Granted Patent B2

US 12,160,762 · App. 18/633,955 · Granted Dec 3, 2024

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 (46)

1. An apparatus for signal detection in an electromagnetic environment, comprising:

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

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

wherein the apparatus is operable to reveal and/or classify at least one signal based on an identification and/or a classification algorithm;

wherein the identification and/or classification algorithm are operable to identify temporal features in real time;

wherein the apparatus includes a temporal feature extraction (TFE) function;

wherein the TFE function is operable to create impressions of the electromagnetic environment based on at least one machine learning (ML) algorithm, wherein the impressions are determined over time and are interpreted as the at least one signal;

wherein the TFE function is operable to identify at least one narrowband signal and at least one wideband signal in the same frequency of a second wideband signal;

wherein the at least one ML algorithm utilizes an artificial neural network (ANN);

wherein the apparatus is operable to determine if the at least one signal is moving using a frequency-locked loop by determining if there is a Doppler change in the at least one signal;

wherein the apparatus is operable to calculate a first derivative of the power level measurements and a second derivative of the power level measurements;

wherein the apparatus is operable to select most prominent derivative of the first derivative and the second derivative;

wherein the apparatus is operable to perform a squaring function on the most prominent derivative;

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

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

wherein the apparatus is operable to reconstruct the at least one signal using the deltas and the at least one baseline; and

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

2. The apparatus of claim 1 , further comprising a multiplicity of receivers, wherein the multiplicity of receivers is operable to monitor multiple bandwidths and perform time-frequency analyses at the same time.

3. The apparatus of claim 1 , wherein the apparatus is operable to reveal the at least one signal by subtracting the at least one baseline from a spectral sweep to reveal an average power above the at least one baseline.

4. The apparatus of claim 1 , wherein the identification and/or classification algorithm is operable to match the at least one signal with at least one signal that it had identified during a learning phase of the at least one ML algorithm.

5. The apparatus of claim 1 , wherein the apparatus is mobile and linked to a Global Positioning System (GPS) system for time and location.

6. The apparatus of claim 1 , wherein the apparatus 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.

7. The apparatus of claim 1 , wherein the at least one signal is interference.

8. The apparatus of claim 1 , wherein the apparatus is operable to form a knowledge map of the electromagnetic environment based on the power level measurements of the electromagnetic environment.

9. The apparatus of claim 8 , wherein the apparatus is operable to scrub a spectral sweep against the knowledge map.

10. The apparatus of claim 1 , wherein the apparatus 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 at least one baseline;

subtracting the at least one baseline from a spectral sweep to reveal at least one signal to create signal data;

classifying the at least one signal based on a classification algorithm;

wherein the classification algorithm is operable to identify temporal features in real time;

using two smoothing filters to create a calibration vector to de-bias raw signal data;

calculating a first derivative of the power level measurements and a second derivative of the power level measurements;

selecting most prominent derivative of the first derivative and the second derivative;

performing a squaring function on the most prominent derivative;

creating impressions of the electromagnetic environment over time using a temporal feature extraction (TFE) function based on at least one machine learning (ML) algorithm, and interpreting the impressions as the at least one signal;

wherein the TFE function is operable to identify at least one narrowband signal and at least one wideband signal in the same frequency of a second wideband signal;

wherein the at least one ML algorithm utilizes an artificial neural network (ANN);

processing the signal data using compressed data for deltas;

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

filling gaps during reconstruction of the at least one signal where data of the at least one signal is absent;

wherein the deltas are differentials from the at least one baseline and minimize data sets or sample data required for comparisons and/or analytics.

12. The method of claim 11 , further comprising scrubbing the spectral sweep against the knowledge map.

13. The apparatus of claim 11 , wherein the classification algorithm is operable to match the at least one signal with at least one second signal that it had identified during a learning phase of the at least one ML algorithm.

Assignments (3)

Continuity (21)