REALTIME SELECTION OF ELECTRONIC COUNTERMEASURES AGAINST UNKNOWN, AMBIGUOUS OR UNRESPONSIVE RADAR THREATS

Abstract

One or more defined countermeasures are selected from a countermeasure library, populated with parameters, and applied against an unknown, ambiguous, or unresponsive imminent radar threat based on an analysis of a hostile RF waveform emitted by the radar threat. The analysis can include comparing static and/or dynamic features of the hostile RF waveform with features of known hostile RF waveforms. A parameter set associated with the selected defined countermeasure in the countermeasure library can be selected. Waveform features can be categorized and sub-categorized for comparison with the known hostile waveforms. A plurality of features can be detected and compared. The analysis can include correlating behavior patterns of a plurality of hostile RF waveforms emitted by the radar threat. A cognitive intelligence trained using a threat database and library of corresponding countermeasures can analyze the hostile RF waveform, select the defined countermeasure, and/or select or generate the parameters.

Claims

1. A method of protecting an asset from an imminent radar threat that is emitting a hostile radio frequency (RF) waveform and poses an imminent threat to the asset, said imminent radar threat being unknown, ambiguous, or unresponsive, the method comprising: detecting the hostile RF waveform; determining that the imminent radar threat is unknown, ambiguous, or unresponsive; performing an analysis of the detected hostile RF waveform; according to the analysis, selecting a defined countermeasure from a library of countermeasures; creating a populated countermeasure by populating the selected, defined countermeasure with a parameter set; and implementing the populated countermeasure, thereby disrupting the imminent radar threat and protecting the asset.

2. The method of claim 1, wherein performing the analysis of the hostile RF waveform includes: determining a feature of the hostile RF waveform; and identifying a known RF waveform included in a database of known radar threats having a feature that is identical or similar to the identified feature of the hostile RF waveform.

3. The method of claim 2, wherein the method further comprises determining a feature category to which the identified feature of the hostile RF waveform belongs, and wherein the selected defined countermeasure has been previously verified as effective against a known radar threat that emits a known RF waveform having a feature in the determined feature category.

4. The method of claim 2, wherein the selected defined countermeasure is linked to the determined feature, in that the selected defined countermeasure has previously been verified as effective against a plurality of known radar threats that emit known RF waveforms having features identical or similar to the identified feature of the hostile RF waveform.

5. The method of claim 1, wherein performing the analysis of the hostile RF waveform includes: determining a plurality of features of the hostile RF waveform; and identifying a known RF waveform included in a database of known radar threats having features that are identical or similar to the plurality of features of the hostile RF waveform.

6. The method of claim 1, wherein populating the selected defined countermeasure with a parameter set includes populating the selected, defined countermeasure with a parameter set that is associated with the selected defined countermeasure in the library of countermeasures.

7. The method of claim 1, wherein performing the analysis of the detected hostile RF waveform includes: providing a plurality of known threat waveforms and associated countermeasures as training data to an artificial intelligence, thereby training the artificial intelligence; and causing the trained artificial intelligence to perform the analysis of the hostile RF waveform.

8. The method of claim 7, wherein populating the selected defined countermeasure with the parameter set includes causing the trained artificial intelligence to select or generate the parameter set according to the analysis of the hostile RF waveform.

9. The method of claim 1, wherein: detecting the hostile RF waveform includes detecting and discriminating a plurality of hostile RF waveforms emitted by the imminent radar threat; and performing the analysis of the detected hostile RF waveform includes performing an analysis of the detected plurality of hostile RF waveforms.

10. The method of claim 9, wherein performing the analysis of the detected plurality of hostile RF waveforms includes analyzing correlations between behavior patterns of the detected plurality of hostile RF waveforms.

11. The method of claim 1, wherein selecting the defined countermeasure from the library of countermeasures includes: selecting a plurality of defined countermeasures from at least one library of countermeasures; creating a plurality of populated countermeasures by populating each of the selected defined countermeasures with a corresponding parameter set; and implementing the plurality of populated countermeasures.

12. The method of claim 11, wherein the plurality of populated countermeasures are implemented simultaneously.

13. The method of claim 1, wherein determining that the imminent radar threat is unknown, ambiguous, or unresponsive includes: comparing the hostile RF waveform with known RF waveforms contained in a threat database; if the hostile RF waveform can be unambiguously matched with one of the known RF waveforms, selecting and implementing an associated known countermeasure from the countermeasure library, and: if the associated known countermeasure is effective against the imminent radar threat, designating the imminent radar threat as a known radar threat; if the associated known countermeasure is not effective against the imminent radar threat, designating the imminent radar threat as an unresponsive radar threat; and if the hostile RF waveform cannot be unambiguously matched with one of the known RF waveforms, designating the imminent radar threat as an unknown or ambiguous radar threat.

