Interference reduction by step function removal

US 6,834,260 B2
Filed: 12/28/2001
Issued: 12/21/2004
Est. Priority Date: 12/28/2001
Status: Expired due to Fees

First Claim

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1. A method comprising:

observing a finite duration signal y_nhaving N samples that comprises a representation of a mixture of a desired signal and an undesired signal, in which the desired signal is a function of at least one unknown signal parameter θ and

the undesired signal comprises an offset component based on interference of an external interference source;

modeling the offset component of the undesired signal as comprising a step function u defined by unknown step function parameters that include a first parameter c1 indicative of a first amplitude of the step function, a second parameter c2 indicative of a second amplitude of the step function, and a third parameter α

indicative of a point at which the step function transitions from the first amplitude to the second amplitude;

estimating jointly the unknown signal parameter θ and

the unknown step function parameters c1, c2, and α

(0≦

α

<

N) based on a non-linear optimization method; and

adjusting y_nbased on the estimated step function parameters.

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Abstract

Correcting a signal offset may include observing a finite duration signal y_nthat comprises a representation of a mixture of a desired signal that may include data of interest, and an undesired signal based on interference of an external interference source. The undesired signal may include an offset component which may be modeled as comprising a step function u defined by unknown step function parameters. The unknown step function parameters may be estimated using, for example, a maximum likelihood method. Thereafter, y_nmay be corrected based on the estimated step function parameters.

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43 Claims

1. A method comprising:
- observing a finite duration signal y_nhaving N samples that comprises a representation of a mixture of a desired signal and an undesired signal, in which the desired signal is a function of at least one unknown signal parameter θ and
  
  the undesired signal comprises an offset component based on interference of an external interference source;
  
  modeling the offset component of the undesired signal as comprising a step function u defined by unknown step function parameters that include a first parameter c1 indicative of a first amplitude of the step function, a second parameter c2 indicative of a second amplitude of the step function, and a third parameter α
  
  indicative of a point at which the step function transitions from the first amplitude to the second amplitude;
  
  estimating jointly the unknown signal parameter θ and
  
  the unknown step function parameters c1, c2, and α
  
  (0≦
  
  α
  
  <
  
  N) based on a non-linear optimization method; and
  
  adjusting y_nbased on the estimated step function parameters.

2. A system comprising:
- an observation circuit structured and arranged to observe a finite duration signal y_nhaving N samples that comprises a discrete representation of a mixture of a desired signal and an undesired signal, in which the desired signal is a function of at least one unknown signal parameter θ and
  
  the undesired signal comprises an offset component based on interference of an external interference source;
  
  a modeling circuit structured and arranged to model the offset component of the undesired signal as comprising a step function u defined by unknown step function parameters that include a first parameter c1 indicative of a first amplitude of the step function, a second parameter c2 indicative of a second amplitude of the step function, and a third parameter α
  
  indicative of a point at which the step function transitions from the first amplitude to the second amplitude;
  
  an estimating circuit structured and arranged to use a non-linear optimization method to determine jointly an estimated signal parameter and estimated step function parameters representative of the unknown signal parameter θ and
  
  the unknown step function parameters c1, c2, and α
  
  (0≦
  
  α
  
  <
  
  N); and
  
  a correction circuit structured and arranged to correct y_nbased on the estimated step function parameters.

3. A computer program stored on a computer readable medium or a propagated signal, the computer program comprising:
- an observation code segment configured to cause a computer to observe a finite duration signal y_nhaving N samples that comprises a representation of a mixture of a desired signal and an undesired signal, in which the desired signal is a function of at least one unknown signal parameter θ and
  
  the undesired signal comprises an offset component based on interference of an external interference source;
  
  a modeling code segment configured to cause the computer to model the offset component of the undesired signal as comprising a step function u defined by unknown step function parameters that include a first parameter c1 indicative of a first amplitude of the step function, a second parameter c2 indicative of a second amplitude of the step function, and a third parameter α
  
  indicative of a point at which the step function transitions from the first amplitude to the second amplitude;
  
  an estimating code segment configured to cause the computer to use a non-linear optimization method to determine jointly an estimated signal parameter and estimated step function parameters representative of the unknown signal parameter θ and
  
  the unknown step function parameters c1, c2, and α
  
  (0≦
  
  α
  
  <
  
  N); and
  
  a correcting code segment configured to cause the computer to correct y_nbased on the estimated step function parameters.

