability density")
plt.title(f"PDF of $\mathrm{{Re}}\{{x_2[n]\}}$ with {snr2:1.2f}-dB SNR")
plt.show()
Non-coherently integrate \(N_{NC}\) contiguous samples from the signal \(x_2[n]\).
\[z_2[n] = \sum_{m=0}^{N_{NC}-1} \left| x_2[n -m] \right|^2\]
z2_h0 = np.sum(np.abs(np.lib.stride_tricks.sliding_window_view(x2_h0, n_nc)) ** 2, axis=-1)
z2_h1 = np.sum(np.abs(np.lib.stride_tricks.sliding_window_view(x2_h1, n_nc)) ** 2, axis=-1)
plt.figure()
sdr.plot.time_domain(z2_h0, label="$\mathcal{H}_0$: Noise")
sdr.plot.time_domain(z2_h1, label="$\mathcal{H}_1$: Signal + Noise")
plt.title(f"$z_2[n]$ with {snr2:1.2f}-dB initial SNR")
plt.show()
nu = 2 * n_nc # Degrees of freedom
lambda_ = A2**2 * n_nc / (sigma2**2 / 2) # Non-centrality parameter
z2_h0_theory = scipy.stats.chi2(nu, scale=sigma2**2 / 2)
z2_h1_theory = scipy.stats.ncx2(nu, lambda_, scale=sigma2**2 / 2)
threshold = z2_h0_theory.isf(p_fa)
p_d = z2_h1_theory.sf(threshold)
plt.figure()
plt.hist(z2_h0, bins=101, histtype="step", density=True)
plt.hist(z2_h1, bins=101, histtype="step", density=True)
plt.gca().set_prop_cycle(None)
axis = np.linspace(min(z2_h0), max(z2_h1), 101)
plt.plot(axis, z2_h0_theory.pdf(axis), label="$\mathcal{H}_0$: Noise")
plt.plot(axis, z2_h1_theory.pdf(axis), label="$\mathcal{H}_1$: Signal + Noise")
plt.axvline(threshold, color="k", linestyle="--", label="Threshold")
plt.legend()
plt.xlabel("$z_2[n]$")
plt.ylabel("Probability density")
plt.title(f"PDF of $z_2[n]$ with {snr2:1.2f}-dB initial SNR\n$P_d =$ {p_d:1.2e}, $P_{{fa}} =$ {p_fa:1.2e}")
plt.show()
Measure the probability of detection and probability of false alarm. Compare the empirical results to theoretical.
Notice that the detection performance of \(x_1[n]\) and \(x_2[n]\) are equivalent.
p_d_meas = np.mean(z2_h1 > threshold)
print(f"Probability of detection: {p_d:1.2e} theory, {p_d_meas:1.2e} measured")
p_fa_meas = np.mean(z2_h0 > threshold)
print(f"Probability of false alarm: {p_fa:1.2e} theory, {p_fa_meas:1.2e} measured")
Probability of detection: 8.52e-01 theory, 8.56e-01 measured
Probability of false alarm: 1.00e-03 theory, 9.20e-04 measured
Last update:
Jun 15, 2024