LeastSquaresFilter¶
Copyright 2015 Roger R Labbe Jr.
FilterPy library. http://github.com/rlabbe/filterpy
Documentation at: https://filterpy.readthedocs.org
Supporting book at: https://github.com/rlabbe/KalmanandBayesianFiltersinPython
This is licensed under an MIT license. See the readme.MD file for more information.

class
filterpy.leastsq.
LeastSquaresFilter
(dt, order, noise_sigma=0.0)[source]¶ Implements a Least Squares recursive filter. Formulation is per Zarchan [R12].
Filter may be of order 0 to 2. Order 0 assumes the value being tracked is a constant, order 1 assumes that it moves in a line, and order 2 assumes that it is tracking a second order polynomial.
It is implemented to be directly callable like a function. See examples.
References
[R12] (1, 2) Zarchan and Musoff. “Fundamentals of Kalman Filtering: A Practical Approach.” Third Edition. AIAA, 2009. Methods
Examples
from filterpy.leastsq import LeastSquaresFilter lsq = LeastSquaresFilter(dt=0.1, order=1, noise_sigma=2.3) while True: z = sensor_reading() # get a measurement x = lsq(z) # get the filtered estimate. print('error: {}, velocity error: {}'.format(lsq.error, lsq.derror))
Attributes
n (int) step in the recursion. 0 prior to first call, 1 after the first call, etc. K1,K2,K3 (float) Gains for the filter. K1 for all orders, K2 for orders 0 and 1, and K3 for order 2 x, dx, ddx: type(z) estimate(s) of the output. ‘d’ denotes derivative, so ‘dx’ is the first derivative of x, ‘ddx’ is the second derivative.