I/Q signals as the gateway to DSP

Two Birds with One Tone: I/Q Signals and Fourier Transform – Part 1

When a new member arrives at the DSP club, this is what they find at the club gate: I/Q signals. Perhaps a secret plot to keep most people out of the party? Some return from here to try another area (e.g., machine learning, which pays more and is easier to understand but less interesting than DSP). Others persist enough to push the gate open (even a little understanding is sufficient for this task). So what exactly makes this topic so mysterious? To investigate the answer, we start with an example audio signal drawn in the figure below that displays amplitude

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Theory of relativity and age of planet Earth

The Easiest Tutorial on Kalman Filter

Kalman filter is one of the most important but not so well explained filter in the field of statistical signal processing. As far as its importance is concerned, it has seen a phenomenal rise since its discovery in 1960. One of the major factors behind this is its role of fusing estimates in time and space in an information-rich world. For example, position awareness is not limited to radars and self driving vehicles anymore but instead has become an integral component in proper operation of industrial control, robotics, precision agriculture, drones and augmented reality. Kalman filter plays a major role

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Pied Piper of Hamelin

AI – An Advanced Civilization or a One-Trick Pony

Oct 19, 2024 Author’s Note This article, unlike the others on this website, is not about how some AI algorithms work. Instead, it is a personal opinion on AI and the future of our world. My hope is to generate more discussions on AI from this perspective. In such an undertaking, it is likely that I have made mistakes and failed to consider some critical aspect of the whole picture. Please feel free to comment and help me learn more. After some false starts, we are witnessing the true dawn of Artificial Intelligence (AI) today. Many people, including high profile

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Bat echolocation principle

FMCW Radar Part 1 – Ranging

This is Part 1 of a 3-Part series in which we describe how an FMCW radar finds the range of multiple stationary targets. In Part 2, we talk about estimating the velocities of several moving targets and their directions through forming a structure known as the radar cube. Part 3 presents system design guidelines for an FMCW radar. In his book Multirate Signal Processing, Fred Harris mentions a great problem solving technique: "When faced with an unsolvable problem, change it into one you can solve, and solve that one instead." We will see in this article how an FMCW radar

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Feedback AGC block diagram

How Automatic Gain Control (AGC) Works

Alfred North Whitehead said, "Civilization advances by extending the number of important operations which we can perform without thinking of them." In today’s world, it is easy to take no notice of the level of process automation integrated into our lives. To have an idea of how things were in the early days, signal processing technology to sort out the radar picture on a map was not available and only a dot or a line could be generated on the screen representing a detected target. A radar operator had to stare at a screen for their whole shift to raise

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