Since the 1960s, the Kalman filter has been the standard for guidance and navigation applications. However, the inertial navigation industry has experienced a significant shift in recent years with the rise of filtering based on artificial neural network (ANN) processing.
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Although there has been little progress in the space of artificial intelligence (AI) for inertial navigation applications, Advanced Navigation’s fusion neural network, commercialised from university research in 2012, has raised the stakes.
Defence organisations are moving away from GNSS-only solutions for position information, instead adopting inertial navigation systems (INS) solutions that provide the necessary precision and reliable dead-reckoning performance due to the widespread use of GNSS jamming and spoofing technologies.
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.
Advanced Navigation’s solution uses the long short-term memory (LSTM) AI principle, which is well-suited to classifying, processing, and making predictions based on sensor data with a variable duration between important events. It relies on three types of memory: long-term learning, short-term learning, and deep learning.
The ANN filter is more accurate in sensor error tracking and provides integrity monitoring that is far superior to traditional filters. It is capable of rejecting erroneous measurements and adjusting accuracies for inconsistent data at a much deeper level. This gives the system greater performance in difficult conditions, especially noticeable in multi-path (reflected signals) GNSS conditions, such as urban canyons.
The use of non-linear constraints with ANN allows air jordan 1 high skyline for a far more thorough, real-world dynamic vehicle motion model, which in turn results in better tracking of errors, more reliable data, and higher accuracy than the traditional linear constraints.
Advanced Navigation’s INS offerings are developed on high-end microprocessors, which operate at relatively low power, due to the implementation of a highly constrained AI learning model.
Their breakthrough innovations are available in several IMU and INS solutions that are very competitive in terms of minimal size, weight, and power consumption. Field tests have shown the real-world performance of Advanced Navigation INS solutions to be of high quality.
Kumbirai is a GIS & MEAL specialist using geospatial analytics nike roshe runs cute girls shoes vans high tops to advance global health and social impact. A certified Data Protection Officer (DPO), an open-data advocate and self-taught software developer, he builds web GIS tools that turn field data into decisions. He lectures in GIS/Remote Sensing and mentors emerging practitioners. Founder of a geospatial startup and nonprofit, he believes, “Real geospatial innovation happens when we empower communities with the right tools and knowledge.” Open to consulting and collaborations.