Bandingkan metode

	Filter Kalman untuk Pelacakan Sinyal	Filter Wiener
Bidang	Pemrosesan Sinyal	Pemrosesan Sinyal
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1960	1949
Pencetus	Rudolf E. Kalman	Norbert Wiener
Tipe	Recursive optimal filter	Linear mean-square optimal filter
Sumber perintis	Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1), 35–45.	Wiener, N. (1949). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. John Wiley & Sons.
Alias	Kalman Filtering, Recursive State Estimation, Optimal Filtering	Wiener Optimal Filter, Kolmogorov-Wiener Filter, Mean-Square Optimal Filter
Terkait	4	4
Ringkasan	The Kalman filter is a recursive algorithm that optimally estimates the state of a linear dynamic system from noisy measurements, minimizing mean-square error. Introduced by Rudolf Kalman in 1960, it revolutionized control theory, navigation, and signal processing by enabling real-time optimal estimation for time-varying systems. The Kalman filter became indispensable for spacecraft tracking, GPS navigation, and countless modern applications.	The Wiener filter is an optimal linear filter that minimizes mean-square error between the desired signal and the filter output given knowledge of signal and noise statistics. Developed by Norbert Wiener in 1949, it provides the theoretical foundation for optimal filtering and remains the benchmark against which all other linear filtering methods are compared.