14. An apparatus for protecting an asset from an imminent radar threat that is emitting a hostile radio frequency (RF) waveform and poses an imminent threat to the asset, said hostile RF waveform being unknown, ambiguous, or unresponsive, the apparatus comprising: an antenna configured to receive the hostile RF waveform; a receiver configured to amplify and digitize the hostile RF waveform; a signal analyzer configured to isolate the hostile RF waveform; a countermeasure library containing known countermeasures that are pre-verified as effective in disrupting associated known radar threats; and a Cognitive Electronic Warfare System (CEW) configured to: analyze the hostile RF waveform; according to said analysis, select a defined countermeasure from the countermeasure library; and create a populated countermeasure for application against the imminent radar threat by populating the selected defined countermeasure with a parameter set.

15. The apparatus of claim 14, wherein the signal analyzer is further configured to use data-driven machine learning to separate and isolate the hostile RF waveform from other signals received by the antenna.

16. The apparatus of claim 14, wherein the signal analyzer is further configured to use data-driven machine learning to select or generate the parameter set.

17. The apparatus of claim 14, further comprising: a threat database; and a waveform identifier configured to compare the hostile RF waveform with known RF waveforms stored in the threat database, and to determine if the radar threat is known, novel, or ambiguous.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0045] FIG. 1 is a flow diagram that illustrates a method embodiment of the present disclosure;

[0046] FIG. 2 is a flow diagram that illustrates implementation of an artificial intelligence in selecting a countermeasure from a countermeasure library in a method embodiment of the present disclosure; and

[0047] FIG. 3 is a block diagram of an apparatus embodiment of the present disclosure.

DETAILED DESCRIPTION

[0048] The present disclosure is a system and method for selecting and implementing a countermeasure that is effective against a radar threat when the radar threat is either an unidentified radar threat or is identified as a known radar threat but is not being sufficiently disrupted by a previously validated countermeasure.

[0049] According to the present disclosure, “detected” features (i.e. directly measured features) and in embodiments also “derived” features (i.e. additional features that are determined from combinations of detected features, including their temporal dynamics) of an RF waveform that is emitted by an unidentified, ambiguous, or unresponsive radar threat are used as a basis for selecting a defined countermeasure from a countermeasure library, even though the selected countermeasure has not been previously validated against the radar threat.

[0050] With reference to FIG. 1, embodiments detect and record RF energy 100 received over a broad range of frequencies, and then analyze the received RF energy 102 to identify and isolate hostile radars and the RF waveforms that they are emitting. These “detected” hostile RF waveforms are then processed as candidates for the application of countermeasures.

[0051] In some embodiments, each detected hostile RF waveform is compared 104 to a threat database to determine if it is a known hostile waveform. If the detected hostile RF waveform is unambiguously matched to an RF waveform in the threat database 106, then an associated known countermeasure is selected 108 from a countermeasure library. On the other hand, if the detected hostile RF waveform is a novel or ambiguous waveform, i.e. a hostile waveform that is not unambiguously matched to an RF waveform included in an available threat database, then the novel or ambiguous waveform is analyzed 110, and a defined countermeasure is selected 108 and parameterized from the countermeasure library on the basis of the analysis of the novel or ambiguous waveform. If the detected hostile RF waveform is known and a corresponding known countermeasure is applied but is not effective, then the hostile RF waveform and associated radar threat are designated to be unresponsive, and the detected hostile RF waveform is treated as if it were a novel or ambiguous RF waveform.

[0052] In other embodiments, comparison with a threat library is not included as part of the disclosed method. Instead, all detected hostile RF waveforms are considered to be novel RF waveforms, and each selection of a defined countermeasure from the countermeasure library is made according to an analysis of the detected hostile RF waveform.

[0053] In embodiments, the analysis of the novel RF waveform includes characterizing the novel RF waveform according to a plurality of feature categories. For example, with reference to Table 1 below, if the feature categories include amplitude modulation type, frequency modulation type, and geographic dispersion pattern, then a first Rf waveform might be characterized according to these categories as having a fixed amplitude, a fixed frequency, and a swept, 360 degree geographic dispersion pattern, while a second RF waveform might be characterized as having a pulsed amplitude modulation, a hopped frequency modulation, and a geographic dispersion pattern that attempts to remain fixed on a selected target.

TABLE-US-00001 TABLE 1 Example of waveform features and categories Feature Amplitude Frequency Geographic Category: Modulation Modulation Dispersion Waveform #1 Fixed Amplitude Fixed frequency Swept 360 deg. Waveform #2 Pulsed Amplitude Frequency Hopped Fixed on target Waveform #3 Sawtooth Chirped Limited sweep

[0054] Sub-categories can also be included as part of the feature categorization, such as a pulse repetition rate and duty cycle applicable to pulsed amplitude modulation, and a hopping rate and number of frequencies included in a hopped frequency modulation.

