MATLABTECH

The Kalman Filter

An optimal estimation algorithm that computes the hidden state of a dynamic system from a series of noisy sensor measurements. By continuously iterating through a feedback loop of mathematical predictions and real-world updates, it achieves highly accurate state estimates. It is an essential component in modern control architecture, powering advanced applications such as autonomous robotic navigation, sensor fusion, and electric vehicle battery management systems.

Kalman Filter Control System Architecture

Feedback loop mapping the Real-World System to the Estimation Algorithm

PHYSICAL ENVIRONMENT KALMAN FILTER ALGORITHM uk Control wk xk True State vk zk Meas. xฬ‚k A Priori xฬ‚k A Posteriori xฬ‚k-1 System Model x=Fx+Bu+w Sensor Model z=Hx+v PREDICT Time Update UPDATE Measurement z-1 Unit Delay

Prior State Estimate (Prediction)

Sensor Measurement

1D Kalman Filter Algorithm

ฮผ = State Estimate (Mean) ฯƒยฒ = Uncertainty (Variance) u = Control Input Q = Process Noise Variance z = Sensor Measurement R = Meas. Noise Variance K = Kalman Gain
1. Predict (Time Update)
ฮผp = ฮผprev + u
ฮผp = 0.0 + 1.0 = 0.0
ฯƒ2p = ฯƒ2prev + Q
ฯƒ2p = 0.0 + 3.0 = 0.0
2. Kalman Gain
K = ฯƒ2p / (ฯƒ2p + R)
K = 0.0 / (0.0 + 25.0) = 0.00
3. Update (Measurement)
Measurement (z) = 0.0

ฮผnew = ฮผp + K * (z – ฮผp)
ฮผnew = 0.0 + 0.0 * (0.0) = 0.0
ฯƒ2new = (1 – K) * ฯƒ2p
ฯƒ2new = (1.0) * 0.0 = 0.0

Posterior State Estimate (Updated)