Optimized stochastic resonance method for signal detection and image processing
First Claim
Patent Images
1. A method of improving the detection of at least one signal embedded in non-Gaussian noise, said method comprising the steps of:
- (a) recording an observed data process;
(b) calculating with a processor stochastic resonance noise by determining the stochastic resonance noise probability density function that does not increase the probability of false alarm and calculating the stochastic resonance noise data probability density function from a known probability density function for said observed data process, wherein the stochastic resonance noise probability density function equals λ
δ
(n−
n1)+(1−
λ
)δ
(n−
n2), with values n1 and n2 equal to two delta function locations having probabilities of λ and
(1−
λ
), respectively;
(c) adding said stochastic resonance noise to said data; and
(d) displaying the results of adding said stochastic noise to said data, thereby improving the detection of said at least one signal in said non-Gaussian noise.
1 Assignment
0 Petitions
Accused Products
Abstract
Apparatus and method for improving the detection of signals obscured by noise using stochastic resonance noise. The method determines the stochastic resonance noise probability density function in non-linear processing applications that is added to the observed data for optimal detection with no increase in probability of false alarm. The present invention has radar, sonar, signal processing (audio, image and video), communications, geophysical, environmental, and biomedical applications.
-
Citations
8 Claims
-
1. A method of improving the detection of at least one signal embedded in non-Gaussian noise, said method comprising the steps of:
-
(a) recording an observed data process; (b) calculating with a processor stochastic resonance noise by determining the stochastic resonance noise probability density function that does not increase the probability of false alarm and calculating the stochastic resonance noise data probability density function from a known probability density function for said observed data process, wherein the stochastic resonance noise probability density function equals λ
δ
(n−
n1)+(1−
λ
)δ
(n−
n2), with values n1 and n2 equal to two delta function locations having probabilities of λ and
(1−
λ
), respectively;(c) adding said stochastic resonance noise to said data; and (d) displaying the results of adding said stochastic noise to said data, thereby improving the detection of said at least one signal in said non-Gaussian noise. - View Dependent Claims (2, 3, 4, 5)
-
-
6. A method in reducing the probability of error in non-Gaussian noise, comprising the steps of:
-
recording an observed data process; determining with a processor stochastic resonance noise by identifying a known data probability density function for said data process and determining from said known probability density function of said observed data process, wherein the stochastic resonance noise probability density function that consists of a single delta function, δ
(n−
n0) with value n0 equal to a delta function location with probability one;adding said stochastic resonance noise to the data of the recorded data process; and displaying the results of adding said stochastic noise to said data. - View Dependent Claims (7, 8)
-
Specification