Granted Patent B2

US 12,483,915 · App. 18/973,812 · Granted Nov 25, 2025

Inventors: David William Kleinbeck (Lees Summit, MO); Ronald C. Dzierwa (Baltimore, MD); Daniel Carbajal (Severna Park, MD)

Assignee: Digital Global Systems, Inc.

H04W24/08H04B17/20H04B17/23H04B17/26H04B17/27H04B17/29H04B17/309H04B17/318H04W24/10H04B17/24H04W24/04

Abstract

Claims (65)

1 . A system for signal detection in an electromagnetic environment, comprising:

at least one receiver, at least one processor, and at least one memory;

wherein the system is operable to create power level measurements of the electromagnetic environment;

wherein the system is operable to perform anomaly detection and alarm generation using a machine learning (ML) algorithm;

wherein the system is operable to determine a baseline;

wherein the system is operable to reveal at least one signal based on the baseline to create signal data;

wherein the system is operable to use a sensitivity parameter to set an energy threshold that determines if the at least one signal is a signal of interest;

wherein the sensitivity parameter is optimized by an event compositor that processes real-time events from the system and records signal observations;

wherein the at least one processor is operable to use a first smoothing filter and a second smoothing filter to create a calibration vector;

wherein the second smoothing filter is performed only on frequencies outside a frequency range of the at least one signal;

wherein the at least one processor is operable to use the calibration vector to de-bias the signal data;

wherein the system is operable to calculate signal degradation data for the at least one signal based at least in part on noise figure parameters, hardware parameters, and environmental parameters;

wherein the hardware parameters comprise antenna position, antenna type, orientation, and/or effective isotropic radiated power (EIRP);

wherein the system is operable to process the signal data using compressed data for deltas;

wherein the deltas are differentials from the baseline and minimize data sets or sample data required for comparisons and/or analytics; and

wherein the system is operable to reconstruct the at least one signal using the deltas and the baseline.

2 . The system of claim 1 , further comprising a multiplicity of receivers operable to monitor multiple bandwidths and perform time-frequency analyses simultaneously.

3 . The system of claim 1 , wherein the system is operable to identify edges of the at least one signal based on matching positive and negative gradients.

4 . The system of claim 1 , wherein the system is operable to determine a baseline by averaging a spectral sweep, removing areas identified by matched positive and negative gradients, and connecting points between removed areas.

5 . The system of claim 1 , wherein the system is operable to create impressions of the electromagnetic environment based on the ML algorithm, wherein the impressions are determined over time and are interpreted as the at least one signal.

6 . The system of claim 1 , wherein the ML algorithm is an artificial neural network (ANN) algorithm.

7 . The system of claim 1 , wherein the system is operable to estimate a location of a signal emitting device from which the at least one signal is emitted based on in-phase and quadrature (I/Q) data generated from a spectral sweep.

8 . The system of claim 1 , wherein the system is operable to produce a topographic map displaying propagation of spectral power per frequency band.

9 . The system of claim 1 , wherein the system is operable to fill gaps during reconstruction of the at least one signal where data of the at least one signal is absent.

10 . The system of claim 1 , wherein the system is operable to detect the at least one signal in the electromagnetic environment based on matched positive and negative gradients.

11 . A method for signal detection in an electromagnetic environment, comprising:

creating power level measurements of the electromagnetic environment;

forming a knowledge map of the electromagnetic environment based on the power level measurements of the electromagnetic environment;

determining a baseline;

subtracting the baseline from a spectral sweep to create signal data;

performing anomaly detection and alarm generation using a machine learning (ML) algorithm;

processing the signal data using compressed data for deltas;

creating a calibration vector with a first smoothing filter and a second smoothing filter;

de-biasing the signal data using the calibration vector;

creating a sensitivity parameter that sets an energy threshold to determine if at least one signal is a signal of interest;

reconstructing the at least one signal using the deltas and the baseline; and

calculating and storing signal degradation data for the at least one signal based at least in part on noise figure parameters, hardware parameters, and environmental parameters;

wherein the hardware parameters comprise antenna position, antenna type, orientation, and/or effective isotropic radiated power (EIRP);

wherein the deltas are differentials from the baseline and minimize data sets or sample data required for comparisons and/or analytics;

wherein the sensitivity parameter is optimized by an event compositor module that processes real-time events and records signal observations;

wherein the second smoothing filter is performed only on frequencies outside a frequency range of the at least one signal; and

wherein the system is operable to fill gaps during reconstruction of the at least one signal where data of the at least one signal is absent.

12 . The method of claim 11 , further comprising creating impressions of the electromagnetic environment based on the ML algorithm, wherein the impressions are determined over time and are interpreted as the at least one signal.

13 . A method for signal detection in an electromagnetic environment, comprising:

scrubbing a spectral sweep against a knowledge map of the electromagnetic environment;

smoothing the spectral sweep with a correction vector;

detecting at least one signal in the electromagnetic environment;

determining a baseline;

subtracting the baseline from the spectral sweep to create signal data;

performing anomaly detection and alarm generation using a machine learning (ML) algorithm;

processing the signal data using compressed data for deltas;

creating a calibration vector with a first smoothing filter and a second smoothing filter;

de-biasing the signal data using the calibration vector;

creating a sensitivity parameter that sets an energy threshold to determine if the at least one signal is a signal of interest;

producing a topographic map displaying propagation of spectral power per frequency band;

detecting a narrowband signal overlapping in frequency with a wideband signal; and

calculating and storing signal degradation data for the at least one signal based at least in part on noise figure parameters, hardware parameters, and environmental parameters;

wherein the hardware parameters comprise antenna position, antenna type, orientation, and/or effective isotropic radiated power (EIRP);

wherein the sensitivity parameter is optimized by an event compositor module that processes real-time events and records signal observations;

wherein the deltas are differentials from the baseline and minimize data sets or sample data required for comparisons and/or analytics; and

wherein the second smoothing filter is performed only on frequencies outside a frequency range of the at least one signal.

14 . The method of claim 13 , further comprising reconstructing the at least one signal using the deltas and the baseline.

15 . The method of claim 14 , further comprising filling gaps during reconstruction of the at least one signal where data of the at least one signal is absent.

16 . The method of claim 13 , wherein the at least one signal is the narrowband signal hidden in the wideband signal, and wherein the narrowband signal has a bandwidth ranging from 1 kHz to 60 kHz is inside the wideband signal with a bandwidth up to 100 MHz.

17 . The method of claim 13 , further comprising calculating a first derivative of power level measurements and a second derivative of the power level measurements.

Assignments (3)

Continuity (23)

References Cited (400)