Method and apparatus for cognitive nonlinear radar

• US 9,435,882 B2
• Filed: 08/27/2013
• Issued: 09/06/2016
• Est. Priority Date: 09/11/2012
• Status: Active Grant

• Current Assignee: The United States Of America As Represented By The Secretary Of The Army
• Original Assignee: The United States Of America As Represented By The Secretary Of The Army
• Inventors: Martone, Anthony F., McNamara, David M., Mazzaro, Gregory J., Hedden, Abigail S.
• Primary Examiner(s): Brainard, Timothy A
• Application Number:
  US14/010,580
• Publication Number:
  US 20160033622A1
• Time in Patent Office: 1,106 Days
• Field of Search: 342/27, 342/44, 342/193
• US Class Current: 1/1
• CPC Class Codes: G01S 13/04 Systems determining presence of a target based on relative movement of target G01S13/56
• CPC Class Codes: G01S 7/021 Auxiliary means for detecting or identifying radar signals or the like, e.g. radar jamming signals
• CPC Class Codes: G01S 7/023 Interference mitigation, e.g. reducing or avoiding non-intentional interference with other HF-transmitters, base station transmitters for mobile communication or other radar systems, e.g. using electro-magnetic interference [EMI] reduction techniques auxiliary means for detecting or identifying radar signals or the like G01S7/021; means for anti-jamming G01S7/36
• CPC Class Codes: G01S 7/0232 Avoidance by frequency multiplex
• CPC Class Codes: G01S 7/0233 Avoidance by phase multiplex
• CPC Class Codes: G01S 7/28 Details of pulse systems
• CPC Class Codes: G01S 7/412 based on a comparison between measured values and known or stored values
• CPC Class Codes: G01S 7/414 Discriminating targets with respect to background clutter

Abstract

A method and apparatus for cognitive non-linear radar processing comprising identifying one or more frequency bands of interest, passively scanning, using a non-linear radar (NR), the one or more frequency bands of interest to determine whether interference signals are occupying the one or more bands, transmitting radar waveforms and receiving radar waveform responses at one or more frequency bands determined to be free of interference, determining a likelihood of a target being present or not based on whether the received waveform responses match stored waveform responses for non-linear targets, and modifying waveform parameters of the transmitted radar waveform when the received waveform responses match the stored waveform responses, so as to transmit a modified radar waveform.

Claims

1. A method for cognitive non-linear radar processing comprising:

identifying one or more frequency bands of interest;

passively scanning, using a non-linear radar (NR), the one or more frequency bands of interest to determine whether interference signals are occupying the one or more bands;

transmitting radar waveforms and receiving radar waveform responses in one or more frequency bands determined to be free of interference;

determining a likelihood of a target being present or not based on whether the received waveform responses match stored waveform responses for non-linear targets; and

modifying waveform parameters of the transmitted radar waveform when the received waveform responses match the stored waveform responses, so as to transmit a modified radar waveform.

2. The method of claim 1 further comprising:

repeating passively scanning the one or more frequency bands of interest and transmitting modified radar waveforms until received waveforms do not match responses for non-linear targets.

3. The method of claim 1 wherein modifying the waveform parameters comprise:

modifying one or more of frequency, amplitude, and phase of the transmitted radar waveform.

4. The method of claim 1 further comprising:

receiving harmonic responses in response to the transmitted radar waveforms identifying non-linear targets.

5. The method of claim 1 further comprising:

receiving intermodulation distortion product responses in response to the transmitted radar waveforms identifying non-linear targets.

6. The method of claim 1 wherein determining whether the received waveform responses match the stored waveform responses further comprises:

measuring the likelihood that a target is present and determining that a target is present or not present based on a comparison between the likelihood and a predetermined threshold value.

7. The method of claim 6 further comprising:

determining a signal to noise ratio (SNR) estimate for each of the received waveform responses; and

determining the likelihood of detection for each feature of the SNR estimates.

8. The method of claim 7 further comprising:

;

classifying the each feature to identify a target type based on wave pattern templates.

9. The method of claim 8, wherein classifying further comprises:

applying one or more of Bayesian discrimination functions, nearest neighbor classifying, support vector machines, neural networks, tree based algorithms and unsupervised learning methods.

10. The method of claim 9 further comprising:

assigning a cost to a frequency associated with the classified feature;

modifying the waveform parameters of the transmitted modified radar waveform based on the assigned cost; and

transmitting the modified radar waveform at frequencies based on least cost to highest cost.

11. The method of claim 1 further comprising processing signals received by passively scanning, the processing further comprising:

estimating signal to noise ratio of the received signals; and

detecting potential communication targets and other RF signals.

12. An apparatus for cognitive non-linear radar processing comprising:

one or more passive receivers to scan one or more frequency bands of interest to determine whether interference signals are occupying the one or more bands of interest;

a radar transmitter to transmit radar waveforms and receiving radar waveform responses in one or more frequency bands determined to be free of interference; and

a cognitive processing module that determines the likelihood of a target being present or not based on whether the received waveform responses match stored waveform responses for non-linear targets and modifies waveform parameters of the transmitted radar waveform when the received waveform responses match the stored waveform responses, so as to transmit a modified waveform.

13. The apparatus of claim 12 further comprising:

wherein the cognitive processing module reiterates passively scanning the one or more frequency bands of interest until received waveforms do not match responses for non-linear targets, identifying frequency sub-bands of interest which do not contain one or more of pre-existing signals and interference.

14. The apparatus of claim 12 wherein modifying the cognitive processing module further comprises a waveform module, wherein the waveform module modified the waveform parameters, and wherein the waveform parameters comprise one or more of frequency, amplitude, and phase of the transmitted waveform.

15. The apparatus of claim 12 wherein the apparatus receives harmonic responses in response to the transmitted radar waveforms identifying non-linear targets.

16. The apparatus of claim 12 wherein the apparatus receives intermodulation distortion product responses in response to the transmitted radar waveforms identifying non-linear targets.

17. The apparatus of claim 12 further wherein the cognitive processing module further comprises a classification module that determines whether the received waveform responses match the stored waveform responses further comprises:

measuring the likelihood that a target is present and determining that a target is present or not present based on a comparison between the likelihood and a predetermined threshold value.

18. The apparatus of claim 17, wherein the cognitive processing module further comprises a signal to noise ratio module that estimates a signal to noise ratio (SNR) for each of the received waveform responses;

and

wherein the classification module further determines the likelihood of detection for each feature of the SNR estimates.

19. The apparatus of claim 18 further comprising wherein the classification module classifies the each feature to identify a target type based on wave pattern templates.

20. The apparatus of claim 19, wherein the classification module applies one or more of Bayesian discrimination functions, nearest neighbor classifying, support vector machines, neural networks, tree based algorithms and unsupervised learning methods to perform the classification.

21. The apparatus of claim 20 wherein the classification module further assigns a cost to a frequency associated with the classified feature, modifies the waveform parameters of the transmitted waveform based on the assigned cost;

and transmits the modified radar waveform at frequencies based on least cost to highest cost.

22. The apparatus of claim 12 wherein the cognitive processing module further comprises a signal to noise ratio (SNR) module that estimates signal to noise ratio of the received signals and the one or more passive receivers detects potential communication targets and other RF signals.