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Course by: Clayton Scott. E-mail the author

Module by: Clayton Scott, Robert Nowak. E-mail the authors

The Kalman filter is just one of many adaptive filtering (or estimation) algorithms. Despite its elegant derivation and often excellent performance, the Kalman filter has two drawbacks:

1. The derivation and hence performance of the Kalman filter depends on the accuracy of the a priori assumptions. The performance can be less than impressive if the assumptions are erroneous.
2. The Kalman filter is fairly computationally demanding, requiring OP2 O P 2 operations per sample. This can limit the utility of Kalman filters in high rate real time applications.
As a popular alternative to the Kalman filter, we will investigate the so-called least-mean-square (LMS) adaptive filtering algorithm.

The principle advantages of LMS are

1. No prior assumptions are made regarding the signal to be estimated.
2. Computationally, LMS is very efficient, requiring OP O P per sample.
The price we pay with LMS instead of a Kalman filter is that the rate of convergence and adaptation to sudden changes is slower for LMS than for the Kalman filter (with correct prior assumptions).

### Noise Cancellation

Suppression of maternal ECG component in fetal ECG (Figure 2).

y ^ y is an estimate of the maternal ECG signal present in the abdominal signal (Figure 4).

## Iterative Minimization

Most adaptive filtering alogrithms (LMS included) are modifications of standard iterative procedures for solving minimization problems in a real-time or on-line fashion. Therefore, before deriving the LMS algorithm we will look at iterative methods of minimizing error criteria such as MSE.

Conider the following set-up: x k : observation x k : observation y k : signal to be estimated y k : signal to be estimated

### Linear estimator

y^k= w 1 x k + w 2 x k1 ++ w p x kp+1 y k w 1 x k w 2 x k 1 w p x k p 1
(1)
Impulse response of the filter: , 0 , 0 , w 1 , w 2 , w p , 0 , 0 , , 0 , 0 , w 1 , w 2 , w p , 0 , 0 ,

### Vector notation

y^k= x k Tw y k x k w
(2)
Where x k = x k x k1 x kp+1 x k x k x k 1 x k p 1 and w= w 1 w 2 w p w w 1 w 2 w p

### Error signal

e k = y k y^k= y k x k Tw e k y k y k y k x k w
(3)

### Assumptions

( x k , y k ) ( x k , y k ) are jointly stationary with zero-mean.

### MSE

E e k 2=E y k x k Tw2=E y k 22wTE x k y k +wTE x k x k Tw= R yy 2wT R xy +wT R xx w e k 2 y k x k w 2 y k 2 2 w x k y k w x k x k w R yy 2 w R xy w R xx w
(4)
Where R yy R yy is the variance of y k 2 y k 2 , R xx R xx is the covariance matrix of x k x k , and R xy =E x k y k R xy x k y k is the cross-covariance between x k x k and y k y k

#### Note:

The MSE is quadratic in W W which implies the MSE surface is "bowl" shaped with a unique minimum point (Figure 8).

### Optimum Filter

Minimize MSE:

(E e k 2 w =2 R xy +2 R xx w=0)( w opt = R xx -1 R xy ) w e k 2 -2 R xy 2 R xx w 0 w opt R xx R xy
(5)
Notice that we can re-write Equation 5 as
E x k x k Tw=E x k y k x k x k w x k y k
(6)
or
E x k ( y k x k Tw)=E x k e k =0 x k y k x k w x k e k 0
(7)
Which shows that the error signal is orthogonal to the input x k x k (by the orthogonality principle of minimum MSE estimator).

### Steepest Descent

Although we can easily determine w opt w opt by solving the system of equations

R xx w= R xy R xx w R xy
(8)
Let's look at an iterative procedure for solving this problem. This will set the stage for our adaptive filtering algorithm.

We want to minimize the MSE. The idea is simple. Starting at some initial weight vector w 0 w 0 , iteratively adjust the values to decrease the MSE (Figure 9).

We want to move w 0 w 0 towards the optimal vector w opt w opt . In order to move in the correct direction, we must move downhill or in the direction opposite to the gradient of the MSE surface at the point w 0 w 0 . Thus, a natural and simple adjustment takes the form
w 1 = w 0 12μE e k 2 w | w = w 0 w 1 w 0 1 2 μ w w 0 w e k 2
(9)
Where μμ is the step size and tells us how far to move in the negative gradient direction (Figure 10). Generalizing this idea to an iterative strategy, we get
w k = w k1 12μE e k 2 w | w = w k1 w k w k 1 1 2 μ w w k 1 w e k 2
(10)
and we can repeatedly update w w: w 0 , w 1 , , w k w 0 , w 1 , , w k . Hopefully each subsequent w k w k is closer to w opt w opt . Does the procedure converge? Can we adapt it to an on-line, real-time, dynamic situation in which the signals may not be stationary?

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