Extracting spectral features from a signal in a multiplicative and additive noise environment

US9239372B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-9239372-B2
Application numberUS-201213613870-A
CountryUS
Kind codeB2
Filing dateSep 13, 2012
Priority dateSep 13, 2012
Publication dateJan 19, 2016
Grant dateJan 19, 2016

How to read this patent

A practical reading order for non-experts. Skip the full description unless you need deep technical detail.

  1. Title

    What the patent document calls the invention.

  2. Abstract

    A short plain-language summary of the technical disclosure.

  3. Assignees and inventors

    Who owns or filed the patent and who is credited as inventor.

  4. Key dates

    Filing, priority, publication, and grant dates set the timeline.

  5. First independent claim

    The legal scope of protection — read this for what is actually claimed.

  6. CPC / IPC classifications

    Technology tags used to group this patent with similar filings.

  7. Citations and related patents

    Prior art links and similar publications in this corpus.

Abstract

Official abstract text for this publication.

A spectral feature extracting engine, chirp detecting engine, and target classifying engine with corresponding method, system, and computer product are provided. Given a “noisy” signal having multiplicative and additive noise, the spectral feature extracting engine extracts spectral features from the noisy signal in the form of a spectral detection density. The extraction includes identifying an initial detection by comparing the spectral content of a subject time-frequency slice with an initial detection threshold calculated from a set of time-frequency slices, and determining the spectral detection density as a function of a number of identified initial detections and number of compared time-frequency slices. Based on the spectral detection density of the noisy signal, the chirp detecting engine detects multiple channel-induced and target-induced chirps present in the noisy signal and the target classifying engine classifies a target from the noisy signal.

First claim

Opening claim text (preview).

