editor's blog
Subscribe Now

Modern Dead Reckoning

VTI Technologies recently announced a project they worked on with Tampere University of Technology in Finland to improve navigation for both cars and pedestrians in environments where GPS may not be reliable. This can particularly be the case in dense urban settings where the GPS signal may be blocked or too distorted to be useful. With the growth in location-based services, even if you may not care what your exact position is, someone else does. Without GPS, dead reckoning is required to figure out where you are.

Dead reckoning refers to navigation where you have no clear points of reference along the way; you know your starting point, and based on your speed and direction, you calculate where you are at any time. The problem is that each of those calculations has some error and that, even if small, such errors accumulate over time and can make you think you’re somewhere you’re not. Before old-school gyroscopes, ships navigated by dead reckoning across the oceans, so you can imagine that, with very little in the way of reference points (except the stars, whose east-west position is confounded with time, making longitude hard to figure out), they might have been surprised when they finally caught sight of land.

Today GPS provides a golden reference point, but dead reckoning can be used in the gaps where GPS isn’t available.

I talked with VTI’s Ulf Meriheinä to understand better how this works. It’s a fusion of a number of technologies that come together to complement each other. The pieces of the dead-reckoning puzzle are:

–          Access to speed information; this could be from the speedometer signal in a car or from a pedometer signal of some sort on a pedestrian (say on the chest-belt of a runner)

–          A gyroscope

–          A digital roadmap

–          A simulation algorithm called “particle filtering”

The speedometer signal tells you how far you’ve gone in a straight line. There is an error of around 1% or less in this calculation. The longer you go without turning, the more linear error might accumulate.

Once you turn, the gyroscope detects that turn. So it can now help correct any accumulated linear error if it can determine accurately where you turned. And that’s where the map and simulations come in.

As you progress, your position is being correlated with the map. If you assume you can’t go off-road, then the number of places you might have turned becomes much smaller. In more technical terms, the probability density of your location gets compartmentalized, if you wish, to a few discrete possibilities.

This is where the simulation comes in. Particle filtering is a Monte Carlo variant where an overall probability density is partitioned into a discrete number of “particles.” As the simulation progresses, some of the particles are calculated as less and less likely and are filtered out, ultimately leaving one. Based on this, they system decides where you turned on the map.

Given that information, you can now reset your dead reckoning reference point to that value and restart your calculations as you continue forward. All previous errors disappear because the new location point isn’t based on your navigation calculations, but on deciding where on the map you are and picking the data from there.

VTI’s point with this is that you need a (or, more specifically, their) very accurate gyroscope for this to work so that those subtle changes in direction that we don’t notice, and which sometimes get us completely turned around, don’t escape detection and get this system all turned around.

Once GPS kicks back in, then you’re back onto that as a reference.

Leave a Reply

featured blogs
Nov 25, 2020
It constantly amazes me how there are always multiple ways of doing things. The problem is that sometimes it'€™s hard to decide which option is best....
Nov 25, 2020
[From the last episode: We looked at what it takes to generate data that can be used to train machine-learning .] We take a break from learning how IoT technology works for one of our occasional posts on how IoT technology is used. In this case, we look at trucking fleet mana...
Nov 25, 2020
It might seem simple, but database units and accuracy directly relate to the artwork generated, and it is possible to misunderstand the artwork format as it relates to the board setup. Thirty years... [[ Click on the title to access the full blog on the Cadence Community sit...
Nov 23, 2020
Readers of the Samtec blog know we are always talking about next-gen speed. Current channels rates are running at 56 Gbps PAM4. However, system designers are starting to look at 112 Gbps PAM4 data rates. Intuition would say that bleeding edge data rates like 112 Gbps PAM4 onl...

featured video

AI SoC Chats: Scaling AI Systems with Die-to-Die Interfaces

Sponsored by Synopsys

Join Synopsys Interface IP expert Manmeet Walia to understand the trends around scaling AI SoCs and systems while minimizing latency and power by using die-to-die interfaces.

Click here for more information about DesignWare IP for Amazing AI

featured paper

Top 9 design questions about digital isolators

Sponsored by Texas Instruments

Looking for more information about digital isolators? We’re here to help. Based on TI E2E™ support forum feedback, we compiled a list of the most frequently asked questions about digital isolator design challenges. This article covers questions such as, “What is the logic state of a digital isolator with no input signal?”, and “Can you leave unused channel pins on a digital isolator floating?”

Click here to download the whitepaper

Featured Chalk Talk

TensorFlow to RTL with High-Level Synthesis

Sponsored by Cadence Design Systems

Bridging the gap from the AI and data science world to the RTL and hardware design world can be challenging. High-level synthesis (HLS) can provide a mechanism to get from AI frameworks like TensorFlow into synthesizable RTL, enabling the development of high-performance inference architectures. In this episode of Chalk Talk, Amelia Dalton chats with Dave Apte of Cadence Design Systems about doing AI design with HLS.

More information