Accelerating the pace of engineering and science

# rlevinson

Reverse Levinson-Durbin recursion

## Syntax

r = rlevinson(a,efinal)
[r,u] = rlevinson(a,efinal)
[r,u,k] = rlevinson(a,efinal)
[r,u,k,e] = rlevinson(a,efinal)

## Description

The reverse Levinson-Durbin recursion implements the step-down algorithm for solving the following symmetric Toeplitz system of linear equations for r, where r = [r(1) Lr(p+1)] and r(i)* denotes the complex conjugate of r(i).

$\left[\begin{array}{cccc}r\left(1\right)& r{\left(2\right)}^{\ast }& \cdots & r{\left(p\right)}^{\ast }\\ r\left(2\right)& r\left(1\right)& \cdots & r{\left(p-1\right)}^{\ast }\\ ⋮& \ddots & \ddots & ⋮\\ r\left(p\right)& \cdots & r\left(2\right)& r\left(1\right)\end{array}\right]\left[\begin{array}{c}a\left(2\right)\\ a\left(3\right)\\ ⋮\\ a\left(p+1\right)\end{array}\right]=\left[\begin{array}{c}-r\left(2\right)\\ -r\left(3\right)\\ ⋮\\ -r\left(p+1\right)\end{array}\right]$

r = rlevinson(a,efinal) solves the above system of equations for r given vector a, where a = [1 a(2) L a(p+1)]. In linear prediction applications, r represents the autocorrelation sequence of the input to the prediction error filter, where r(1) is the zero-lag element. The figure below shows the typical filter of this type, where H(z) is the optimal linear predictor, x(n) is the input signal, $\stackrel{^}{x}\left(n\right)$ is the predicted signal, and e(n) is the prediction error.

Input vector a represents the polynomial coefficients of this prediction error filter in descending powers of z.

$A\left(z\right)=1+a\left(2\right){z}^{-1}+\cdots +a\left(n+1\right){z}^{-p}$

The filter must be minimum phase to generate a valid autocorrelation sequence. efinal is the scalar prediction error power, which is equal to the variance of the prediction error signal, σ2(e).

[r,u] = rlevinson(a,efinal) returns upper triangular matrix U from the UDU* decomposition

${R}^{-1}=U{E}^{-1}{U}^{\ast }$

where

$R=\left[\begin{array}{cccc}r\left(1\right)& r{\left(2\right)}^{\ast }& \cdots & r{\left(p\right)}^{\ast }\\ r\left(2\right)& r\left(1\right)& \cdots & r{\left(p-1\right)}^{\ast }\\ ⋮& \ddots & \ddots & ⋮\\ r\left(p\right)& \cdots & r\left(2\right)& r\left(1\right)\end{array}\right]$

and E is a diagonal matrix with elements returned in output e (see below). This decomposition permits the efficient evaluation of the inverse of the autocorrelation matrix, R−1.

Output matrix u contains the prediction filter polynomial, a, from each iteration of the reverse Levinson-Durbin recursion

$U=\left[\begin{array}{cccc}{a}_{1}{\left(1\right)}^{\ast }& {a}_{2}{\left(2\right)}^{\ast }& \cdots & {a}_{p+1}{\left(p+1\right)}^{\ast }\\ 0& {a}_{2}{\left(1\right)}^{\ast }& \ddots & {a}_{p+1}{\left(p\right)}^{\ast }\\ 0& 0& \ddots & {a}_{p+1}{\left(p-1\right)}^{\ast }\\ ⋮& \ddots & \ddots & ⋮\\ 0& \cdots & 0& {a}_{p+1}{\left(1\right)}^{\ast }\end{array}\right]$

where ai(j) is the jth coefficient of the ith order prediction filter polynomial (i.e., step i in the recursion). For example, the 5th order prediction filter polynomial is

```a5 = u(5:-1:1,5)'
```

Note that u(p+1:-1:1,p+1)' is the input polynomial coefficient vector a.

[r,u,k] = rlevinson(a,efinal) returns a vector k of length (p+1) containing the reflection coefficients. The reflection coefficients are the conjugates of the values in the first row of u.

```k = conj(u(1,2:end))
```

[r,u,k,e] = rlevinson(a,efinal) returns a vector of length p+1 containing the prediction errors from each iteration of the reverse Levinson-Durbin recursion: e(1) is the prediction error from the first-order model, e(2) is the prediction error from the second-order model, and so on.

These prediction error values form the diagonal of the matrix E in the UDU* decomposition of R−1.

${R}^{-1}=U{E}^{-1}{U}^{\ast }$

## References

[1] Kay, S.M., Modern Spectral Estimation: Theory and Application, Prentice-Hall, Englewood Cliffs, NJ, 1988.