created 09/02/2009            last update 26/08/2009 (vocabulary confusion: process noise is not the estimation noise) author: Claude Baumann
The scalar and discrete Kalman filter applied to Roger Rabbit

We have started an unusual way to introduce the rudimentary fundamentals of the discrete Kalman filter. Now we are going to explore some of the math behind. Note that we will concentrate on the statistical approach an we will only develop equations for the most simple scalar filter. Please refer to the Intuitive introduction to the scalar Kalman filter page, where we develop the Roger Rabbit experiments. 


1. State equation


Roger Rabbit's movement is approximated as a linear motion with constant speed. The reality diverges from this simplified view. Obviously there are small, but significant variations in speed as can be seen on (Fig.1). However the graphical description of Roger's motion does not correspond to reality either, because we know from Newton's laws that the velocity of a mass cannot change so abruptly. The graph suggests discontinuities through the interpolation lines between the sampled data-points that are drawn for each time step Dt. Any realistic plot of Roger's speed would appear much smoother. However, such a continuous description would presuppose infinitesimal time steps dt and, according to Cauchy's definition of a continuous function f, for any t and dt, there would exist a number e that . This continuity postulate would lead to the conclusion that two infinitesimally adjacent points are not independent of each other, because they always are localized in close proximity defined by dt and e.

In the digital-countable world we are accustomed to divide time into steps Dt. It is clear that the discretized function can no longer represent all the points between two adjacent points in time. There necessarily is a loss of information -although the sampling theorem teaches us that under certain conditions periodic functions can be entirely reproduced from the digitalization-. The time-discretization always requires a careful preliminary fixing of the time step in order to keep the losses of information as small as possible. By contrast, in many cases it is desired to keep the beat slow enough, so as to assure that any function-value depends at the utmost on its predecessor, or even more strictly that two adjacent points are statistically independent. Such a function or process then is called a Markov process. Both sampling requirements must be well balanced, when fixing the time step.

Fig. 1 : Roger Rabbit's speed suffers from noise. (mean m=1.76 m/s, variance Q=s2=0.55).

The discrete description of Roger Rabbit's path can be considered as a Markov process, because each new position only depends on the previous one. The new state therefore can be expressed in a stochastic difference equation that starts from the preceding step:

, ( 1 )

where x symbolizes the process-state, t the discrete point in time and w represents the noise that partly corrupts the presumed constant speed s. Dt is supposed to equal 1[sec]. The noise itself has zero mean and is normally distributed. If we generate a dozen of noise classes over the range [-2,2] and we count the number of times that a certain divergence w from the mean (m=1.76) belongs to one of the classes, we can draw a histogram. If the occurrences are divided by the total number of data, we obtain the relative frequency distribution that, due to the very small number of samples, only roughly follows the normal probability density distribution with variance Q=0.55 as it can be seen on (Fig. 2). (Take note that we suppose the noise to be white, which means that the wave-spectrum is equally distributed.) The recursive equation above suggests that we go back step by step down to negative infinity in time, which obviously doesn't make any sense. It is therefore assumed that the system starts at t=0 with an initial state value x(0)=0. The very first estimate also is set to zero.

Fig. 2 : The relative frequency distribution of the noise with the Gaussian probability density.

It is clear that we are not able to determine the state of Roger's actual location directly. We cannot yield the actual noise component either.


2. Measurement equation


  • P. S. MAYBECK, Stochastic models, estimation, and control, Vol. 1., Academic Press, N.Y., 1979. download by permission

We have measured Roger's path while estimating the noise variance to R=4. A typical relative frequency distribution of the measurement errors under these conditions is shown on (Fig. 3) together with the corresponding probability density function. The measurements are related to the real state through the equation:

, ( 2 )

where z symbolizes the actual measurement and v the measurement noise.

Fig. 3 : A typical relative frequency distribution of the measurement noise and the probability density.


3. State estimate equations


A rough estimate of Roger's position at t=12 can be deduced from the (bad) deterministic model x=1.5t, that underestimates Roger Rabbit's speed at sapprox=1.5m/s. The model equation is transformed into the difference equation:

, ( 3.1 )

where we use the symbol s rather than sapprox. Since we have no possibility to know s exactly at start, we must rely on our initial approximation and consider the error from the true value as part of the process noise. The a priori estimate can be produced as soon as we have computed the a posteriori estimate of the preceding point in time. Remember that one of the qualities of the Kalman filter is the faculty to predict the next system state. In the case of t=12 and , we get for example. We are aware that this a priori estimate is erroneous, but we have no idea about the variance of the error e-. So, let us define:

Note that we need to initialize the very first value of P- with any non-zero number, just as we did for the very first state prediction.

From the measurement we get the value with error variance R=4. Equation (2) may be rewritten as . Hence it can be concluded that the state estimation, based on the measurement alone, will exactly have the same probability density as the measurement itself. We may fuse both estimations into the a posteriori estimation by calculating the weighted average while using the variances as weights. The result is a better estimate of the state, since the conditional probability density has narrowed and the error variance has decreased (Fig. 4) . The question whether this fusion of the data is the best that we can do, may be answered positively. We now will prove this. If you are not familiar with the statistical term "expectation of a random variable E{X}", you may skip the proof or consult D. Joyce's Introduction to the Expectation for discrete random variables.

