posted Jul 9, 2015, 6:38 PM by Javad Taghia
updated Jul 16, 2015, 2:09 AM
It's the most important step. First of all, you must be sure that, Kalman filtering conditions fit to your problem.
As we remember the two equations of Kalman Filter is as follows:
It means that each xk (our signal values) may be evaluated by using a linear stochastic equation (the first one). Any xk is a linear combination of its previous value plus a control signal uk and a process noise (which may be hard to conceptualize). Remember that, most of the time, there's no control signal uk.
The second equation tells that any measurement value (which we are not sure its accuracy) is a linear combination of the signal value and the measurement noise. They are both considered to be Gaussian.
The process noise and measurement noise are statistically independent.
The entities A, B and H are in general form matrices. But in most of our signal processing problems, we use models such that these entities are just numeric values. Also as an additional ease, while these values may change between states, most of the time, we can assume that they're constant.
If we are pretty sure that our system fits into this model (most of the systems do by the way), the only thing left is to estimate the mean and standard deviation of the noise functions Wk-1 and vk. We know that, in real life, no signal is pure Gaussian, but we may assume it with some approximation. This is not a big problem, because we'll see that the Kalman Filtering Algorithm tries to converge into correct estimations, even if the Gaussian noise parameters are poorly estimated.
The only thing to keep in mind is : "The better you estimate the noise parameters, the better estimates you get."
|STEP 2 - Start the Process|
If you succeeded to fit your model into Kalman Filter, than the next step is to determine the necessary parameters and your initial values.
We have two distinct set of equations : Time Update (prediction) andMeasurement Update (correction). Both equation sets are applied at each kthstate.
We made the modeling in STEP1, so we know the matrices A, B and H. Most probably, they will be numerical constants. And even most probably, they'll be equal to 1. I suggest you to re-write these equations and see how simplified will these equations become. (if you're lazy enough not to do it, I'll do it for you in the Example below).
The most remaining painful thing is to determine R and Q. R is rather simple to find out, because, in general, we're quite sure about the noise in the environment. But finding out Q is not so obvious. And at this stage, I can't give you a specific method.
To start the process, we need to know the estimate of x0, and P0.
|Answers to Silly Questions|
Can I deploy Kalman Filter to all Digital Signal Processing problems?
I've seen lots of papers that use Kalman Filter for a variety of problems, such as noise filtering, sub-space signal analysis, feature extraction and so on. The bottom line is, you can use Kalman Filter with a quite approximation and clever modeling.
Can I use it for Image Processing?
Where do we find these Time Update and Measurement Update equations? It seems that they suddenly appeared from nowhere.
You can derive it from the linear stochastic difference equation (the equations in STEP 1), by taking the partial derivative and setting them to zero(for minimizing the estimation error). Of course they're hard and time consuming.
Now let's try to estimate a scalar random constant, such as a "voltage reading" from a source. So let's assume that it has a constant value of aV (volts) , but of of course we some noisy readings above and below a volts. And we assume that the standard deviation of the measurement noise is 0.1 V.
Now let's build our model:
As I promised earlier, we reduced the equations to a very simple form.
• Above all, we have a 1 dimensional signal problem, so every entity in our model is a numerical value, not a matrix.
• We have no such control signal uk, and it's out of the game
• As the signal is a constant value, the constant A is just 1, because we already know that the next value will be same as the previous one. We are lucky that we have a constant value in this example, but even if it were any other linear nature, again we could easily assume that the value A will be 1.
• The value H = 1, because we know that the measurement is composed of the state value and some noise. You'll rarely encounter real life cases that H is different from 1.
And finally, let's assume that we have the following measurement values:
OK, we should start from somewhere, such as k=0. We should find or assume some initial state. Here, we throw out some initial values. Let's assume estimate of X0 = 0, and P0 = 1. Then why didn't we choose P0 = 0 for example? It's simple. If we chose that way, this would mean that there's no noise in the environment, and this assumption would lead all the consequent to be zero (remaining as the initial state). So we choose P0 something other that zero.
Let's write the Time Update and Measurement Update equations.
Now, let's calculate the values for each iteration.
Here, I displayed the first 2 state iterations in detail, the others follow the same pattern. I've completed the other numerical values via a computer algorithm, which is the appropriate solution. If you try to write it as an algorithm, you'll discover that Kalman Filter is very easy to implement.
The chart here (right) shows that the Kalman Filter algorithm converges to the true voltage value. Here, I displayed the first 10 iterations and we clearly see the signs of convergence. In 50 or so iterations, it'll converge even better.
To enable the convergence in fewer steps, you should
• Model the system more elegantly
• Estimate the noise more precisely
OK. We're done. The only thing to do is collecting the values we've calculated. That's it!
The Kalman Filter algorithm converges to the truth over a few iterations
 Greg Welch, Gary Bishop, "An Introduction to the Kalman Filter", University of North Carolina at Chapel Hill Department of Computer Science, 2001
 M.S.Grewal, A.P. Andrews, "Kalman Filtering - Theory and Practice Using MATLAB", Wiley, 2001