An antenna system and method includes or uses a set of M antenna structures and a cross bar beam former. The analog (or digital) cross bar beam former includes a set of M N to 1 (M(N×1)) interfaces, each of the M N to 1 interfaces having a first line coupled to a respective one of the set of the M antenna structures. The cross bar beam former also includes a set of N 1 to M (N(1×M)) interfaces, each of the N 1 to M interfaces having a set of M second lines, each of the M second lines being coupled to a respective one of the M N to 1 interfaces. Each of the N 1 to M interfaces includes a third line for a respective one of N independent beams.

An antenna system and method includes or uses a set of M antenna structures and a cross bar beam former. The analog (or digital) cross bar beam former includes a set of M N to 1 (M(N×1)) interfaces, each of the M N to 1 interfaces having a first line coupled to a respective one of the set of the M antenna structures. The cross bar beam former also includes a set of N 1 to M (N(1×M)) interfaces, each of the N 1 to M interfaces having a set of M second lines, each of the M second lines being coupled to a respective one of the M N to 1 interfaces. Each of the N 1 to M interfaces includes a third line for a respective one of N independent beams.

A method of assessing the effectiveness of an electronic countermeasure (ECM) applied against an unknown, ambiguous, or unresponsive radar threat includes monitoring changes in a radar-associated factor while applying the ECM and determining if the ECM is disrupting the hostile radar. The radar-associated factor can be a weapon that is controlled by the radar threat, and assessing the ECM can include determining whether the weapon is misdirected due to applying the ECM. Or the radar-associated factor can be a feature of an RF waveform emitted by the radar threat, and assessing the ECM can include determining if the feature is changed due to applying the ECM. Continuous changes in the feature can indicate unsuccessful attempts to mitigate the ECM. Return of the feature to a pre-threat state can indicate disruption of the radar. The ECM can be selected from a library of countermeasures pre-verified as effective against known threats.

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.

A method of selecting and optimizing a countermeasure for application against a novel, ambiguous, or unresponsive radar threat includes selecting a candidate countermeasure and an initial parameter set and varying at least one of the parameters while the effectiveness of the candidate countermeasure against the radar threat is assessed, for example by a human observer. Embodiments include repeating the process with additional candidate countermeasures. For an unresponsive radar threat, a previously effective countermeasure can be selected as the candidate countermeasure. For an ambiguous radar threat, at least one countermeasure previously verified as effective against a partially matching known threat can be selected as the candidate countermeasure. Correlated parameters can be simultaneously varied. An optimization surface and trajectory formed by a plurality of correlated parameters can be identified by machine intelligence, used to guide the parameter variations, and/or stored for use against the same or similar threats in the future.

A closed loop, real-time, cognitive Electronic Warfare (EW) system without a threat database includes an EW receiver for receiving radar threat signals; a Signal Analysis and Characterization module; a Pulse to Emitter Association sub-module; a Function De-interleaving Classifier sub-module; a Threat Behavior Model sub-module; a Countermeasures Synthesis module; a Capability, Severity, and Intent sub-module; a Countermeasure Selection sub-module; a Countermeasure Optimization sub-module; a Countermeasures Effectiveness Assessment module; a Resource Management module; and an EW transmitter.

A closed loop, real-time, cognitive Electronic Warfare (EW) system without a threat database includes an EW receiver for receiving radar threat signals; a Signal Analysis and Characterization module; a Pulse to Emitter Association sub-module; a Function De-interleaving Classifier sub-module; a Threat Behavior Model sub-module; a Countermeasures Synthesis module; a Capability, Severity, and Intent sub-module; a Countermeasure Selection sub-module; a Countermeasure Optimization sub-module; a Countermeasures Effectiveness Assessment module; a Resource Management module; and an EW transmitter.

A method of selecting and optimizing a countermeasure for application against a novel, ambiguous, or unresponsive radar threat includes selecting a candidate countermeasure and an initial parameter set and varying at least one of the parameters while the effectiveness of the candidate countermeasure against the radar threat is assessed, for example by a human observer. Embodiments include repeating the process with additional candidate countermeasures. For an unresponsive radar threat, a previously effective countermeasure can be selected as the candidate countermeasure. For an ambiguous radar threat, at least one countermeasure previously verified as effective against a partially matching known threat can be selected as the candidate countermeasure. Correlated parameters can be simultaneously varied. An optimization surface and trajectory formed by a plurality of correlated parameters can be identified by machine intelligence, used to guide the parameter variations, and/or stored for use against the same or similar threats in the future.

This application relates to a method for selecting sources of illumination to jam, performed by an apparatus of selecting sources of illumination to jam for passive radar jamming. The method may include, predicting sources of illumination used by a virtual passive radar of interest disposed within a jamming range with respect to the location of a passive radar jammer. The method may also include selecting sources of illumination as candidates for jamming if the signal strength of a target reflection signal received by the virtual passive radar of interest from the sources of illumination is equal to or greater than an acceptable received signal strength. The method may further include picking out which sources of illumination to jam from the sources of illumination selected as candidates for jamming, based on how frequently the virtual passive radar of interest uses each of the selected candidates for jamming.

This application relates to a method for selecting sources of illumination to jam, performed by an apparatus of selecting sources of illumination to jam for passive radar jamming. The method may include, predicting sources of illumination used by a virtual passive radar of interest disposed within a jamming range with respect to the location of a passive radar jammer. The method may also include selecting sources of illumination as candidates for jamming if the signal strength of a target reflection signal received by the virtual passive radar of interest from the sources of illumination is equal to or greater than an acceptable received signal strength. The method may further include picking out which sources of illumination to jam from the sources of illumination selected as candidates for jamming, based on how frequently the virtual passive radar of interest uses each of the selected candidates for jamming.