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US6044336A
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Method and apparatus for situationally adaptive processing in echo-location systems operating in non-Gaussian environments
Assignee: Northrop Grumman Systems Corp
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Filing: 1998-07-13
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Granted: 2000-03-28
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Expires: 2018-07-13
Abstract
Abstract A signal processing method for use in echo-location systems (radar, sonar, laser radar) to obtain accurate estimates of the target reflectivity sequence in a random (non-Gaussian) noise environment, including ambient noise, reverberation, and clutter. The method derives the statistical characteristics of the random background environments and then constructs and maximizes the corresponding approximate likelihood function using iterative methods. The approximate maximum likelihood estimates are generated on the basis of an approximation of the ideal likelihood function which is maximized using computationally efficient algorithms.
Inventors
- 👤 Vasilis Z. Marmarelis
- 👤 Chrysostomos L. Nikias
- 👤 Dae Cheol Shin
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-07 16:35
CPC Classifications
G; G01; G01S; G01S7/00; G01S7/02; G01S7/28; G01S7/285; G01S7/292; G01S7/48; G01S7/483; G01S7/486; G01S7/487; G01S7/52; G01S7/523; G01S7/526; G01S7/527
Claims Analysis
Total Claims: 21
Claim 1
Independent Claim
69 words
In an echo-location system, a method for adaptively estimating the target reflectivity sequence from received data that has been corrupted by non-Gaussian disturbances, said method comprising: estimating the statistics of the prevailing non-Gaussian disturbance in the received data; generating an approximate likelihood function from the estimated statistics of the prevailing non-Gaussian disturbances; and maximizing the likelihood function by applying an iterative gradient-descent algorithm to obtain the target reflectivity sequence.
Claim 2
Dependent Claim
69 words
In an echo-location system, a method for adaptively estimating the target reflectivity sequence from received data that has been corrupted by non-Gaussian disturbances, said method comprising: estimating the statistics of the prevailing non-Gaussian disturbance in the received data; generating an approximate likelihood function from the estimated statistics of the prevailing non-Gaussian disturbances; and maximizing the likelihood function by applying an iterative gradient-descent algorithm to obtain the target reflectivity sequence.
Claim 3
Dependent Claim
31 words
The method of claim 1 or claim 3 wherein said estimating step comprises fitting the data to broad classes of parametric distributions to obtain a probability density function of the disturbances.
Claim 4
Dependent Claim
68 words
In an echo-location system, a method for adaptively estimating the target reflectivity sequence from received data that has been corrupted by non-Gaussian disturbances, said method comprising: estimating the statistics of the prevailing non-Gaussian disturbances in the received data; generating an approximate likelihood function from the estimated statistics of the prevailing non-Gaussian disturbances by obtaining a log-likelihood function; and maximizing the likelihood function to obtain the target reflectivity sequence.
Claim 5
Dependent Claim
68 words
In an echo-location system, a method for adaptively estimating the target reflectivity sequence from received data that has been corrupted by non-Gaussian disturbances, said method comprising: estimating the statistics of the prevailing non-Gaussian disturbances in the received data; generating an approximate likelihood function from the estimated statistics of the prevailing non-Gaussian disturbances by obtaining a log-likelihood function; and maximizing the likelihood function to obtain the target reflectivity sequence.
Claim 6
Dependent Claim
13 words
The method of claim 3 wherein said maximizing step comprises an iterative process.
Claim 7
Dependent Claim
15 words
The method of claim 3 wherein said maximizing step comprises applying an iterative gradient-descent algorithm.
Claim 8
Dependent Claim
68 words
In a data reception system, a method for adaptively estimating the transmitted data from received data that has been corrupted by non-Gaussian disturbances, said method comprising: estimating the statistics of the prevailing non-Gaussian disturbances in the received data; generating an approximate likelihood function from the estimated statistics of the prevailing non-Gaussian disturbances; and maximizing the likelihood function by applying an iterative gradient-descent algorithm to obtain the transmitted data.
