kalman

class gps_helper.kalman.DvKalman(initial_state=[0, 20])

Kim Chapter 11.2 Velocity from Position Estimation

Python > 3.5 is assumed so the operator @ can be used for matrix multiply

Mark Wickert December 2017

Methods

next_sample(z)

Update the Kalman filter state by inputting a new scalar measurement.

next_sample(z)

Update the Kalman filter state by inputting a new scalar measurement. Return the state array as a tuple Update all other Kalman filter quantities

class gps_helper.kalman.IntKalman(initial_state=[0, 20])

Kim Chapter 11.4 Position from Velocity Estimation

Python > 3.5 is assumed so the operator @ can be used for matrix multiply

Mark Wickert December 2017

Methods

next_sample(z)

Update the Kalman filter state by inputting a new scalar measurement.

next_sample(z)

Update the Kalman filter state by inputting a new scalar measurement. Return the state array as a tuple Update all other Kalman filter quantities

class gps_helper.kalman.PosKalman(Q, R, initial_state=[0, 20], dt=0.1)

Position Estimation from Position and Velocity Measurements

Python > 3.5 is assumed so the operator @ can be used for matrix multiply

Mark Wickert May 2018

Methods

next_sample(z)

Update the Kalman filter state by inputting a new scalar measurement.

next_sample(z)

Update the Kalman filter state by inputting a new scalar measurement. Return the state array as a tuple Update all other Kalman filter quantities

class gps_helper.kalman.RadarEKF(dt=0.05, initial_state=[0, 90, 1100])

Kim Chapter 14.4 Radar Range Tracking

Python > 3.5 is assumed so the operator @ can be used for matrix multiply

Mark Wickert December 2017

Methods

Hjacob(xp)	Jacobian used to linearize the measurement matrix H given the state vector
hx(xhat)	State vector predicted to slant range
next_sample(z)	Update the Kalman filter state by inputting a new scalar measurement.

Hjacob(xp)

Jacobian used to linearize the measurement matrix H given the state vector

hx(xhat)

State vector predicted to slant range

next_sample(z)

Update the Kalman filter state by inputting a new scalar measurement. Return the state array as a tuple Update all other Kalman filter quantities

class gps_helper.kalman.RadarUKF(dt=0.05, initial_state=[0, 90, 1100])

Kim Chapter 15.4 Radar Range Tracking UKF Version

Python > 3.5 is assumed so the operator @ can be used for matrix multiply

Mark Wickert December 2017

Methods

fx(x)	The function f(x) in Kim
hx(x)	The range equation r(x1,x3)
next_sample(z[, kappa])	Update the Kalman filter state by inputting a new scalar measurement.

fx(x)

The function f(x) in Kim

hx(x)

The range equation r(x1,x3)

next_sample(z, kappa=0)

Update the Kalman filter state by inputting a new scalar measurement. Return the state array as a tuple Update all other Kalman filter quantities

class gps_helper.kalman.SimpleKalman(dt=0.2, initial_state=14, P=6)

Kim Chapter 10.2 Battery voltage estimation with measurement noise

Python > 3.5 is assumed so the operator @ can be used for matrix multiply

Mark Wickert February 2018

Methods

next_sample(z)

Update the Kalman filter state by inputting a new scalar measurement.

next_sample(z)

Update the Kalman filter state by inputting a new scalar measurement. Return the state array as a tuple Update all other Kalman filter quantities

gps_helper.kalman.sigma_points(xm, P, kappa)

Calculate the Sigma Points of an unscented Kalman filter

Mark Wickert December 2017 Translated P. Kim’s program from m-code

gps_helper.kalman.ut(Xi, W, noise_cov=0)

Unscented transformation

Mark Wickert December 2017 Translated P. Kim’s program from m-code