4. A processor which:
- observes a finite duration signal y_nhaving N samples that comprises a representation of a mixture of a desired signal and an undesired signal, the undesired signal comprising an offset component based on interference of an external interference source;
  
  models the offset component of the undesired signal as a step function u defined by unknown step function parameters that include a first parameter c1 indicative of a first amplitude of the step function, a second parameter c2 indicative of a second amplitude of the step function, and a third parameter α
  
  (0≦
  
  α
  
  <
  
  N) indicative of a point at which the step function transitions from the first amplitude to the second amplitude;
  
  determines estimated step function parameters comprising;
  
  a first estimate ĉ
  
  1 of c1 where $\hat{c} 1 \approx \frac{1}{\hat{α}} \sum_{n = 0}^{\hat{α} - 1} y_{n};$ a second estimate ĉ
  
  2 of c2 where $\hat{c} 2 \approx \frac{1}{N - \hat{α}} \sum_{n = \hat{α}}^{N - 1} y_{n}; and$ a third estimate {circumflex over (α
  
  )} of α
  
  where $\hat{α} \approx \underset{α_{Test}}{argmax} \frac{1}{α_{Test}} {\langle \sum_{n = 0}^{α_{Test} - 1} y_{n} \rangle}^{2} + \frac{1}{N - α_{Test}} {\langle \sum_{n = α_{Test}}^{N - 1} y_{n} \rangle}^{2}; and$ corrects the signal y_nbased on the estimated step function parameters.

5. A method comprising:
- observing a finite duration signal y_nthat comprises a discrete representation, including N samples, of a mixture of a desired signal, an undesired signal comprising an offset component, and a second signal including a generally sinusoidal waveform and an attenuated version of the desired signal;
  
  modeling y_nas including a discrete representation of the desired signal and a discrete representation of an offset component related to a square of the undesired signal, in which the offset component is modeled as comprising a step function u defined by unknown step function parameters;
  
  estimating the unknown step function parameters; and
  
  adjusting y_nbased on the estimated step function parameters.
- View Dependent Claims (6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
- - 6. The method of claim 5 in which y_nis based on a continuous signal.
  - 7. The method of claim 5 in which y_nis based on a discrete signal.
  - 8. The method of claim 5 in which the step function parameters include a first parameter c1 indicative of a first amplitude of the step function, a second parameter c2 indicative of a second amplitude of the step function, and a third parameter α
    - indicative of a point at which the step function transitions from the first amplitude to the second amplitude, and in which the desired signal is a function of at least one unknown signal parameter θ
      
      .
  - 9. The method of claim 8 in which estimating the step function parameters includes jointly estimating θ
    - , c1, c2, and α
      
      (0≦
      
      α
      
      <
      
      N) based on a non-linear optimization method.
  - 10. The method of claim 8 in which estimating the step function parameters includes estimating c1, c2, and α
    - (0≦
      
      α
      
      <
      
      N) based on a maximum likelihood method.
  - 11. The method of claim 10 in which the estimates of the step function parameters comprise:
12. The method of claim 11 in which determining {circumflex over (α
- )} comprises;
  
  selecting more than one value of α
  
  _Test;
  
  determining a value g for each selected value of α
  
  _Testwhere $g \approx \frac{1}{α_{Test}} {\langle \sum_{n = 0}^{α_{Test} - 1} y_{n} \rangle}^{2} + \frac{1}{N - α_{Test}} {\langle \sum_{n = α_{Test}}^{N - 1} y_{n} \rangle}^{2};$ selecting from among the determined values of g one or more maximum values of g; and
  
  selecting {circumflex over (α
  
  )} based on the one or more maximum values of g.
13. The method of claim 12 in which less than N values of α
- _Testare selected.
14. The method of claim 10 in which estimating the step function parameters further comprises jointly estimating θ
- , c1, c2, and α
  
  based on a non-linear minimization of a function comprising $f (θ, c1, c2, α) \approx \sum_{n = 0}^{α - 1} {\langle y_{n} - \frac{1}{α} \sum_{m = 0}^{α - 1} y_{m} - \frac{A_{0}}{2} s_{m} (θ) + \frac{1}{α} \sum_{m = 0}^{α - 1} \frac{A_{0}}{2} s_{m} (θ) \rangle}^{2} + \sum_{n = α}^{N - 1} {\langle y_{n} - \frac{1}{N - α} \sum_{m = α}^{N - α} y_{m} - \frac{A_{0}}{2} s_{n} (θ) + \frac{1}{N - α} \sum_{m = α}^{N - α} \frac{A_{0}}{2} s_{m} (θ) \rangle}^{2}$ in which the minimization is performed by computing one or more of the derivatives of ƒ
  
  .
15. The method of claim 5 wherein the desired signal comprises data of interest.