First let's express the a posteriori state estimation as the weighted average of the prediction and the measurement. In the following, for the better readability, we will omit the time references, because the values now all concern the point of time t. We also will abandon the notation from the preceding document about Roger Rabbit's path. Instead we are using the simple . In the general form we may write:

Now we will transform the variance of the a posteriori state estimation error e as a function of the variances of the prediction error and the measurement variance. It is important to take into consideration that the probability for the exact state to be situated within a certain range from the a priori predicted state is the same as the probability for the predicted state to be located within the same bounds from the exact state. In other words, there is an equivalence of considering the predicted state as the center of the probability density distribution under the condition of Roger's exact position with considering the true location as the center of the probability density distribution under the condition of the prediction. (The same is true for the measurement.) Note that we don't know the variance P- yet, but we prefixed it arbitrarily to 0.62. We can affirm that the expectation of both the measurement and the prediction is the true state x[t]. Although this proposition is easily proved by statistics, we intuitively know it from experience. In physics, if we repeat measurement experiments several times about the same state, the mean of the measurements approaches the true value. That the mean of the prediction can approach the true state is less evident. But, we know from Monte-Carlo methods that this also perfectly works. Random is a curious product, isn't it?


*** Note that this term is zero, because the prediction and the measurement are supposed to be statistically independent and therefore:

In order to minimize P, we solve the equation:

The value K is called the Kalman gain. Thus we can calculate the a posteriori estimate, assuming for this example that the a priori prediction error variance didn't change since t=0 and P-[12]=0.62:

This demonstration proves that the weights R and P- are optimal in that sense that they cannot be improved. The optimal Kalman gain also minimizes the estimation error variance. (In the following we will omit the suffix "opt", because the optimal character of the Kalman gain is explicit.)

Fig. 4 : Conditional probability density at t=12 based on the model prediction and the measurement. The green curve has better variance and is closer to the true state than the others. 


4. Estimation error equations


There still is one issue that not has been solved so far, although we were able to improve the state estimate by merging the model prediction with the measurement. The whole calculation has been founded on an estimation of the a priori process variance that has been prefixed arbitrarily. Because we don't know the state exactly, we have no direct possibility to deduce the degree of confidence of the a priori state estimate. In other words, since we cannot calculate the error between the true and the predicted state - and this value is essential in the equations above - we can not know, if our estimate is valuable or not. It is therefore essential to find a clear expression for the a priori process noise.

We now need to reintroduce the time reference, because values from the preceding time point appear in the equations:

Note that the last term is zero, because the measurement and the process noise are assumed to be uncorrelated.

If we rearrange (EQ. 3.4), then we can express R as a function of K and P- and rewrite the prediction noise equation with time reference omitted:


5. Convergence of the estimation noise sequence


The reader will certainly have noticed that EQ. (4.1) and (4.2) form a recursive system of equations. The first step predicts the a priori prediction noise variance from the preceding a posteriori variance, while the second step defines the actual a priori variance on the base of the actual a posteriori variance. We know from EQ. (3.4) that the Kalman gain directly depends on the a priori prediction variance, so we merge the three equations. Again we need the time reference here:

This equation represents a recursive sequence. In order to prove that this sequence converges to a finite limit, we must prove two things: first that the sequence is monotonous and second that it is bounded. Let's suppose for a moment that the sequence converges to a finite limit. This means that if t tends to , P[t]-P[t-1]=0. The limit can be calculated as follows:

Note that we only need to retain the positive root. If we define the function:


and we set P[t-1]=y, we observe that the sign of the numerator of f(y) serves as an indicator of the type of sequence. For instance, it proves that P[t]<P[t-1] in the case of y>ym and P[t]>P[t-1] for y<ym, and the sequence is monotonously increasing for any P[0]<ym and monotonously decreasing for any P[0]>ym. The trivial case, where P[0]=ym results in a constant sequence.

In order to prove that the sequence is bounded, we consider the function:

We have for all R,Q >0:

and the function g(y) is bounded in the interval

The consequence is that, in the case of invariable R and Q, the estimation error variance can be calculated at system start. The estimation noise variance reaches an optimum that cannot be improved. An important quality of the Kalman filter is that the noise variance sequence converges rather quickly. (Fig. 5 & 6) show the convergence of the process variance in the case of Roger Rabbit's first Kalman filtering. The error variance stays within the bounds:

and the limit is


Fig. 5 : Evolution of the error variance in the case of Q=0.55 and R=4 and P-[0]=0.01

Fig. 6 : The yellow curve represents the function g(y) -see text-. The green segments develop the sequence of the error variance.


6. The Kalman filter algorithm


The rudimentary discrete scalar Kalman filter that we described here is a recursive Markov process, where every new step depends on the previous alone. The algorithm can be divided into three major parts (cf. Fig. 7). Good initialization of the process variables helps increasing the convergence speed of the process noise variance. The prediction step is very useful, in order to forecast the next system state, especially in the case of slow system updates. The correction step throws in the measurements and adapts the Kalman filter process variables to the best verification with reality.

NOTES: The Roger Rabbit experiments demonstrate well that R and Q must not necessarily be constant throughout the process. If they change in time, so does the process noise and the variance starts varying accordingly. This does not affect the Kalman filter's minimizing and optimizing capabilities. If more measurement sources are present in the system, they are fused with a weighted averaging function that is very similar to the one developed in section 3 of this document (and that is well described in the Maybeck article.)