Detection statistics¶
Pd, Pfa and threshold models, a lock detector and a sync-word finder.
Python module doppler.detection. Area: Estimation.
Classes and functions¶
Gallery pages in this domain¶
- Detection Theory Curves — Pd vs SNR and dwell from closed-form Marcum Q.
- Monte Carlo vs Marcum Q Theory — 30,000-trial validation of the Marcum Q curves.
Design pages in this domain¶
- Detection Sizing — the four laws behind one prefix — the four statistical laws behind one
det_prefix: which one a statistic actually obeys, why non-coherent looks raise their own threshold, and the 41x an estimated noise reference costs - Lock Detection — the reasoning — the sizing chain every lock detector shares, and the independence it assumes
Used in other domains¶
Pages filed under another domain that name a symbol of this module, or link to or from a page filed here: a class used for another task is found from both sides, never re-filed.
- Spectral analysis: 2-D Acquisition Grid
- Acquisition: CarrierAcquisition: RRC Pulse Shaping, The acquisition surface: the DSSS search engine
- Tracking & synchronization: Lock Detection Across doppler.track, Lock Detection: Verify Counts + Hysteresis, M-PSK Receiver — Pull-in, Lock, and BER, MPSK Receiver, SymbolSync Timing Lock Detector
- Spread spectrum (DSSS): AsyncDsssReceiver and AsyncDsssPool — continuous DSSS, from spec to object, AsyncDsssReceiver and AsyncDsssPool — the measurement record, DSSS Burst Acquisition, Streaming Async Despreader
- Channel coding: Name Your Own Code — and What Happens Past the Radius, The FEC Receive Half
- Bit-error rate (BER): Measuring an Error Rate, Defensibly
- Utilities: API taxonomy: the DSP building-block hierarchy and its naming axis, The Exponential Moving Average