16. A system comprising:
- an observation circuit structured and arranged to observe a finite duration signal y_nthat comprises a discrete representation, including N samples, of a mixture of a desired signal, an undesired signal comprising an offset component, and a second signal including a generally sinusoidal waveform and an attenuated version of the desired signal;
  
  a modeling circuit structured and arranged to model y_nas including a discrete representation of the desired signal and a discrete representation of an offset component related to a square of the undesired signal, in which the offset component is modeled as comprising a step function u defined by unknown step function parameters;
  
  an estimating circuit structured and arranged to determine estimated step function parameters representative of the unknown step function parameters; and
  
  a correction circuit structured and arranged to correct y_nbased on the estimated step function parameters.
- View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25, 26)
- - 17. The system of claim 16 in which y_nis based on a continuous signal.
  - 18. The system of claim 16 in which y_nis based on a discrete signal.
  - 19. The system of claim 16 in which the unknown step function parameters include a first parameter c1 indicative of a first amplitude of the step function, a second parameter c2 indicative of a second amplitude of the step function, and a third parameter α
    - indicative of a point at which the step function transitions from the first amplitude to the second amplitude, and in which the desired signal is a function of at least one unknown signal parameter θ
      
      .
  - 20. The system of claim 19 in which the estimating circuit is further configured to estimate jointly the unknown step function parameters θ
    - , c1, c2, and α
      
      (0≦
      
      α
      
      <
      
      N) based on a non-linear optimization method.
  - 21. The system of claim 19 in which the estimating circuit is further configured to estimate the unknown step function parameters c1, c2, and α
    - (0≦
      
      α
      
      <
      
      N) based on a maximum likelihood method.
  - 22. The system of claim 21 in which the estimating circuit is further configured to estimate the unknown step function parameters as comprising:
23. The system of claim 22 in which the estimating circuit is further configured to determine {circumflex over (α
- )} based on the following;
  
  selecting more than one value of α
  
  _Test;
  
  determining a value g for each selected value of α
  
  _Testwhere $g \approx \frac{1}{α_{Test}} {\langle \sum_{n = 0}^{α_{Test} - 1} y_{n} \rangle}^{2} + \frac{1}{N - α_{Test}} {\langle \sum_{n = α_{Test}}^{N - 1} y_{n} \rangle}^{2};$ selecting from among the determined values of g one or more maximum values of g; and
  
  selecting {circumflex over (α
  
  )} based on the one or more maximum values of g.
24. The system of claim 23 in which less than N values of α
- _Testare selected by the estimating circuit.
25. The system of claim 21 in which the estimating circuit is further configured to estimate jointly the unknown step function parameters θ
- , c1, c2, and α
  
  based on non-linear minimization of a function comprising $f (θ, c1, c2, α) \approx \sum_{n = 0}^{α - 1} {\langle y_{n} - \frac{1}{α} \sum_{m = 0}^{α - 1} y_{m} - \frac{A_{0}}{2} s_{m} (θ) + \frac{1}{α} \sum_{m = 0}^{α - 1} \frac{A_{0}}{2} s_{m} (θ) \rangle}^{2} + \sum_{n = α}^{N - 1} {\langle y_{n} - \frac{1}{N - α} \sum_{m = α}^{N - α} y_{m} - \frac{A_{0}}{2} s_{n} (θ) + \frac{1}{N - α} \sum_{m = α}^{N - α} \frac{A_{0}}{2} s_{m} (θ) \rangle}^{2}$ in which minimization is performed by computing one or more of the derivatives of ƒ
  
  .
26. The system of claim 16 wherein the desired signal comprises data of interest.

27. A computer program stored on a computer readable medium or a propagated signal, the computer program comprising:
- an observation code segment configured to cause a computer to observe a finite duration signal y_nthat comprises a discrete representation, including N samples, of a mixture of a desired signal, an undesired signal comprising an offset component, and a second signal including a generally sinusoidal waveform and an attenuated version of the desired signal;
  
  a modeling code segment configured to cause the computer to model y_nas including a discrete representation of the desired signal and a discrete representation of an offset component related to a square of the undesired signal, in which the offset component is modeled as comprising a step function u defined by unknown step function parameters;
  
  an estimating code segment configured to cause the computer to determine estimated step function parameters representative of the unknown step function parameters; and
  
  a correcting code segment configured to cause the computer to correct y_nbased on the estimated step function parameters.
- View Dependent Claims (28, 29, 30, 31, 32, 33, 34, 35, 36, 37)
- - 28. The computer program of claim 27 in which y_nis based on a continuous signal.
  - 29. The computer program of claim 27 in which y_ncomprises is based on a discrete signal.
  - 30. The computer program of claim 27 in which the unknown step function parameters include a first parameter c1 indicative of a first amplitude of the step function, a second parameter c2 indicative of a second amplitude of the step function, and a third parameter α
    - indicative of a point at which the step function transitions from the first amplitude to the second amplitude, and in which the desired signal is a function of at least one unknown signal parameter θ
      
