期刊
IEEE SIGNAL PROCESSING LETTERS
卷 27, 期 -, 页码 1005-1009出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2020.3000679
关键词
Kalman filters; Vehicle dynamics; Markov processes; Standards; Neural networks; Dynamics; Mathematical model; Dynamic models; single target tracking; long short-term memory; recurrent neural network; Kalman filter
资金
- German Federal Ministry of Defence (BMVg)
Analytic dynamic models for target estimation are often approximations of the potentially complex behaviour of the object of interest. Its true motion might depend on hundreds of parameters and can involve long-term temporal correlation. However, conventional models keep the degrees of freedom low and they usually assume the Markov property to reduce computational complexity. In particular, the Kalman Filter assumes prior and posterior Gaussian densities and is hence restricted to linear transition functions which are often insufficient to reflect the behaviour of a real object. In this letter, a Mnemonic Kalman Filter is introduced which overcomes the Markov property and the linearity restriction by learning to predict a full transition probability density with Long Short-Term Memory networks.
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