Hi Bravo,
Advanced Navigation is an Australian company with your friend and mine Malcolm Turnbull on the board.
Given the newly announced JD with MBDA/NILEQ, BRN should contact them urgently, because they wouldn't want one hand tied behind their back by using obsolete technology.
www.advancednavigation.com
Since its inception in the 1960s, the Kalman filter has been commonly used to this day for guidance and navigation applications. It has undergone many adjustments designed to improve upon the basic implementation, such as the extended and unscented Kalman filter. In recent years, however, a new approach to filtering based on artificial neural network (ANN) processing has made significant breakthroughs that have pushed the inertial navigation industry into a new era.
Up until recently, little has been concretely achieved in the space of artificial intelligence (AI) for inertial navigation applications, until Advanced Navigation began commercializing a fusion neural network from university research in 2012.
The stakes have been further raised with the widespread use of GNSS jamming and spoofing technologies. This is forcing defence organizations to move away from GNSS-only solutions for position information and, instead, adopt inertial navigation systems (INS) solutions that can provide the necessary precision and reliable dead-reckoning performance.
How does an Artificial Neural Network (ANN) Work?
At its core, an artificial neural network has self-learning capabilities that enable it to convert inputs from various sensors into better resulting outputs as more data becomes available, over time. More precisely, a typical ANN goes through two distinct phases.
As LSTM operates over a long timespan, it is relatively insensitive to gap length as an advantage over the hidden Markov model generally associated with Kalman filters.
Advanced Navigation’s ANN relies on three types of memory:
They mention optic fibre (OF) navigation. I assume they are using the Sagnac effect which measures the phase difference in light beams travelling in opposite directions around an OF loop to detect changes in direction.
https://en.wikipedia.org/wiki/Sagnac_effect
Advanced Navigation is an Australian company with your friend and mine Malcolm Turnbull on the board.
Given the newly announced JD with MBDA/NILEQ, BRN should contact them urgently, because they wouldn't want one hand tied behind their back by using obsolete technology.
How is AI Revolutionising Inertial Navigation? | Advanced Navigation
Learn how AI is revolutionizing inertial navigation today here at Advanced Navigation. Read more here.
www.advancednavigation.com
Since its inception in the 1960s, the Kalman filter has been commonly used to this day for guidance and navigation applications. It has undergone many adjustments designed to improve upon the basic implementation, such as the extended and unscented Kalman filter. In recent years, however, a new approach to filtering based on artificial neural network (ANN) processing has made significant breakthroughs that have pushed the inertial navigation industry into a new era.
Up until recently, little has been concretely achieved in the space of artificial intelligence (AI) for inertial navigation applications, until Advanced Navigation began commercializing a fusion neural network from university research in 2012.
The stakes have been further raised with the widespread use of GNSS jamming and spoofing technologies. This is forcing defence organizations to move away from GNSS-only solutions for position information and, instead, adopt inertial navigation systems (INS) solutions that can provide the necessary precision and reliable dead-reckoning performance.
How does an Artificial Neural Network (ANN) Work?
At its core, an artificial neural network has self-learning capabilities that enable it to convert inputs from various sensors into better resulting outputs as more data becomes available, over time. More precisely, a typical ANN goes through two distinct phases.
- An initial phase, where processing units making up the ANN are “taught” a set of learning rules used to guide outcomes, recognize patterns in data by comparing actual output produced with the desired output.
- A second phase, where corrections (referred to as back-propagation) are applied to the actual data to achieve the desired output.
As LSTM operates over a long timespan, it is relatively insensitive to gap length as an advantage over the hidden Markov model generally associated with Kalman filters.
Advanced Navigation’s ANN relies on three types of memory:
- In the lab, long-term learning is hardcoded in the inference engine, based on many hours of testing in various environments.
- In the field, short-term learning operates to update the model in the inference engine twice per second. This learning is more constrained and offers what we call “medium level learning”.
- Once per minute “deep learning” operates across all sensor data, to self-model the system in order to make the most complex updates to the learned model.
They mention optic fibre (OF) navigation. I assume they are using the Sagnac effect which measures the phase difference in light beams travelling in opposite directions around an OF loop to detect changes in direction.
https://en.wikipedia.org/wiki/Sagnac_effect
YouTube·PROPHESEE Metavision Technologies·29 Feb 2024
( hehehe ) hope ChatGPT is wrong in this instance in saying that we're not working with Zinn Labs. I think there's a pretty good chance that we are. It certainly would be TENNs out of ten if we were! 
