Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Filtro Adattato× | Filtro LMS Adattivo× | |
|---|---|---|
| Campo | Elaborazione dei segnali | Elaborazione dei segnali |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1943 | 1960 |
| Ideatore≠ | D. O. North | Bernard Widrow and Marcian E. Hoff |
| Tipo≠ | Optimal filter for signal detection | Gradient descent adaptive filtering |
| Fonte seminale≠ | North, D. O. (1943). An Analysis of the Factors Which Determine Signal/Noise Discrimination in Pulsed Carrier Systems. RCA Laboratories, Technical Report PTM-946. link ↗ | Widrow, B., & Hoff, M. E. (1960). Adaptive Switching Circuits. IRE Wescon Convention Record, 4, 96–104. link ↗ |
| Alias | Correlation Detector, Optimal Filter Detection, Template Matching | LMS Filter, Adaptive LMS Algorithm, Gradient Descent Filtering |
| Correlati | 4 | 4 |
| Sintesi≠ | The matched filter is an optimal signal detector that maximizes the signal-to-noise ratio (SNR) for detecting a known signal in additive Gaussian noise. Developed by D. O. North during World War II for radar applications, the matched filter represents the optimal linear filter for signal detection and remains the foundation for detection theory and digital communications. | The Least Mean Squares (LMS) filter is an adaptive signal processing algorithm that continuously updates filter coefficients to minimize the squared error between the filter output and a desired signal. Introduced by Bernard Widrow and Marcian Hoff in 1960, the LMS algorithm is one of the most widely used adaptive filtering techniques due to its simplicity, low computational cost, and ability to track time-varying signals. |
| ScholarGateInsieme di dati ↗ |
|
|