L3harris Technologies, Inc.

L3 Technologies, Inc.

Andrew L Nelson

Thomas R Giallorenzi

Radivoje Zarubica

Philip M Hirz

N Thomas Nelson

Osama Sami Haddadin

Brent A Kenney

Seth John Thorup

Jason Wilden

Phillip M Hirz

Boston C Terry

Armin Hedzic

A radio frequency (RF) system may include at least one RF sensor in an RF environment and at least one RF actuator. The RF system may also include at least one processor that includes a machine learning agent configured to use a machine learning algorithm to generate an RF model to operate the at least one RF actuator based upon the at least one RF sensor. The processor may also include a recommendation training agent configured to generate performance data from the machine learning agent, and change the RF environment based upon the performance data so that the machine learning agent updates the machine learning algorithm.

Recommendation training agent for machine learning algorithm and related methods

Methods and systems for dynamically modifying a sampling operation of a sensor. The method includes obtaining a dynamically changing transmission characteristic based on an available channel bandwidth parameter. The dynamically changing transmission characteristic includes at least one of a sample rate, a time period, or a spectral bandwidth. The method further includes updating the sampling operation of the sensor based on the dynamically changing transmission characteristic. The method further includes measuring signal energy at a location of the sensor. The method further includes sampling the signal energy using the sampling operation to obtain sampled data. The method further includes providing the sampled data to a processing entity configured to analyze the data using a dynamically updated cross-ambiguity function.

Opportunistic adjustable rate cross-ambiguity function geolocation

Methods and systems adapted for providing a dynamically updated geolocation system. The geolocation system measures a signal, samples the signal, and applies a cross-ambiguity function to the sampled data to calculate the location of a signal source. The sampling operation and cross-ambiguity function are updated opportunistically and adaptively based on available channel resources between a plurality of sensors and a central processing location in the system. These update methods allow control of the data rate when channel resources are impacted by the physical environment where the geolocation system is operating.

Optimizations are provided for controlling an amount of radiated power (i.e. spectral flux density) that is being transmitted to a particular location. To that end, one or more antennas are used to transmit a power signal. Then, position information for each of those antennas is determined. Additionally, environmental information for the environment in which the antennas are operating is also determined. Also, an antenna radiation pattern for each of those antennas is also determined. Thereafter, how much power is radiated to a particular location is controlled so that the power never exceeds a certain threshold value. This control is achieved by dynamically adjusting the transmit power of the antennas based on the information obtained from the position information, the environmental information, and the antenna radiation pattern information.

Navigation aware power limiting

Geolocating an emitter of a low probability of detection (LPD) signal being transmitted from the emitter in an environment with a noise floor, where the LPD signal is below the noise floor. At a sensor node, a version of the LPD signal is received from the emitter. For the version of the LPD signal, cyclostationary feature detection or energy detection of the version of the LPD signal is performed. A low probability of detection descriptor word, including at least one of a frequency feature of the version of the LPD signal or an energy feature of the version of the LPD signal is created. The low probability of detection descriptor word is provided to a data processor, where the data processor is configured to use a plurality of low probability of detection descriptor words from different sensor nodes for different versions of the LPD signal to geolocate the emitter.

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Salt Lake City, UT, United States of America

Andrew L Nelson