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What I would definitely check is this line self.P = self.P - dot(self.K, dot(IS, self.K.T)) I tried to reformulate this, and I think it does not match the update equation for the covariance. Usually people use one of the following: $P = \left(I - K C\right)P\left(I - K H\right)^T + KRK^T$ - this is numerically more stable. $P = \left(I - K C\right)P$ ...

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If superposition works, then independent mode/component extraction is of interest. Synchrosqueezing is well-suited for this task. Extracted features can ten be fed to an anomaly detection system - optionally with Gaussianization. Other methods can be applied to the extracted components as if they were individual signals, so the described approach is ...

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Summary You can have only one (or no) primary sensor which is used in the prediction process. All other sensors are secondary and only used for corrections. Each correction takes the current state and a measurement from one of the secondary sensors and outputs the corrected state. You can run corrections as often as you want, typically each time one of the ...

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