ukfan

Run one update of a basic unscented (sigma point) Kalman filter where the process and measurement noise are additive. This runs faster than a traditional UKF and, because the propagation and observation functions do not require inputs for the process and measurement noise, is a drop-in replacement for an extended Kalman filter.

[x_k, P_k] = ukfan( ...
t_km1, t_k, x_km1, P_km1, u_km1, z_k, ...
f, h, Q_km1, R_k, ...
alpha, beta, kappa, ...
(etc.))

Inputs

t_km1 Time at sample k-1 Time step from sample k-1 to k State estimate at sample k-1 Estimate covariance at sample k-1 Input vector at sample k-1 Measurement at sample k Propagation function with interface: x_k = f(t_km1, t_k, x_km1, u_km1, q_km1) where q_km1 is the process noise at sample k-1 Measurement function with interface: z_k = h(t_k, x_k, u_km1, r_k) where r_k is the measurement noise at sample k Pocess noise covariance at sample k-1 Measurement noise covariance at sample k Optional turning parameter, often 0.001 Optional tuning parameter, with 2 being optimal for Gaussian estimation error Optional tuning parameter, often 3 - nx, where nx is the dimension of the state Additional arguments to be passed to f and h after their normal arguments

Outputs

x Upated estimate at sample k Updated estimate covariance at sample k

Example

We can quickly create a simulation for discrete, dynamic system, generate noisy measurements of the system over time, and pass these to an unscented Kalman filter.

First, define the discrete system.

rng(1);
dt    = 0.1;                                  % Time step
F_km1 = expm([0 1; -1 0]*dt);                 % State transition matrix
G_km1 = [0.5*dt^2; dt];                       % Process-noise-to-state map
Q_km1 = G_km1 * 0.5^2 * G_km1.';              % Process noise variance
R_k   = 0.1;                                  % Meas. noise covariance

Make propagation and observation functions. These are just linear for this example, but ukf is meant for nonlinear functions.

f = @(t_km1, t_k, x_km1, u_km1) F_km1 * x_km1;
h = @(t_k, x_k, u_k) x_k(1);

Now, we'll define the simulation's time step and initial conditions. Note that we define the initial estimate and set the truth as a small error from the estimate (using the covariance).

n       = 100;                     % Number of samples to simulate
x_hat_0 = [1; 0];                  % Initial estimate
P       = diag([0.5 1].^2);        % Initial estimate covariance
x_0     = x_hat_0 + mnddraw(P, 1); % Initial true state

Now we'll just write a loop for the discrete simulation.

% Storage for time histories
x     = [x_0, zeros(2, n-1)];                         % True state
x_hat = [x_hat_0, zeros(2, n-1)];                     % Estimate
z     = [x_0(1) + mnddraw(R_k, 1), zeros(1, n-1)];    % Measurement

% Simulate each sample over time.
for k = 2:n

% Propagate the true state.
x(:, k) = F_km1 * x(:, k-1) + mnddraw(Q_km1, 1);

% Create the real measurement at sample k.
z(:, k) = x(1, k) + mnddraw(R_k, 1);

% Run the Kalman correction.
[x_hat(:,k), P] = ukfan((k-1)*dt, dt, x_hat(:,k-1), P, [], z(:,k), ...
f, h, Q_km1, R_k);

end

Plot the results.

figure(1);
clf();
t = 0:dt:(n-1)*dt;
plot(t, x, ...
t, z, '.', ...
t, x_hat, '--');
legend('True x1', 'True x2', 'Meas.', 'Est. x1', 'Est. x2');
xlabel('Time');

Note how similar this example is to the example of ukf.

Reference

Wan, Eric A. and Rudoph van der Merwe. "The Unscented Kalman Filter." Kalman Filtering and Neural Networks. Ed. Simon Haykin. New York: John Wiley & Sons, Inc., 2001. Print. Pages 221-276.