Summary of Ai-aided Kalman Filters, by Nir Shlezinger et al.
AI-Aided Kalman Filters
by Nir Shlezinger, Guy Revach, Anubhab Ghosh, Saikat Chatterjee, Shuo Tang, Tales Imbiriba, Jindrich Dunik, Ondrej Straka, Pau Closas, Yonina C. Eldar
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP); Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A tutorial-style overview of design approaches for incorporating artificial intelligence (AI) in aiding Kalman filter (KF)-type algorithms is provided, showcasing the fusion of DNNs with classic KFs. The article reviews generic and dedicated deep neural network (DNN) architectures suitable for state estimation, highlighting task-oriented and SS model-oriented design approaches that leverage partial state-space modeling and data. A qualitative and quantitative study investigates the usefulness of each approach in preserving individual strengths of model-based KFs and data-driven DNNs. The code is publicly available, illustrating gains from hybrid model-based/data-driven designs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to combine artificial intelligence (AI) with classic Kalman filter algorithms. It shows that AI can help improve the accuracy of state estimation in dynamic systems. The article discusses different ways to design these combined systems and shares a study that compares their effectiveness. It also provides the code for readers to try out. |
Keywords
» Artificial intelligence » Neural network
