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Previously when I have implemented Kalman filters I have used the transformation

$$ \mathbf{A(t)} = \mathcal{L}^{-1} \left( s \mathbf{I} - \mathbf{F} \right) ^{-1} $$

to calculate the state transition matrix $\bf{A}$ from the system dynamics matrix $\bf{F}$.

Where the equation describing the state of the system is

$$ \vec{x_t} = \mathbf{F_t} \vec{x_{t-1}} + \mathbf{B_t} \vec{u_t} + \vec{w_t} $$

and my prediction step calculation is

$$ \vec{x_{k|k-1}} = \mathbf{A} \vec{x_{k-1|k-1}} + B\vec{u_k} $$

For example for simple sinusoidal Kalman filter started from the following equation of motion:

$ \ddot{x} = -\omega^2x $

So my matrix equation of motion was:

$$ \begin{bmatrix} \dot{x} \\ \ddot{x} \\ \end{bmatrix} = \begin{bmatrix} 0 & 1 \\ -\omega^2 & 0 \\ \end{bmatrix} . \begin{bmatrix} x \\ \dot{x} \\ \end{bmatrix} $$

and my system dynamics matrix was

$$ F = \begin{bmatrix} 0 & 1 \\ -\omega^2 & 0 \\ \end{bmatrix} $$

I then performed the transformation $ \mathbf{A(t)} = \mathcal{L}^{-1} \left( s \mathbf{I} - \mathbf{F} \right) ^{-1} $ to get the state transition matrix like so:

$$ A(t) = \begin{bmatrix} \cos(\omega t) & \dfrac{\sin(\omega t)}{\omega} \\ -\omega \sin(\omega t) & \cos(\omega t) \\ \end{bmatrix} $$

Where I then replaced $t$ with my sample time $T_s$ where $T_s = \dfrac{1}{F_s}$ where $F_s$ was my sample frequency.

I then used this to extract my sinusoidal signal.

My question is did I need to perform this transformation? And if I did, why did I have to do so?

I was reading the paper linked here where they attempt the explain the Kalman filter in simple way and they did no such transformation.

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  • $\begingroup$ It's not clear to me, but I suspect you're doing it because your "equation of motion" is not a discrete-time equation, but you're applying a discrete time state-space system. Perhaps it's a sampling step? The state transition matrix doesn't change like that when starting from a discrete-time state-space equation and applying the KF equations. $\endgroup$ – Peter K. May 18 '17 at 9:03
  • $\begingroup$ I was following the derivation of a sinusoidal kalman filter in Fundamentals of Kalman filtering : a practical approach 2nd ed. by Paul Zarchan where they perform this operation. I assume this approach is not necessary in general then to take any arbitrary F matrix to an A matrix? $\endgroup$ – SomeRandomPhysicist May 18 '17 at 9:13
  • $\begingroup$ I don't have the book to hand currently to check if he explains why this step was taken. $\endgroup$ – SomeRandomPhysicist May 18 '17 at 9:15
  • $\begingroup$ Provided everything is discrete, then the transformation is not needed, yes. $\endgroup$ – Peter K. May 18 '17 at 9:19
  • $\begingroup$ Are you aware of how this transformation takes you from the continuous-time state-space to the discrete-time state-space? $\endgroup$ – SomeRandomPhysicist May 18 '17 at 9:29
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As I said in the comments, there should be no need at all to do such a transformation if all of your equations start in discrete-time.

The equation $$ (s \mathbf{I} - \mathbf{F} )^{-1} $$ is just the Laplace transform of the solution for the state equation: $$ \dot{\vec{x_t}} = \mathbf{F} \vec{x_t} + \vec{w_t} $$

However, I really can't see how $\mathbf{F}$ works in: $$ \vec{x_t} = \mathbf{F_t} \vec{x_{t-1}} + \mathbf{B_t} \vec{u_t} + \vec{w_t} $$ because this is a discrete-time equation, and the equation you're looking at generating the sinewave is continuous-time.

Can you clarify?

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  • $\begingroup$ I'll need to check in the book from which I obtained the derivation, I don't have it to hand at the moment, but should have it in 4 hours. If $\left( s \mathbf{I} - \mathbf{F} \right)^{-1}$ is the Laplace transform, which I didn't realise, then the inverse laplace transform performed in $\mathcal{L}^{-1} \left( s \mathbf{I} - \mathbf{F} \right)^{-1}$ of this should bring you back to the original matrix. I presume that there is some trick being used to take it from continuous time to discrete time by Laplace transforming it and then performing the inverse transformation. $\endgroup$ – SomeRandomPhysicist May 18 '17 at 12:11
  • $\begingroup$ @SomeRandomPhysicist That'd be my guess, but you'd have to check the book to make sense of it. $\endgroup$ – Peter K. May 18 '17 at 12:35
  • $\begingroup$ The inverse Laplace transform will give you the homogeneous (non-forced) solution to the differential equation --- in this case, a sine wave. $\endgroup$ – Peter K. May 18 '17 at 13:54
  • $\begingroup$ In the book I worked from, the author states that for a time invariant systems dynamics matrix one can derive the state transition matrix by performing the transformation $\mathcal{L}^{-1}([s\mathbf{I}-\mathbf{F}]^{-1})$ and cites Kalman and Bucy's 1961 paper "New Results in Linear Filtering and Prediction Theory" $\endgroup$ – SomeRandomPhysicist May 18 '17 at 15:17
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    $\begingroup$ RIght, so that Python discussion uses equation (8), which makes sense (see under the section The Matrix Exponential). Nice writeup, that! $\endgroup$ – Peter K. May 18 '17 at 15:50
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The Laplace transform you show is equivalent to taking the matrix exponential of $\mathbf{F}$. Think about the properties of a continuous time state transition matrix, $\Psi(t,t)=I $, $$\Psi(t0,t1)\Psi(t1,t3)=\Psi(t0,t3),$$ and also that you can reverse time. $\mathbf{F}$ doesn't have those properties. It doesn't have to be invertible either.

$\mathbf{F}$ defines the continuous time differential equation at time $t$. It isn't the solution of the state at t+tau. One must also include the contribution of the control through $\mathbf{B}$, which is a bit more complicated. The solution is for $t$ doesn't assume that $t$ is evaluated at regular intervals but if we want to convert to discrete time, we evaluate at times corresponding to a discrete times.

If the state is discrete time and things like financial models are intrinsically discrete in time, that aren't derived from a continuous time system. One does not need to take the Laplace transform or matrix exponential. It only comes into play if the system was specified as a continuous time system

There is also the Kalman Bucy filter which is all continuous time.

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