[0055] This approach of characterizing novel, ambiguous, and unresponsive RF waveforms according to features that belong to categories and sub-categories can enable rapid identification of a “closest match” known threat waveform that “matches” the novel, ambiguous, or unresponsive RF waveform in the largest number of feature categories and sub-categories. Table 2 below present a simple example where known threat waveform #2 is a closest match to a novel RF waveform.

TABLE-US-00002 TABLE 2 Example of comparing a novel RF waveform to known RF waveforms Feature Amplitude Frequency Geographic Category: Modulation Modulation Dispersion Novel Pulsed Amplitude Frequency hopped Swept 360 deg. Waveform Waveform #1 Fixed Amplitude Fixed frequency Swept 360 deg. Waveform #2 Pulsed Amplitude Frequency hopped Fixed on target Waveform #3 Sawtooth Chirped Limited sweep

[0056] In the example of Table 2, the countermeasure that is known to be most effective against known threat waveform #2 would likely be selected and applied to the detected novel RF waveform, because it matches the novel waveform in two of the three categories.

[0057] The example of Tables 1 and 2 is, of course, relatively simple and intended to illustrate a basic concept of embodiments of the present disclosure. In other embodiments, detected and derived features of a novel, ambiguous, or unresponsive RF waveform are compared with a set of general waveform features instead of, or in addition to, a comparison with features of known waveforms. In particular, embodiments compare detected and derived features of a novel, ambiguous, or unresponsive RF waveform with general classes of waveform features that are often mutually associated, as can be determined for example by applying machine learning to the features of known hostile RF waveforms.

[0058] Accordingly, a data-driven approach can be applied in advance to recognize associations between hostile RF waveform features that may not be apparent from a simple cataloging of known hostile RF waveform feature combinations, thereby providing additional flexibility and an expanded ability to select and optimize countermeasures for application against novel, ambiguous, and unresponsive RF waveforms. In principle, all possible unique combinations of individual features can be considered. However, embodiments employ a preapplication of machine learning and analysis to identify which combinations of features are valid, serve the purpose of a radar, and conform to the laws of physics. In general, either or both of these approaches can be applied, i.e. the approach of directly comparing and matching detected and derived features with known hostile database features, and the approach of comparing and matching detected and derived features with abstracted associations between hostile waveform features, as determined for example using machine learning.

[0059] Embodiments further attempt to determine relationships or “links” between waveform features and defined countermeasures, so that the links can be used to further optimize the selection of a defined countermeasure to be applied against a threat that is emitting RF energy having a novel waveform. These links can take the form of assessing the similarity or dis-similarity between a novel waveform and either a specific known hostile waveform or a more general class of hostile waveform, as identified for example by applying machine learning to a database of known hostile waveforms.

[0060] For example, with reference to Table 3 below, if defined countermeasure #1, when populated with an associated parameter set, is effective against known threat waveforms 1-3, all of which include frequency hopping, but include various amplitude modulation patterns, while defined countermeasure #2, when populated with an associated parameter set, is effective against threat waveforms 2, 4, and 5, which differ in their frequency modulation patterns, but all of which include a pulsed amplitude, then, in embodiments, defined countermeasure #1 will be deemed to be linked to frequency hopping but not to pulsed amplitudes, while defined countermeasure #2 will not be linked to frequency hopping, but will be linked to pulsed amplitudes. Of course, in embodiments the linkage of waveform features to defined countermeasures does not necessarily require that a linked feature be shared by all of the known waveforms against which the defined countermeasure is known to be effective.

TABLE-US-00003 TABLE 3 Example of linking features to countermeasures Feature Amplitude Frequency Geographic Category: Modulation Modulation Dispersion Defined Countermeasure #1, effective against: Waveform #1 Fixed Amplitude Frequency Hopped Swept 360 deg. Waveform #2 Pulsed Amplitude Frequency Hopped Fixed on target Waveform #3 Sawtooth Frequency Hopped Limited sweep Defined Countermeasure #2, effective against: Waveform #2 Pulsed Amplitude Frequency Hopped Fixed on target Waveform #4 Pulsed Amplitude fixed Fixed on target Waveform #5 Pulsed Amplitude chirped Limited sweep Novel Fixed amplitude Frequency Hopped Fixed on target Waveform

[0061] With continuing reference to the very simple example of Table 3, if a detected novel waveform is characterized by a fixed amplitude, frequency hopping, and a geographic dispersion that remains fixed on a target, then according to the example presented in Table 3 the novel waveform would be an equally close match to both known threat waveform #1 and known threat waveform #2, such that defined countermeasure #1 and defined countermeasure #2 would both be candidates for selection from the countermeasure library. However, as discussed above, defined countermeasure #1 is linked to frequency hopping, while defined countermeasure #2 is linked to a pulsed amplitude. Therefore, because the novel waveform includes frequency hopping but does not include a pulsed amplitude, defined countermeasure #1 would be selected from the countermeasure library and implemented against the novel RF waveform.

