Improving the Rainbow – Part 2

In the previous part of this tutorial, Improving the Rainbow – Part 1, we have seen different techniques to reproduce the colours of the rainbow procedurally. Solving this problem efficiently will allow us to simulate physically based reflections with a much higher fidelity.

The purpose of this post is to introduce a novel approach that yields better results than any of the previous solutions, without using any branching.

You can find the complete series here:

A link to download the Unity project used in this series is also provided at the end of the page.

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Positioning and Trilateration

This post shows how it is possible to find the position of an object in space, using a technique called trilateration. The traditional approach to this problem relies on three measurements only. This tutorial addresses how to it is possible to take into account more measurements to improve the precision of the final result. This algorithm is robust and can work even with inaccurate measurements.

If you are unfamiliar with the concepts of latitude and longitude, I suggest you read the first post in this series: Understanding Geographical Coordinates.

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Understanding Geographical Coordinates

This series introduces the concept of trilateration. This technique can be applied to a wide range of problems, from indoor localisation to earthquake detection. This first post provides a general introduction to the concept of geographical coordinates, and how they can be effectively manipulated. The second post in the series, Positioning and Trilateration, will cover the actual techniques used to identify the position of an object given independent distance readings. Most trilateration tutorials require the measures from the sensors to be precise and consistent. The approach here presented, instead, is highly robust and can tolerate inaccurate readings.

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