Computes the theoretical detection threshold \(\gamma\).

Parameters:¶

p_fa: ArrayLike¶

The desired probability of false alarm \(P_{fa}\) in \((0, 1)\).

sigma2: ArrayLike = 1¶

The noise variance \(\sigma^2\) in linear units.

detector: 'coherent' | 'linear' | 'square-law' = 'square-law'¶

The detector type.

• "coherent": A coherent detector, \(T(x) = \mathrm{Re}\{x[n]\}\).

• "linear": A linear detector, \(T(x) = \left| x[n] \right|\).

• "square-law": A square-law detector, \(T(x) = \left| x[n] \right|^2\).

complex: bool = True¶

Indicates whether the input signal is real or complex. This affects how the SNR is converted to noise variance.

n_c: int = 1¶

The number of samples to coherently integrate \(N_c\).

n_nc: int | None = None¶

The number of samples to non-coherently integrate \(N_{nc}\). Non-coherent integration is only allowable for linear and square-law detectors.

Returns:¶

The detection threshold \(\gamma\) in linear units.

Examples¶

In [1]: p_fa = 1e-1  # Probability of false alarm

In [2]: sigma2 = 1  # Noise variance

Compute the detection threshold for the coherent detector. Plot the PDFs and observe the desired \(P_{fa}\) is achieved.

In [3]: detector = "coherent"; \
   ...: h0 = sdr.h0(sigma2, detector)
   ...: 

In [4]: threshold = sdr.threshold(p_fa, sigma2, detector); threshold
Out[4]: 0.9061938024368232

In [5]: plt.figure(); \
   ...: sdr.plot.detector_pdfs(h0=h0, threshold=threshold); \
   ...: plt.title("Coherent Detector: Probability density functions");
   ...: 

Compute the detection threshold for the linear detector. Plot the PDFs and observe the desired \(P_{fa}\) is achieved.

In [6]: detector = "linear"; \
   ...: h0 = sdr.h0(sigma2, detector)
   ...: 

In [7]: threshold = sdr.threshold(p_fa, sigma2, detector); threshold
Out[7]: 1.5174271293851465

In [8]: plt.figure(); \
   ...: sdr.plot.detector_pdfs(h0=h0, threshold=threshold); \
   ...: plt.title("Linear Detector: Probability density functions");
   ...: 

Compute the detection threshold for the square-law detector. Plot the PDFs and observe the desired \(P_{fa}\) is achieved.

In [9]: detector = "square-law"; \
   ...: h0 = sdr.h0(sigma2, detector)
   ...: 

In [10]: threshold = sdr.threshold(p_fa, sigma2, detector); threshold
Out[10]: 2.302585092994046

In [11]: plt.figure(); \
   ....: sdr.plot.detector_pdfs(h0=h0, threshold=threshold); \
   ....: plt.title("Square-Law Detector: Probability density functions");
   ....: