Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation [Lecture Notes]

By Ramsey Faragher. IEEE Signal Processing Magazine, 2012

528 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/edf3309ee3f45f02f7344f5f5e6731b479b0e8c2

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In Aerospace Engineering

1. Kangkang Jiang
2. Yanran Li
3. Junhao Zhu
4. Hao Chen
5. Zhipeng Liang

In Artificial Intelligence

1. Kangkang Jiang
2. Yanran Li
3. Junhao Zhu
4. Hao Chen
5. Zhipeng Liang

Citations in the corpus

• Adversarial Deep Reinforcement Learning in Portfolio Management (2018, methodology): “We can then choose actions by solving the following optimization problem: (at, . . . , at+H−1) = arg max at,...,at+H−1 t+H−1∑ t′=t γt ′−tr(st′ , at′) What’s more, due to the fact that neural network is sensitive to the quality of data, traditional financial data noise reduction approaches can be uti”