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Method for detecting weak signals in a non-gaussian and non-stationary background
| Details |
Inventors: Stein, David W. J.;
Assignee: The United States of America as represented by the Secretary of the Navy (Washington, DC)
Primary Examiner: Assouad; Patrick
Assistant Examiner:
Attorney, Agent or Firm: Fendelman; Harvey, Lipovsky; Peter A., Kagan; Michael A.
A method is described for detecting weak signals in a non-Gaussian and non-stationary background using a hidden Markov parameter estimator. The method comprises the steps of: a) partitioning input data into sets of range bins, where the input data has a noise component; b) estimating hidden Markov model parameters from the input data; c) determining the average intensity I.sub.i of the range bins, where i represents an index for the ranges bins, and 1.ltoreq.i .ltoreq.R, and R represents the total number of range bins; d) estimating the average noise intensity N.sub.i of each range bin; e) determining the residual intensity estimates .rho..sub.i of each range bin as the maximum of 0 and (I.sub.i -N.sub.i) for all values of i; f) transforming the hidden Markov model parameters into estimates of hidden Markov model parameters of the noise component of the input data; g) generating modified locally optimal detection statistics representing the likelihood that the input data for the range bins contains a signal of interest using the estimates of the hidden Markov model parameters of the noise component of the input data, the residual intensity estimates, and the input data; h) transforming the modified locally optimal detection statistic into normalized values of the detection statistics applied to each range bin; and i) generating a detection output signal if any of the normalized values of the detection statistics exceeds a threshold. |
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DETAILED DESCRIPTION A method is described for detecting weak signals in a non-Gaussian and non-stationary background using a hidden Markov parameter estimator.
The method comprises the steps of: a) partitioning input data into sets of range bins, where the input data has a noise component; b) estimating hidden Markov model parameters from the input data; c) determining the average intensity I.
sub.
i of the range bins, where i represents an index for the ranges bins, and 1.
ltoreq.
i.
ltoreq.
R, and R represents the total number of range bins; d) estimating the average noise intensity N.
sub.
i of each range bin; e) determining the residual intensity estimates .
rho.
.
sub.
i of each range bin as the maximum of 0 and (I.
sub.
i -N.
sub.
i) for all values of i; f) transforming the hidden Markov model parameters into estimates of hidden Markov model parameters of the noise component of the input data; g) generating modified locally optimal detection statistics representing the likelihood that the input data for the range bins contains a signal of interest using the estimates of the hidden Markov model parameters of the noise component of the input data, the residual intensity estimates, and the input data; h) transforming the modified locally optimal detection statistic into normalized values of the detection statistics applied to each range bin; and i) generating a detection output signal if any of the normalized values of the detection statistics exceeds a threshold.
The invention preferably may be implemented as a set of computer readable program instructions which may be encoded onto a program storage device such as magnetic tape, a floppy disk, magnetoptical disk, compact disk, computer memory, or the like.
The storage device embodies the program of instructions for implementing the function of detecting weak signals in a non-Gaussian and non-stationary backgrounds.
The hidden Markov parameter estimator offers improved performance and simpler implementations compared to the prior art exponential mixture detector
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