| CPC H04B 17/309 (2015.01) [H04B 17/20 (2015.01); H04B 17/23 (2015.01); H04B 17/26 (2015.01); H04B 17/27 (2015.01); H04B 17/29 (2015.01); H04W 24/08 (2013.01); H04W 24/10 (2013.01); H04W 64/00 (2013.01); H04B 17/24 (2015.01); H04W 24/04 (2013.01)] | 18 Claims |

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1. A system for signal or interference detection in an electromagnetic environment, comprising:
at least one event manager;
at least one direction finding (DF) module;
at least one receiver operable to receive electromagnetic environment data including at least one signal;
at least one node including at least one signal processor operable to process the electromagnetic environment data; and
wherein the at least one receiver is in communication with the at least one node;
wherein the at least one signal processor is operable to process signal data using compressed data for deltas from at least one baseline;
wherein the deltas are differentials from the at least one baseline and minimize data sets or sample data required for comparisons and/or analytics;
wherein the at least one signal processor is operable to use a calibration vector, a first smoothing filter, and a second smoothing filter to de-bias raw signal data;
wherein the system is operable to use gradients and the second smoothing filter to create the calibration vector;
wherein the at least one node is operable to receive and analyze processed signal data from the at least one signal processor;
wherein the at least one node is operable to analyze the processed signal data by conducting at least two fast Fourier transform (FFT) analyses to create FFT data, aggregating the FFT data from the at least two FFT analyses to create the at least one baseline to be used in reporting, and comparing incoming FFTs to the at least one baseline to detect potential conflicts;
wherein the at least one node is operable to analyze the processed signal data based on at least one machine learning algorithm;
wherein the event manager is operable to decide a course of action based on data regarding the potential conflicts, user supplied knowledge, publicly available data, job manifests, and/or learned data from the at least one machine learning algorithm; and
wherein the at least one DF module is operable to locate a transmitter for the at least one signal using at least one angle-of-arrival (AoA) measurements of the at least one signal;
wherein the at least one node is operable to calculate a power distribution by frequency of the electromagnetic environment based on the FFT data including a first derivative of the FFT data and a second derivative of the FFT data; and
wherein an FFT engine is operable to track the at least one signal using edge processing and the first derivative of the FFT data and/or the second derivative of the FFT data.
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