§ 5 Filtering
Extracting the required information from the data ( or signal ) containing errors ( meaning interference, noise ) , this data processing method is called filtering. Only a few types of least squares ( or least variance ) filtering are described below.
1. Least Squares Filtering
[ Least Squares Filtering for Growing Memory ]
Least Squares Criterion for Filtering
Given a discrete observation system
in the formula
where represents the transpose, which is the observation value at the moment , and is the n -dimensional column vector formed by , called the observation vector; yes , the matrix, where m is a constant; is the n -dimensional column vector, and its component is the error when; is the state vector ( m dimension ) . Suppose the state satisfies the equation
Among them is the m -order square matrix, its elements are all constants, and it is set to be reversible, remember .
is called the optimal estimate in the least squares sense.
Assuming that it is reversible, remember , then it can be deduced
The recursive formula for filtering
The above-mentioned formula for the optimal estimate is not practical on an electronic computer, and a recursive formula is usually used on the computer. can be launched
is an m -dimensional row vector
From the above formula, all historical observations are actually used for the purpose , and as n increases, the number used increases, so this filtering recursion formula is called growth memory.
[ Weighted Least Squares Filtering ] When n is sufficiently large, the earlier historical data is often used in an unfavorable role in estimation. Through weighting, the effect of "too old" data can gradually disappear. The following is an example of an "exponential decline" right.
Select , introduce a diagonal matrix sequence
As before, the observation system and the state equation are set as
It is called the optimal valuation in the sense of least squares with the weight of "exponential decline".
And there is a recursive formula for calculation
is an m -dimensional row vector with the same definition as before.
2. Wiener filter
Wiener filtering and Kalman filtering are both least square deviation filtering, but they are based on known conditions, calculation methods and scope of application are different.
[ Wiener Filtering Criterion ] Suppose you want to get the sequence z ( t ) ( t only takes some integer values ) , and actually get the sequence x ( t ) . So design a linear constant system with impulse response h ( t ) , so that its input quantity is x ( t ) , and the output quantity is
The deviation from z ( t ) is as small as possible under some criterion.
The so-called Wiener filtering problem is to take h ( t ) appropriately so that
When x ( t ) is a deterministic sequence,
When x ( t ) is a random sequence,
[ Single Least Squares Filtering ] Suppose that the filter factor h ( t ) is a sequence consisting of s + 1 equally spaced coefficients, such as
s + 1 is called the length of the filter factor, then the least square filter factor h ( t ) satisfies
or expressed by convolution as
is called the autocorrelation function of the input x ( t ) ( Chapter 16 § 3 ) ,
is called the cross-correlation function of z ( t ) and x ( t ) .
How well the filtered output matches the desired output can be measured by the normalized mean squared error :
to measure, where
Obviously . At that time , the filtering effect was the best; at that time , the filtering effect was the worst.
Similar results can be obtained when the length of the filter factor is infinite. Use , , to represent the frequency spectrum * of h ( t ) , respectively , then according to the convolution of the two functions is equivalent to the product of their spectra, the frequency characteristics can be obtained
It shows that the frequency spectrum of the filter factor can be determined by the frequency spectrum of the cross-correlation function and the auto-correlation function.
[ Multi-channel Least Square Filtering ] Multi-channel least square filtering method utilizes the repetition of multi-channel signals to provide more beneficial information.
Set as n - way input, m -way desired output, written as a matrix, respectively
is the multi-channel filter factor, written as a matrix as
The frequency spectrum of a discrete time series is defined as
When the discrete time series has only finite items, it can be filled with many 0s to become an infinite series and the above definition can be used.
is the m -channel filtered output, written as a matrix as
If the length of each input is k +1 , and the length of all filter factors is s +1 , then the length of the filtered output of each channel is k + s +1 .
The so-called multi-channel least square filtering is to appropriately select the multi-channel filter factor ( matrix ) H ( t ) to make the total mean square error
When it is a definite sequence,
When it is a random sequence,
The multi-channel least squares filter factor satisfies the following equation
( 1 )
is the multi-channel least squares filter factor, is the matrix,
is the transpose matrix of the input autocorrelation function square matrix, called the multi-way autocorrelation matrix ,
A matrix of cross-correlation functions for the desired output and input .