What is claimed is: 1. An improved method of operating a RADAR or wireless communications signal processing apparatus implemented as either one or more programmable processors or as special purpose logic circuitry for detecting channel-induced and target-induced chirps in a signal or for classifying a target from a signal using spectral feature extraction, the method comprising: operating a spectral feature extracting engine of the apparatus to: remove direct current bias from a RADAR or wireless communication signal captured by a receiver, the signal having impairments including multiplicative and additive noise, in order to produce a bias-free signal; transform the bias-free signal into a time-frequency spectrum; slice the time-frequency spectrum across time into time-frequency slices, each of the time-frequency slices having spectral content representing a measure of power spectral density of the bias-free signal at a given point in time; compute an initial detection threshold associated with the given signal from the time-frequency slices; compare, for at least some of the time-frequency slices the spectral content of a subject time-frequency slice with the computed initial detection threshold to identify an initial detection; determine a spectral detection density as a function of a number of initial detections being identified and number of time-frequency slices being compared; and operating a chirp detecting engine of the apparatus to detect channel-induced and target-induced chirp(s) present in the given signal based on the determined spectral detection density or, a target classifying engine of the apparatus to classify a target from the given signal based on the determined spectral detection density, thereby improving an ability to detect the channel-induced and target-induced chirps in the RADAR or the wireless communication signal impaired with multiplicative and additive noise. 2. The method of claim 1 wherein transforming the bias-free signal includes using any one of Short-Time Fourier Transform, and Wignerville Transform, to transform the given signal. 3. The method of claim 1 wherein computing the initial detection threshold includes computing any order moment of the spectral content of the time-frequency slices measured in power, log-power, magnitude, or combinations thereof. 4. The method of claim 1 wherein comparing includes identifying the initial detection when the spectral content of the subject time-frequency slice exceeds the computed initial detection threshold. 5. The method of claim 1 wherein determining the spectral detection density includes scaling the number of initial detections being identified by a time-bandwidth product of a given number of time-frequency slices. 6. The method of claim 1 wherein detecting channel-induced and target-induced chirp(s) includes comparing the spectral detection density to a threshold indicative of one or more chirps being present in the given signal. 7. The method of claim 1 wherein classifying the target includes comparing the determined spectral detection density with spectral signatures obtained from training data. 8. The method of claim 7 wherein classifying the target includes comparing the determined spectral detection density over time with the spectral signatures. 9. The method of claim 1 further comprising: given one or more chirps detected based on the spectral detection density of the given signal, associating initial detections occurring over a given span of time together; and curve fitting the associated initial detections to determine one or more chirp estimates. 10. The method of claim 9 further comprising demodulating the one or more detected chirps from the given signal using the chirp estimates. 11. A RADAR or wireless communications signal processing apparatus for detecting channel-induced and target-induced chirps in a signal or for classifying a target from a signal using spectral feature extraction, the apparatus comprising: a hardware memory having computer executable instructions stored thereupon; and one or more programmable processors or special purpose logic circuitry communicatively coupled to the hardware memory and configured to execute the instructions to: remove direct current bias from a RADAR or wireless communication signal captured by a receiver, the signal having impairments including multiplicative and additive noise, in order to produce a bias-free signal; transform the bias-free signal into a time-frequency spectrum; slice the time-frequency spectrum across time into time-frequency slices, each of the time-frequency slices having spectral content representing a measure of power spectral density of the bias-free signal at a given point in time; compute an initial detection threshold associated with the given signal from the time-frequency slices; compare, for at least some of the time-frequency slices; the spectral content of a subject time-frequency slice with the computed initial detection threshold to identify an initial detection; determine a spectral detection density as a function of a number of initial detections being identified and number of time-frequency slices being compared; and either detect channel-induced and target-induced chirp(s) present in the given signal based on the determined spectral detection density, or classify a target from the given signal based on the determined spectral detection density, thereby improving an ability to detect the channel-induced and target-induced chirps in the RADAR or the wireless communication signal impaired with multiplicative and additive noise. 12. A non-transitory computer-readable storage medium encoded with instructions that when executed by one ore more programmable processors of a RADAR or wireless communications signal processing apparatus cause the apparatus to: remove direct current bias from a RADAR or wireless communication signal captured by a receiver, the signal having impairments including multiplicative and additive noise, in order to produce a bias-free signal; transform the bias-free signal into a time-frequency spectrum; slice the time-frequency spectrum across time into time-frequency slices, each of the time-frequency slices having spectral content representing a measure of power spectral density of the bias-free signal at a given point in time; compute an initial detection threshold associated with the given signal from the time-frequency slices; compare, for at least some of the time-frequency slices, the spectral content of a subject time-frequency slice with the computed initial detection threshold to identify an initial detection; determine a spectral detection density as a function of a number of initial detections being identified and number of time-frequency slices being compared; and either detect channel-induced and target-induced chirp(s) present in the given signal based on the determined spectral detection density, or classify a target from the given signal based on the determined spectral detection density, thereby improving an ability to detect the channel-induced and target-induced chirps in the RADAR or the wireless communication signal impaired with multiplicative and additive noise.

Assignees

Inventors

Classifications

  • using analysis of echo signal for target characterisation; Target signature; Target cross-section · CPC title

  • G01S7/4004Primary

    of parts of a radar system · CPC title

  • G01S7/414Primary

    Discriminating targets with respect to background clutter · CPC title

  • Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems · CPC title

Patent family

Related publications grouped by family.

External sources

Frequently asked questions

Answers are generated from the same data shown on this page.

What does patent US9239372B2 cover?
A spectral feature extracting engine, chirp detecting engine, and target classifying engine with corresponding method, system, and computer product are provided. Given a “noisy” signal having multiplicative and additive noise, the spectral feature extracting engine extracts spectral features from the noisy signal in the form of a spectral detection density. The extraction includes identifying a…
Who is the assignee on this patent?
Maalouli Ghassan C, Young Brett J, Raytheon Co
What technology area does this patent fall under?
Primary CPC classification G01S7/4004. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Jan 19 2016 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).