#science

So far, we have been using linear Kalman filters to track reasonably simple problems. However, more complex problems require more complex Kalman filters. One of the primary limitations of the Kalman filter is that it is not capable of handling non-linear situations very well. In this example, we will be tracking a cann

In previous blogs, we looked at objects falling under gravity. In those blogs we asked the Kalman filter to try and estimate the position, velocity and acceleration from the readings. However, we know in advance what gravity is doing. So, why don't we instead tell the Kalman filter that the object is accelerating? That

I've already published two blogs on practical applications of the Kalman filter. In both of those blogs, I use the somewhat throw away line "simply solve the Riccati equations". If you are not familiar with the Riccati equations then this line could be very confusing indeed. So I hope to clear up any confusio

In a previous blog entry we looked at tracking a falling object. That was focused on the practical implementation. This time we will cover tracking an object moving at constant velocity but focus more on the actual derivation of the filter. Let's start with the differential state-space equations.\[ \begin{bmatrix} \dot

In this blog we will track a falling object using a Kalman filter. Our goal will be to create a simulation of a falling object, and filter that with the Kalman filter. We'll plot the residuals and see how well we can reconstruct the original signal from the noisey signal. We'll code this in Rust, which is a great choic

Zoé Ziani was working on her PhD thesis in the field of behavioural psychology. She was looking at a particular paper "CGK 2014" (an abbreviation for The Contaminating Effects of Building Instrumental Ties: How Networking Can Make Us Feel Dirty). This paper is about how individuals can feel "pysically d