System and method for linear prediction

• US 7,426,463 B2
• Filed: 08/01/2006
• Issued: 09/16/2008
• Est. Priority Date: 10/13/2000
• Status: Expired due to Fees

• Current Assignee: Leidos, Inc. (Leidos Holdings, Inc.)
• Original Assignee: Science Applications International Corporation
• Inventors: Goldstein, Jay Scott, Witzgall, Hanna Elizabeth
• Primary Examiner(s): Hudspeth; David R.
• Assistant Examiner(s): ALBERTALLI, BRIAN LOUIS
• Application Number:
  US11/496,419
• Publication Number:
  US 20060265214A1
• Time in Patent Office: 777 Days
• Field of Search: None
• US Class Current: 704/219
• CPC Class Codes: G06F 2218/22 Source localisation; Inverse modelling
• CPC Class Codes: G10L 19/04 using predictive techniques
• CPC Class Codes: G10L 25/12 the extracted parameters being prediction coefficients

Abstract

In a digital signal processor (DSP), input data is configured as a data matrix comprising data samples collected from an input signal. A weight vector is applied to the matrix, where the weight vector comprises three parts including (a) a rank reduction transformation produced by decomposition of data samples in a multistage Wiener filter having a plurality of stages, each stage comprising projection onto two subspaces. Each subsequent stage comprises projecting data transformed by the preceding second subspace onto each of a first subspace comprising a normalized cross-correlation vector at the previous stage and a second subspace comprising the null space of the normalized cross-correlation vector of the current stage, to form a reduced rank data matrix. Part (b) of the weight vector comprises minimizing mean squared error in the reduced rank data space. The output is a linear estimate of input data.

Claims

1. A method for training a linear prediction filter for prediction of information, comprising:

providing reference data;

collecting observed data containing the reference data;

identifying a reduced order Krylov subspace between the observed data and the reference data; and

maximizing mutual data between the observed data and the reference data in the subspace to define at least one autoregressive weight for use in the linear prediction filer; and

wherein the information comprises time of arrival information.

2. A method for linear prediction of information determinable from at least one input signal comprising plurality of data points, the method comprising:

providing plurality of reference data points;

training a filter by collecting p observed data points from the at least one input signal corresponding to p reference data points, wherein p reference data points comprise a portion of the plurality of reference data points, wherein the training further comprises;

processing the p observed data points through the filter to identify a reduced order Krylov subspace between the observed data points and the reference data points; and

defining a weight for minimizing the mean squared error between a predicted p+1 observed data point and a p+1 reference data point; and

applying the weight to filter the at least one input signal; and

wherein the information comprises time of arrival information.

3. A method for linear prediction of information determinable from at least one received signal containing a plurality of data points received at a detector, the method comprising:

defining a reference data matrix comprising data points collected from the at least one reference signal;

defining a received data matrix comprising data points collected from the at least one received signal;

calculating a weight vector by;

(a) performing a rank reduction transformation to create a reduced rank data matrix produced by decomposition of the received data matrix in a multi-stage Wiener filter; and

(b) minimizing the mean squared error in the reduced rank data matrix; and

applying the weight vector to the received data matrix; and

wherein the information comprises time of arrival information.

4. A system for linear prediction of information determinable from at least one received signal comprising a plurality of data points, the system comprising:

a linear prediction filter for processing the plurality of data points, the linear prediction filter comprising a multi-stage Wiener filter for projecting a full rank data matrix formed from the plurality of data points into a subspace having a reduced rank to form a reduced rank data matrix and minimizing the mean squared prediction error in the reduced rank matrix, and for applying a weight vector to the at least one received signal to generate a predicted signal, and

wherein the information comprises time of arrival information.

5. A computer program product for training a linear prediction filter for prediction of information, the computer program product comprising:

at least one computer readable medium;

a providing module resident on the medium and operable to provide reference data;

a collection module resident on the medium and operable to collect observed data containing the reference data;

an identification module resident on the medium and operable to identify a reduced order Krylov subspace between observed data containing the information and the reference data characteristic of the information; and

a maximization module resident on the medium and operable to maximize mutual data between the observed data and the reference data in the reduced order data space; and

wherein the information comprises time of arrival information.

6. A computer program product for linear prediction of information determinable from at least one input signal comprising a plurality of data points, the computer program product comprising:

at least one computer readable medium;

a providing module resident on the medium and operable to provide a plurality of reference data points;

a training module resident on the medium and operable to train a filter by collecting p observed data points in a snapshot from the at least one input signal corresponding to p reference data points, wherein p reference data points comprise a portion of the plurality of reference data points, wherein the training module further comprises;

a processing module resident on the medium and operable to process the p observed data points through the filter to identify a reduced order Krylov subspace between the observed data points and the reference data points; and

a defining module resident on the medium and operable to define a weight for minimizing the mean squared error between a predicted p+1 observed data point and a p+1 reference data point; and

an applying module resident on the medium and operable to apply the weight to filter the at least one input signal; and

wherein the information comprises time of arrival information.

7. A computer program product for linear prediction of information determinable from at least one signal comprising a plurality of data points, the computer program product comprising:

at least one computer readable medium;

a processing module resident on the medium and operable to process the plurality of data points by using a multi-stage Wiener filter for projecting a full rank data matrix formed from the plurality of data points into a subspace having a reduced rank to form a reduced rank data matrix;

the processing module resident on the medium and operable to minimize the mean squared prediction error in the reduced rank matrix;

the processing module resident on the medium and operable to apply a weight vector to the at least one signal to generate a predicted signal; and

wherein the information comprises time of arrival information.