Pulse or digital communications – Receivers – Interference or noise reduction
Reexamination Certificate
2000-07-14
2004-08-03
Chin, Stephen (Department: 2634)
Pulse or digital communications
Receivers
Interference or noise reduction
C375S227000, C375S340000, C375S343000, C375S346000
Reexamination Certificate
active
06771723
ABSTRACT:
STATEMENT OF GOVERNMENT INTEREST
The invention described herein may be manufactured and used by or for the US Government for governmental purposes without the payment of any royalty thereon.
BACKGROUND OF THE INVENTION
1. Background-Field of the Invention
The present invention relates to improving the detection performance of multi-channel receivers, and, in particular, to improving the detection of signals masked by the presence of partially correlated Gaussian or non-Gaussian noise plus additive Gaussian thermal white noise. The apparatus and method of the present invention is directed to the signal processing architecture and computational procedures of multi-channel receivers. The present invention has radar, sonar, geophysical, and biomedical applications.
2. Background-Description of the Prior Art
The use of multi-channel signal processing methods to detect the presence of a desired signal is well established. Basing such methods on parametric models offers the prospect of improved performance over the prior art.
In airborne array radar applications, for example, with J antenna elements (spatial channels) and N pulses per coherent processing interval (“CPI”), optimal signal detection methods using both angle- and Doppler-processing require joint space-time matched filtering in the JN×JN complex vector space. Such techniques are generally computationally prohibitive, and they require large amounts of secondary data (i.e., data from the radar surveillance region assumed to be free of the target signal of interest) to estimate the noise disturbance correlation. In addition, for conditions of non-homogeneous clutter, the secondary data may lack statistical equivalence to that in the range cell under test. Also, for the conventional Gaussian receiver, distinct thresholds must be established for individual range-azimuth cells over the entire radar surveillance volume. This requirement follows from the observation that the data sequence of N pulses is Gaussian for each individual range cell but non-Gaussian from cell to cell.
The performance of Gaussian receivers is improved to a degree by the systems described in the following U.S. patents:
U.S. Pat. No. 5,640,429 issued to Michels and Rangaswamy
U.S. Pat. No. 5,272,698 issued to Champion
U.S. Pat. No. 5,168,215 issued to Puzzo
U.S. Pat. No. 4,855,932 issued to Cangiani
U.S. Pat. No. 6,226,321 issued to Michels, et. al.
Cangiani et al. discloses a three dimensional electro-optical tracker with a Kalman filter in which the target is modeled in space as the superposition of two Gaussian ellipsoids projected onto an image plane. Puzzo offers a similar disclosure. Champion discloses a digital communication system.
Michels et al., U.S. Pat. No. 6,226,321, hereby incorporated by reference, discloses implementations, for a signal that has unknown amplitude. For the signal of unknown amplitude, Michels et al. teaches how to incorporate the estimated signal amplitude directly into the parametric detection procedure. Furthermore, Michels teaches two separate methods, namely, (1) how to detect the signal in the presence of partially correlated non-Gaussian clutter disturbance and (2) how to detect the signal in the presence of partially correlated Gaussian clutter disturbance. Furthermore, the method to detect the signal in the presence of partially correlated non-Gaussian clutter involves processing the received radar data and requires the use of functional forms that depend upon the probability density function (pdf) of the disturbance. Thus, the latter method requires knowledge of the pdf statistics of the non-Gaussian disturbance. The method does not teach how to process the data in such a manner that would not require knowledge of the disturbance process. Furthermore, it does not teach how to process the data with one method that would detect the signal in either Gaussian or non-Gaussian disturbance. Thus there exists a need for apparatus and method of processing the data with a detection method that does not require knowledge of the clutter statistics. Furthermore, there exists a need for a method that detects the signal in either Gaussian or non-Gaussian disturbance.
The performance improvements of the presently disclosed invention relative to prior art are detailed in J. H. Michels, M. Rangaswamy, and B. Himed, “Evaluation of the Normalized Parametric Adaptive Matched Filter STAP Test in Airborne Radar Clutter,” IEEE International Radar 2000 Conference, May 7-11, 2000, Washington, D.C. and J. H. Michels, M. Rangaswamy, and B. Himed, “Performance of STAP Tests in Compound-Gaussian Clutter,” First IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2000), Mar. 16-17, 2000, Cambridge, Mass. Both of these documents, designated references A and B, respectively, are incorporated herein by reference thereto.
OBJECTS AND SUMMARY OF INVENTION
Therefore one object of the present invention is to provide apparatus and method of detecting desired signals in additive Gaussian or non-Gaussian disturbance processes using single-channel or multiple-channel sensors.
Another of the present invention is to provide apparatus and method of detecting desired signals in additive Gaussian or non-Gaussian disturbance processes that use efficiently the available data from secondary data cells; i.e., require only a small number of secondary data cells.
Still another object of the present invention is to provide apparatus and method of detecting desired signals in additive Gaussian or non-Gaussian disturbance processes that uses linear prediction error filters.
Briefly stated, the present invention provides an apparatus and method for improving the detection of signals obscured by either correlated Gaussian or non-Gaussian noise plus additive jamming interference and thermal white Gaussian noise. Estimates from multi-channel data of model parameters that describe the noise disturbance correlation are obtained from data that contain signal-free data vectors, referred to as “secondary” or “reference” cell data. These parameters form the coefficients of a multi-channel whitening filter. A data vector, referred to as the “test cell” or “primary” data vector, to be tested for the presence of a signal passes through the multi-channel whitening filter. The filter's output is then processed to form a test statistic. The test statistic is compared to a threshold value to decide whether a signal is “present” or “absent”. Embodiments of the apparatus and method include estimating the signal amplitude both implicitly and explicitly and calculating test statistics for signal detection in both Gaussian and non-Gaussian noise.
According to an embodiment of the invention, in a system for processing signals, a method for identifying presence or absence of at least one potential target comprises the steps of: receiving from multiple channels signals corrupted by Gaussian or non-Gaussian disturbance; partitioning the signals into secondary data having a low probability of containing the potential target and primary data to be assessed for the presence of the target; estimating at least one linear filter parameter from the secondary data; filtering at least one steering vector and the primary data with at least one whitening filter based on the at least one linear filter parameter to produce at least one steering vector residual and at least one primary data residual; calculating a first test statistic as a function of the at least one linear filter parameter, the at least one steering vector residual, and the at least one primary data residual; and comparing the first test statistic to a threshold value to provide a “target present” or a “target absent” response when the signals are corrupted by Gaussian disturbance.
According to a feature of the invention, in a system for processing signals, a method for identifying presence or absence of at least one potential target comprises the steps of: receiving from multiple channels signals corrupted by Gaussian or non-Gaussian disturbance; partitioning the signals into secondary data having a low probabilit
Davis Dennis W.
Michels James H.
Román Jaime R.
Chin Stephen
Lugo David B.
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