
Correlating data with the expected shape of the signal — the optimal detector of a known waveform in Gaussian noise.A matched filter is a signal processing technique that detects the presence of a known waveform in a noisy signal. It is the optimal linear detector when the noise is Gaussian and white (uniformly distributed across frequencies). The filter works by correlating the incoming data with a template of the expected signal. If the signal is present, the correlation produces a sharp peak; if absent, the output stays low. This is the standard method for detecting weak, known signals buried in noise.
The concept was developed during World War II for radar applications, with key contributions from Dwight North in 1943, who derived the filter that maximizes the output signal-to-noise ratio for a known signal in additive noise.
The matched filter is essentially a cross-correlation: you multiply the received signal point-by-point with a time-reversed copy of the expected waveform, then sum the products. This is equivalent to convolving the signal with the time-reversed template. When the template aligns with an actual occurrence of the signal, the products add constructively, producing a large output. The filter's impulse response is the time-reversed and shifted version of the signal, which maximizes the signal-to-noise ratio at the moment of detection.
Used in radar and sonar detection, digital communications (e.g., CDMA), and gravitational wave astronomy to identify the chirp signals from merging black holes.
The matched filter is so fundamental that it is used in barcode scanners to read the pattern of black and white stripes.











