System and Method for Training an Adaptive Filter in an Alternate Domain with Constraints

• US 20120066162A1
• Filed: 09/09/2010
• Published: 03/15/2012
• Est. Priority Date: 09/09/2010
• Status: Abandoned Application

• Current Assignee: Texas Instruments, Inc.
• Original Assignee: Texas Instruments, Inc.
• Inventors: Borkar, Milind Anil, Mujica, Fernando Alberto
• Application Number:
  US12/878,817
• Publication Number:
  US 20120066162A1
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: H03H 21/0012 Digital adaptive filters
• CPC Class Codes: H03H 21/0025 Particular filtering methods

Abstract

The adaptive filtering techniques described herein allow a filter that is operating in a target domain to be trained in another domain, possibly with constraints, using the same adaptation framework used in a standard adaptive filter. As a result, the adaptation engine may be configured to run in a transform domain that is more desirable than the target domain. For example, the transform domain may be less susceptible to noise or may have more impact on the trained filter'"'"'s desired results. The filter is trained in the transform domain and then the filter hardware is updated in the target domain.

Claims

1. An adaptive filter, comprising:

a filter circuit comprising a plurality of weighted filter elements, each weighted filter element corresponding to a weighted coefficient in a target domain;

an adaptation engine comprising circuitry for generating weighted coefficients in a transform domain, the weighted coefficients in the transform domain selected to minimize an error signal input to the adaptation engine; and

a coefficient transform circuit receiving the weighted coefficients in the transform domain and converting the weighted coefficients in the transform domain to weighted coefficients in the target domain.

2. The adaptive filter of claim 1, wherein the target domain is a time domain, and the transform domain is a frequency domain.

3. The adaptive filter of claim 1, wherein the coefficient transform circuit uses an Inverse Discrete Fourier Transform (IDFT) matrix to convert the weighted coefficients in the transform domain to weighted coefficients in the target domain.

4. The adaptive filter of claim 1, wherein the circuitry for generating weighted coefficients in a transform domain further comprises:

mask circuitry for selecting a subset of transform coefficients to be trained.

5. The adaptive filter of claim 1, wherein the adaptation engine and the coefficient transform circuit comprise a processor running transform domain adaptation software.

6. A method for training an adaptive filter, comprising:

identifying transform domain coefficients to be trained;

applying a coefficient mask in the transform domain to ensure only selected transform domain coefficients are trained by a selected adaptation algorithm;

generating trained coefficients in the transform domain;

applying an inverse transformation to the trained coefficients in the transform domain to derive trained coefficients in a target domain; and

updating adaptive filter coefficients using the trained coefficients in a target domain.

7. The method of claim 6, wherein the target domain is a time domain, and the transform domain is a frequency domain.

8. The method of claim 6, wherein the adaptive filter is a Finite Impulse Response (FIR) filter.

9. The method of claim 8, wherein the trained coefficients in the target domain are applied to taps in the FIR filter.

10. The method of claim 6, wherein the inverse transform is an Inverse Discrete Fourier Transform (IDFT) matrix.

11. A method for training a Finite Impulse Response (FIR) filter, comprising:

identifying frequency domain coefficients to be trained;

generating trained coefficients in the frequency domain;

applying an inverse transformation to the trained coefficients in the frequency domain to derive trained coefficients in a time domain; and

updating adaptive filter coefficients using the trained coefficients in a time domain.

12. The method of claim 11, wherein the inverse transform is an Inverse Discrete Fourier Transform (IDFT) matrix.

13. The method of claim 11, further comprising:

applying a coefficient mask in the frequency domain to ensure only selected frequency domain coefficients are trained by a selected adaptation algorithm.

14. The method of claim 11, wherein updating adaptive filter coefficients using the trained coefficients in a time domain comprises:

modifying the tap weights in the FIR filter.