CFAR time-frequency processor for signal detection and extraction in noise

Abstract

Detection and extraction of unknown signal in noise may be important in radar When an unknown signal of a transient nature is received, representation in terms of basis functions, localized in both time and frequency, such as Gabor representation, may be very useful for signal detection. By using time-frequency decomposition, noise energy tends to spread across entire time-frequency domain, while signal energy often concentrates within a small region with a limited time interval and frequency band. Signal recognition in the time-frequency domain becomes easier than that in either time or frequency domain. By setting a CFAR threshold for and examining time-frequency Gabor coefficients which exceed the threshold, presence of a signal may be determined. CFAR time-frequency processing for detection and extraction of signals in noise improves detection and extraction performance for low Signal-to-noise-ratio (SNR) signals. Due to low SNR, it may be very difficult to identify signals from within either the time or the frequency domain alone. However, in the time-frequency domain, the signal can be easily recognized and its time location and instantaneous frequency can be measured. By performing CFAR thresholding and taking inverse Gabor transform, an unknown signal embedded in noise may be detected and reconstructed with enhanced quality.

Claims

1. In a radar receiver, an improved method for detection and extraction of an unknown information signal and noise comprising:

processing said unknown information signal and said noise received in said receiving step into meaningful information using a series of processing steps comprising:

generating and outputting time-frequency information from said unknown information signal and said noise,

processing said time-frequency information using statistical distribution information of said noise and generating noise filtered information,

receiving said noise filtered information and said time-frequency information and normalizing said noise filtered information and said time frequency information and outputting normalized information,

receiving said normalized information and false alarm rate information and thresholding said normalized information and generating thresholded time-frequency information, and

detecting and extracting a noiseless information signal from said threshold time-frequency information.

2. The method of claim 1, wherein said step of generating and outputting time-frequency information further comprises generating actual time-frequency coefficients and outputting said actual time-frequency coefficients.

3. The method of claim 1, wherein said step of receiving and processing said time-frequency information further comprises:

receiving said time-frequency information representative of said unknown information signal and said noise,

comparing said time-frequency information with a statistical distribution representative of said noise distribution, and calculating a mean value for said noise.

4. The method of claim 3, wherein said statistical distribution information further comprises a Rayleigh distribution.

5. The method of claim 1, wherein said step of receiving said normalized information and false alarm rate information and thresholding said normalized information further comprises Constant False Alarm Rate thresholding.

6. The method of claim 1, wherein said step of detecting and extracting a noiseless information signal from said thresholded time-frequency information further comprises:

receiving said thresholded time-frequency information,

estimating a desired signal corresponding to said thresholded time-frequency information,

generating modified time-frequency information corresponding to said desired signal estimated in said estimating step and outputting said modified time-frequency information, and

processing said modified time-frequency information and outputting said noiseless target information signal.

7. The method of claim 6, wherein said step of estimating a desired signal corresponding to said thresholded time-frequency information further comprises:

estimating an anticipated signal using a Least Squares approximation,

generating approximate time-frequency coefficients for said anticipated signal,

comparing said approximate time-frequency coefficients with said actual time-frequency coefficients,

optimizing said anticipated signal with respect to said desired signal using results from said comparing step and outputting an optimal anticipated signal, and

outputting said desired signal using said optimal anticipated signal derived in said optimizing step.

8. The method of claim 1, wherein said step of generating and outputting time-frequency information from said unknown information and said noise further comprises:

applying a time-frequency transform individually to a plurality of signal inputs having a plurality of scales and outputting a plurality of time-frequency signal outputs,

interpolating said plurality of time-frequency signal outputs and outputting a plurality of interpolated signal outputs, and

summing said plurality of interpolated signal outputs and outputting an improved time-frequency signal output.

9. A filter comprising:

means for providing a signal of interest in the time domain;

means for producing a transformed signal by transforming said signal of interest into the time-frequency domain, said time-frequency domain having a plurality of time-frequency bins; and

means for discarding the portion of said signal in each one of said time-frequency bins in which said portion is less than a preselected threshold.