Equation ( 1 ) is mn ( s + 1) linear simultaneous equations with the filter factor as the unknown . Its solution is the required multi-channel least squares filter factor.
The degree of agreement between the filtered output and the desired output, using the normalized mean square error
to measure, obviously, . At that time , the multi-channel least square filtering output matched the desired output best; at that time , the matching degree was the worst.
When the length of the filter factor is infinite, the least squares filter factor satisfies the following equation
Expressed in frequency spectrum, we have
The solution of the above equation is the desired multi-path least squares filter factor.
3. Kalman filter
[ Kalman Filtering for Linear Discrete Systems ]
The dynamic model assumes that an n -dimensional linear dynamic system and a p -dimensional linear observation system are respectively described by the following difference equations :
Or introduce the corresponding symbol simply written as
( 1 )
( 2 )
where ( k is an integer ) satisfies
x ( t )is the n -dimensional state vector,( t )is the m -dimensional dynamic noise vector, z ( t )is the p -dimensional()observation vector, and v ( t )is the p -dimensional()observation noise vector;a matrix, called dynamicnoise matrix, H ( t )is amatrix, called the observation matrix,is a non- singular matrix, called the transition matrix of the system, with the following properties:
(i ) ( for all t , where I is the identity matrix )
( ii ) ( for any )
( iii )
Linear minimum variance estimation If the estimation of the state of the system at time is determined from the dynamic model , the following conditions are satisfied :
( i ) the estimate is a linear function of the observed value ;
( ii ) = minimum value, where is the estimation error ; then this estimation is called the linear minimum variance estimation.
Suppose that through the p -dimensional linear observation system ( 2 ) , from the 1st time to the kth time, the state of the n -dimensional linear dynamic system ( 1 ) has been observed k times , and according to the k observation data, the state of the jth time An estimate is made , and the estimated error is , and the mean squared error of the estimate is denoted as . At the time , it was called forecasting or extrapolation, at the time , it was called interpolation. Especially when j = k is called filtering, and is abbreviated .
The Kalman filter formula is set in the above dynamic model, and the dynamic noise and observation noise are uncorrelated zero-mean white noise sequences; that is, for all k, j
mean , mean square error
Let the statistical characteristics of the initial state be
and are not related to
Then the optimal linear filter can be calculated recursively by the following formula
its initial value ;
Called the weighting matrix or gain matrix, it is the covariance matrix of the optimal estimation error , the I in parentheses represents the identity matrix, and the last equation is called the covariance update equation.
At this time, the optimal linear prediction ( extrapolation ) is estimated as
( j > k )
[ Kalman Filtering for Continuous Time Systems ]
The dynamic model assumes that the state equation is
( 1 )
The observation equation is
where is a random process of n -dimensional vector type, and is a random process of p -dimensional vector type. , respectively, are m -dimensional ( ) and p -dimensional vector-type, uncorrelated white noise processes with zero mean, namely
In the formula, Q ( t ) and R ( t ) are both continuously differentiable, symmetric and non-negative definite matrices for time t ; they are Dirac functions. And F ( t ) , G ( t ) and H ( t ) are respectively matrices whose elements are non-random functions or constants of t .
The linear minimum variance estimation assumes the known ( observed ) value ( ) , and is calculated by the formula
represents a linear estimate such that
= minimum value
Such an estimate is called a linear minimum variance estimate, where the filter factor matrix, each element of which is continuously differentiable for both independent variables.
The Kalman filter equation assumes that the above dynamic model satisfies the following conditions :
( i ) the matrix R ( t ) is positive definite for all t ;
( ii ) Under the action of u ( t ) , the dynamic system ( 1 ) reaches a steady state, that is, x ( t ) is given by
Deterministic random function;
( iii ) At a certain time , the variance of the measured and the time is known;
Then the optimal filtering equation of the dynamic model is
in the formula
( weighted matrix equation )
( 2 )
( Ricati equation ( see Chapter XIII § 1 ))
The initial conditions are
In the above formula, it is called the weighting matrix, which is the covariance matrix of the optimal estimation error .
In particular, when it is a vector-type stationary random process, it can be solved in ( 2 ) .