Claim 9
Dependent Claim
68 words
In a data reception system, a method for adaptively estimating the transmitted data from received data that has been corrupted by non-Gaussian disturbances, said method comprising: estimating the statistics of the prevailing non-Gaussian disturbances in the received data; generating an approximate likelihood function from the estimated statistics of the prevailing non-Gaussian disturbances; and maximizing the likelihood function by applying an iterative gradient-descent algorithm to obtain the transmitted data.
Claim 10
Dependent Claim
31 words
The method of claim 6 or claim 8 wherein said estimating step comprises fitting the data to broad classes of parametric distributions to obtain a probability density function of the disturbances.
Claim 11
Dependent Claim
67 words
In a data reception system, a method for adaptively estimating the transmitted data from received data that has been corrupted by non-Gaussian disturbances, said method comprising: estimating the statistics of the prevailing non-Gaussian disturbances in the received data; generating an approximate likelihood function from the estimated statistics of the prevailing non-Gaussian disturbances by obtaining a log-likelihood function; and maximizing the likelihood functions to obtain the transmitted data.
Claim 12
Dependent Claim
67 words
In a data reception system, a method for adaptively estimating the transmitted data from received data that has been corrupted by non-Gaussian disturbances, said method comprising: estimating the statistics of the prevailing non-Gaussian disturbances in the received data; generating an approximate likelihood function from the estimated statistics of the prevailing non-Gaussian disturbances by obtaining a log-likelihood function; and maximizing the likelihood functions to obtain the transmitted data.
Claim 13
Dependent Claim
13 words
The method of claim 8 wherein said maximizing step comprises an iterative process.
Claim 14
Dependent Claim
15 words
The method of claim 8 wherein said maximizing step comprises applying an iterative gradient-descent algorithm.
Claim 15
Dependent Claim
111 words
In an echo location system, a method for adaptively estimating target-channel kernels h(k) from given transmitted data x(k) and given received data y(k) in the presence of severe noise, clutter and reverberation ε(k), said parameters having the relationship y(k)=h(k)*x(k)+ε(k), wherein * denotes convolution, said method comprising; estimating the statistics of ε(k) to obtain a probability density function p(ε); constructing an approximate likelihood function based on p(ε) for N independent samples according to the relationship: ##EQU9## where: ##EQU10## and y(n) and x(n) are the n-th samples of the respective received and transmitted signals, and h(m) is the sought unknown discrete kernel values; and maximizing the likelihood function to obtain the estimated h(m).
Claim 16
Dependent Claim
111 words
In an echo location system, a method for adaptively estimating target-channel kernels h(k) from given transmitted data x(k) and given received data y(k) in the presence of severe noise, clutter and reverberation ε(k), said parameters having the relationship y(k)=h(k)*x(k)+ε(k), wherein * denotes convolution, said method comprising; estimating the statistics of ε(k) to obtain a probability density function p(ε); constructing an approximate likelihood function based on p(ε) for N independent samples according to the relationship: ##EQU9## where: ##EQU10## and y(n) and x(n) are the n-th samples of the respective received and transmitted signals, and h(m) is the sought unknown discrete kernel values; and maximizing the likelihood function to obtain the estimated h(m).
Claim 17
Dependent Claim
13 words
The method of claim 11 wherein said maximizing step comprises an iterative process.
Claim 18
Dependent Claim
14 words
The method of claim 12 wherein said iterative process comprises an iterative gradient-descent algorithm.
Claim 19
Dependent Claim
15 words
The method of claim 11 further comprising taking the log of the likelihood flnction: ##EQU11##
Claim 20
Dependent Claim
10 words
The method of claim 14 wherein said maximizing step comprises:
Claim 21
Dependent Claim
24 words
The method of claim 15 wherein said maximizing step further comprises an iterative gradient-descent algorithm: where γ j is a possibly variable step size.
References Cited (Backward Citations)
US4719466A
Cited
US4933914A
Cited
US5517531A
Cited
US5550759A
Cited
US5282225A
Cited
US5546084A
Cited
US5337055A
Cited
US5760732A
Cited
US5694342A
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2003-03-28 | 2003-09-27 | 2004-03-28 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2007-03-28 | 2007-09-27 | 2008-03-28 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2011-03-28 | 2011-09-27 | 2012-03-28 | $7700.00 | Window Closed / Lapsed |