Kalman Filter
In a dynamic system, there is uncertain information. To capture the uncertainty, yet another mathematical tool, called the Kalman filter, comes into play. One can utilize the Kalman filter to optimally estimate the system’s next state, and it is ideal for continuously changing systems. It is especially useful for handling noisy sensor data by collating sensor data to best estimate the parameter of interest. In other words, the Kalman filter is an estimator of the system’s states in the presence of imprecise and uncertain measurements. It is mostly useful for the estimation of unobserved variables in real time.
The Kalman filter algorithm is widely used in signal processing, target tracking, navigation, and control applications. In tracking and control systems, an accurate and precise estimation of location and velocity, which are hidden (unknown) states, is a challenge. The uncertainty in the measurement of hidden states is attributed to external factors...