Detection¶

Detectors¶

class sdr.ReplicaCorrelator

Implements an clairvoyant replica-correlator detector.

class sdr.EnergyDetector

Implements an energy detector.

Coherent integration¶

sdr.coherent_gain(n_c: ArrayLike) → NDArray[float64]

Computes the SNR improvement by coherently integrating \(N_C\) samples.

sdr.coherent_gain_loss(integration_time, ...) → NDArray[float64]

Computes the coherent gain loss (CGL) as a function of the given integration time and frequency offset.

sdr.max_integration_time(cgl: ArrayLike, ...) → NDArray[float32]

Computes the maximum integration time that produces at most the provided coherent gain loss (CGL).

sdr.max_frequency_offset(cgl: ArrayLike, ...) → NDArray[float32]

Computes the maximum frequency offset that produces at most the provided coherent gain loss (CGL).

Non-coherent integration¶

sdr.non_coherent_gain(n_nc: ArrayLike, snr, ...) → NDArray[float64]

Computes the SNR improvement by non-coherently integrating \(N_{NC}\) samples.

Theoretical performance¶

sdr.h0_theory(sigma2: float = 1.0, ...) → rv_continuous

Computes the statistical distribution under the null hypothesis \(\mathcal{H}_0\).

sdr.h1_theory(snr: float, sigma2: float = 1.0, ...) → rv_continuous

Computes the statistical distribution under the alternative hypothesis \(\mathcal{H}_1\).

sdr.p_d(snr: ArrayLike, p_fa: ArrayLike, ...) → NDArray[float64]

Computes the theoretical probability of detection \(P_{D}\).

sdr.p_fa(threshold: ArrayLike, ...) → NDArray[float64]

Computes the theoretical probability of false alarm \(P_{FA}\).

sdr.threshold(p_fa: ArrayLike, ...) → NDArray[float64]

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

Approximated performance¶

sdr.albersheim(p_d: ArrayLike, p_fa, ...) → NDArray[float64]

Estimates the minimum required single-sample SNR.