Method for detecting signals in non-Gaussian background clutter

A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps, the method steps comprising:

• 1) filtering input data to identify Gaussian input data and non-Gaussian input data;
• 2) estimating the intensity level of the Gaussian input data;
• 3) determining the probability distributions of the non-Gaussian input data.
• 4) determining the background noise level of the input data;
• 5) estimating noise parameters of the input data;
• 6) estimating the residual intensity of the input data using the noise parameters;
• 7) determining a detection statistic M from the input data, estimated noise parameters, and the residual intensity of the input data;
• 8) determining a standard deviation N.sub.i of the detection statistic, M.sub.i ;
• 9) determining a normalized detection statistic Si, where S.sub.i =M.sub.i /N.sub.i,
• 10) determining a maximum value, S.sub.imax of S.sub.i,
• 11) determining a mean value S.sub.Mean and a standard deviation S.sub.Std dev of all S.sub.i excluding S.sub.imax ;
• 12) determining a threshold .tau..sub..alpha. of a zero mean unit variance normal distribution corresponding to a predetermined false alarm probability, where .alpha. is a predetermined false alarm probability;
• and
• 13) generating an output signal if ›S.sub.i -S.sub.Mean !/S.sub.Std dev .ltoreq..tau..sub..alpha..

A method for detecting signals in non-Gaussian background clutter, comprising the steps of:

• 1) filtering input data to partition Rayleigh input data from non-Rayleigh input data;
• 2) estimating the intensity level of the Rayleigh input data;
• 3) determining the probability distributions of the non-Rayleigh input data.
• 4) determining the background noise level of the input data;
• 5) estimating noise parameters of the input data;
• 6) estimating the residual intensity of the input data using the noise parameters;
• 7) determining a detection statistic M.sub.i from the input data, estimated noise parameters, and the residual intensity of the input data;
• 8) determining the standard deviation N.sub.i of the detection statistic, M.sub.i ;
• 9) determining a normalized detection statistic S.sub.i, where S.sub.i =M.sub.i /N.sub.i,
• 10) determining a maximum value, S.sub.imax of S.sub.i ;
• 11) determining a mean value S.sub.Mean and a standard deviation S.sub.Std dev of all S.sub.i excluding S.sub.imax ;
• 12) determining a threshold .tau..sub..alpha. of a zero mean unit variance normal distribution corresponding to a predetermined false alarm probability, where .alpha. is a predetermined false alarm probability;
• and
• 13) generating an output signal if ›S.sub.i -S.sub.Mean !/S.sub.Std dev .gtoreq..tau..sub..alpha..

A method for detecting signals in non-Gaussian background clutter, comprising the steps of:

• 1) filtering input data to partition exponential input data from non-exponential input data;
• 2) estimating the intensity level of the exponential input data;
• 3) determining the probability distributions of the non-exponential input data.
• 4) determining the background noise level of the input data;
• 5) estimating noise parameters of the input data;
• 6) estimating the residual intensity of the input data using the noise parameters;
• 7) determining a detection statistic M.sub.i from the input data, estimated noise parameters, and the residual intensity of the input data;
• 8) determining the standard deviation N.sub.i of the detection statistic, M.sub.i ;
• 9) determining a normalized detection statistic S.sub.i, where S.sub.i =M.sub.i /N.sub.i,
• 10) determining a maximum value, S.sub.imax of S.sub.i ;
• 11) determining a mean value S.sub.Mean and a standard deviation S.sub.Std dev of all S.sub.i excluding S.sub.imax ;
• 12) determining a threshold .tau..sub..alpha. of a zero mean unit variance normal distribution corresponding to a predetermined false alarm probability, where .alpha. is a predetermined false alarm probability;
• and
• 13) generating an output signal if ›S.sub.i -S.sub.Mean !/S.sub.Std dev .gtoreq..tau..sub..alpha..

A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps, the method steps comprising:

• 1) filtering input data to partition Rayleigh input data from non-Rayleigh input data;
• 2) estimating the intensity level of the Rayleigh input data;
• 3) determining the probability distributions of the non-Rayleigh input data.
• 4) determining the background noise level of the input data;
• 5) estimating noise parameters of the input data;
• 6) estimating the residual intensity of the input data using the noise parameters;
• 7) determining a detection statistic M.sub.i from the input data, estimated noise parameters, and the residual intensity of the input data;
• 8) determining the standard deviation N.sub.i of the detection statistic, M.sub.i ;
• 9) determining a normalized detection statistic S.sub.i, where S.sub.i =M.sub.i /N.sub.i,
• 10) determining a maximum value, S.sub.imax of S.sub.i ;
• 11) determining a mean value S.sub.Mean and a standard deviation S.sub.Std dev of all S.sub.i excluding S.sub.imax ;
• 12) determining a threshold .tau..sub..alpha. of a zero mean unit variance normal distribution corresponding to a predetermined false alarm probability, where .alpha. is a predetermined false alarm probability;
• and
• 13) generating an output signal if ›S.sub.i -S.sub.Mean !/S.sub.Std dev .gtoreq..tau..sub..alpha..

A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps, the method steps comprising:

• 1) filtering input data to partition exponential input data from non-exponential input data;
• 2) estimating the intensity level of the exponential input data;
• 3) determining the probability distributions of the non-exponential input data.
• 4) determining the background noise level of the input data;
• 5) estimating noise parameters of the input data;
• 6) estimating the residual intensity of the input data using the noise parameters;
• 7) determining a detection statistic M.sub.i from the input data, estimated noise parameters, and the residual intensity of the input data;
• 8) determining the standard deviation N.sub.i of the detection statistic, M.sub.i ;
• 9) determining a normalized detection statistic S.sub.i, where S.sub.i =M.sub.i /N.sub.i,
• 10) determining a maximum value, S.sub.imax of S.sub.i ;
• 11) determining a mean value S.sub.Mean and a standard deviation S.sub.Std dev of all S.sub.i excluding S.sub.imax ;
• 12) determining a threshold .tau..sub..alpha. of a zero mean unit variance normal distribution corresponding to a predetermined false alarm probability, where .alpha. is a predetermined false alarm probability;
• and
• 13) generating an output signal if ›S.sub.i -S.sub.Mean !/S.sub.Std dev .gtoreq..tau..sub..alpha..