      .
  - 31. The computer program of claim 30 in which the estimating code segment further comprises a non-linear optimization code segment configured to cause the computer program to estimate jointly the unknown step function parameters θ
    - , c1, c2, and α
      
      (0≦
      
      α
      
      <
      
      N) based on a non-linear optimization method.
  - 32. The computer program of claim 30 in which the estimating code segment further comprises a maximum likelihood code segment configured to cause the computer to estimate the unknown step function parameters c1, c2, and α
    - (0≦
      
      α
      
      <
      
      N) based on a maximum likelihood method.
  - 33. The computer program of claim 32 in which the maximum likelihood code segment is further configured to cause the computer to estimate the unknown step function parameters as comprising:
34. The computer program of claim 33 in which the maximum likelihood code segment further comprises:
- a selecting code segment configured to cause the computer to select more than one value of α
  
  _Test;
  
  a calculating code segment configured to cause the computer to determine a value g for each selected value of α
  
  _Testwhere $g \approx \frac{1}{α_{Test}} {\langle \sum_{n = 0}^{α_{Test} - 1} y_{n} \rangle}^{2} + \frac{1}{N - α_{Test}} {\langle \sum_{n = α_{Test}}^{N - 1} y_{n} \rangle}^{2};$ a g_max code segment configured to cause the computer to select from among the determined values of g one or more maximum values of g; and
  
  an {circumflex over (α
  
  )}_max code segment configured to cause the computer to select {circumflex over (α
  
  )} based on the one or more maximum values of g.
35. The computer program of claim 34 in which the selecting code segment is further configured to cause the computer to select less than N values of α
- _Test.
36. The computer program of claim 32 in which the maximum likelihood code segment is further configured to cause the computer to estimate jointly the unknown step function parameters θ
- , c1, c2, and α
  
  based on non-linear minimization of a function comprising $f (θ, c1, c2, α) \approx \sum_{n = 0}^{α - 1} {\langle y_{n} - \frac{1}{α} \sum_{m = 0}^{α - 1} y_{m} - \frac{A_{0}}{2} s_{m} (θ) + \frac{1}{α} \sum_{m = 0}^{α - 1} \frac{A_{0}}{2} s_{m} (θ) \rangle}^{2} + \sum_{n = α}^{N - 1} {\langle y_{n} - \frac{1}{N - α} \sum_{m = α}^{N - α} y_{m} - \frac{A_{0}}{2} s_{n} (θ) + \frac{1}{N - α} \sum_{m = α}^{N - α} \frac{A_{0}}{2} s_{m} (θ) \rangle}^{2}$ in which the minimization is performed by computing one or more of the derivatives of ƒ
  
  .
37. The computer program of claim 27 wherein the desired signal comprises data of interest.

38. A processor which:
- observes a finite duration signal y_nthat comprises a discrete representation, including N samples, of a mixture of a desired signal, an undesired signal comprising an offset component, and a second signal including a generally sinusoidal waveform and an attenuated version of the desired signal;
  
  models y_nas including a discrete representation of the desired signal and a discrete representation of an offset component related to a square of the undesired signal, in which the offset component is modeled as comprising a step function u defined by unknown step function parameters;
  
  determines estimated step function parameters; and
  
  corrects the signal y_nbased on the estimated step function parameters.
- View Dependent Claims (39, 40, 41, 42, 43)
- - 39. The processor of claim 38 in which y_nis based on a continuous signal.
  - 40. The processor of claim 38 in which y_nis based on a discrete signal.
  - 41. The processor of claim 38 in which the unknown step function parameters include a first parameter c1 indicative of a first amplitude of the step function, a second parameter c2 indicative of a second amplitude of the step function, and a third parameter α
    - (0≦
      
      α
      
      <
      
      N) indicative of a point at which the step function transitions from the first amplitude to the second amplitude.
  - 42. The processor of claim 41 in which the processor estimates the unknown step function parameters as comprising:
43. The processor of claim 38 wherein the desired signal comprises data of interest.

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Current Assignee
Intel Corporation
Original Assignee
Intel Corporation
Inventors
Golden, Stuart A., Askar, Naiel K.
Primary Examiner(s)
Hoff, Marc S.
Assistant Examiner(s)
Gutierrez, Anthony

Application Number

US10/040,602
Publication Number

US 20030125912A1
Time in Patent Office

1,089 Days
Field of Search

702/191, 702/17
US Class Current

702/191
CPC Class Codes

H04L 25/061 providing hard decisions on...

Interference reduction by step function removal

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Interference reduction by step function removal

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