[0062] It should be noted that the examples discussed above with reference to Tables 1-3 are highly simplified, and are intended only to illustrate principles that apply to embodiments of the present disclosure, and do not necessarily present realistic examples.

[0063] As noted above, in various embodiments machine learning is implemented to select an optimal countermeasure to be used against a hostile radar that emits RF energy having a novel waveform. In some of these embodiments, with reference to FIG. 2, a plurality of known threat waveforms and their associated countermeasures are provided as training data to an artificial intelligence 200, after which the trained artificial intelligence is implemented to analyze each novel waveform 202, and to select an optimal countermeasure 204 from a countermeasure library. In various embodiments, the training data that is provided to the artificial intelligence can be, or can include, hypothesized waveforms that are derived from general features of known threat waveforms by machine learning, as is described above, by analyzing the feature combinations that are most likely to make-up waveform types, rather than being limited to only known waveforms.

[0064] With reference to FIG. 3, in embodiments the disclosed system includes an antenna 300 that captures wireless RF signals and directs them to receiver electronics 302 that may include a preamplifier 304 and digitizer 306, as well as a digital filter 308 and a digital downconverter 310 configured to eliminate the carrier frequency of the detected RF and to convert the detected RF to baseband. Embodiments of the present system further include a Signal Analyzer 312 that uses data-driven machine learning to separate (de-interleave) and isolate from each other the hostile radar-emitted waveforms that are present in the RF environment, and, in embodiments, associates each of the hostile RF waveforms with the hostile radar from which it is being emitted. In embodiments, data-driven machine learning, such as self-guided clustering, is used to de-interleave the RF waveforms, determine the features that characterize each of the RF waveforms, and in some embodiments to classify each of the hostile RF waveforms as to the inferred mode and intent of the associated radar according to its features and behavior.

[0065] The system further includes a countermeasure library 316, and in embodiments also a threat database 314 in which characterizing features of known threat waveforms are stored together with links between the known threat waveforms and associated known countermeasures contained in the countermeasure library 316 that were previously verified to be effective against the known threat waveforms. In embodiments, the threat database 314 also includes parameters associated with each of the threat waveforms that are to be used to populate the associated defined countermeasure.

[0066] In embodiments, a waveform identifier 318 compares detected waveforms that are isolated by the Signal Analyzer 312 with the known hostile waveforms that are contained in the threat database 314, and identifies each of the detected waveforms as either a known, ambiguous, or novel hostile waveform that is a candidate for application of a countermeasure, or as non-hostile waveform that is not a candidate for application of a countermeasure.

[0067] If a detected waveform is uniquely matched with a hostile waveform found in the threat database 314 and is therefore a known hostile waveform, then in embodiments a database driven warfare system 320 selects a defined or known countermeasure from the countermeasure library 316 according to the links between the known threats and countermeasures. Otherwise, if the detected waveform is a novel or ambiguous (hostile) RF waveform, then a Cognitive Electronic Warfare (CEW) system 322 analyses the novel or ambiguous RF waveform and selects an optimal defined countermeasure from the countermeasure library 316 according to the novel methods disclosed herein.

[0068] In either case, the selected defined countermeasure is then forwarded to appropriate countermeasure implementation systems 324 for population with appropriate parameters and implementation. If a pre-verified countermeasure applied to a known threat is found to be ineffective, then the known threat is re-classified as an unresponsive threat, and is treated as if it were a novel threat.

[0069] The foregoing description of the embodiments of the disclosure has been presented for the purposes of illustration and description. Each and every page of this submission, and all contents thereon, however characterized, identified, or numbered, is considered a substantive part of this application for all purposes, irrespective of form or placement within the application. This specification is not intended to be exhaustive or to limit the disclosure to the precise form disclosed. Many modifications and variations are possible in light of this disclosure.

[0070] Although the present application is shown in a limited number of forms, the scope of the disclosure is not limited to just these forms, but is amenable to various changes and modifications without departing from the spirit thereof. The disclosure presented herein does not explicitly disclose all possible combinations of features that fall within the scope of the disclosure. The features disclosed herein for the various embodiments can generally be interchanged and combined into any combinations that are not self-contradictory without departing from the scope of the disclosure. In particular, the limitations presented in dependent claims below can be combined with their corresponding independent claims in any number and in any order without departing from the scope of this disclosure, unless the dependent claims are logically incompatible with each other.