Systems, methods, and devices for automatic signal detection with temporal feature extraction within a spectrum
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
H04B-017/30
G06N-099/00
G06N-005/02
출원번호
US-0412982
(2017-01-23)
등록번호
US-10122479
(2018-11-06)
발명자
/ 주소
Dzierwa, Ronald C.
출원인 / 주소
DGS GLOBAL SYSTEMS, INC.
대리인 / 주소
Neo IP
인용정보
피인용 횟수 :
0인용 특허 :
191
초록▼
Systems, methods and apparatus for automatic signal detection with temporal feature extraction in an RF environment are disclosed. An apparatus learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. A knowledge map is formed base
Systems, methods and apparatus for automatic signal detection with temporal feature extraction in an RF environment are disclosed. An apparatus learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. A knowledge map is formed based on the learning data. The apparatus automatically extracts temporal features of the RF environment from the knowledge map. A real-time spectral sweep is scrubbed against the knowledge map. The apparatus is operable to detect a signal in the RF environment, which has a low power level or is a narrowband signal buried in a wideband signal, and which cannot be identified otherwise.
대표청구항▼
1. A method for automatic signal detection in a radio-frequency (RF) environment, comprising: learning the RF environment in a period of time to a settled percent of at least 99.95% based on statistical learning techniques, thereby creating learning data including power level measurements of the RF
1. A method for automatic signal detection in a radio-frequency (RF) environment, comprising: learning the RF environment in a period of time to a settled percent of at least 99.95% based on statistical learning techniques, thereby creating learning data including power level measurements of the RF environment;indexing the power level measurements for each frequency interval in a spectrum section in the period of time;forming a knowledge map of the RF environment based on the power level measurements of the RF environment;automatically extracting at least one temporal feature of the RF environment from the knowledge map;scrubbing a real-time spectral sweep against the knowledge map;calculating a first derivative of the power level measurements and a second derivative of the power level measurements;selecting most prominent derivatives of the first derivative and the second derivative;performing a squaring function on the most prominent derivatives;detecting at least one signal in the RF environment based on matched positive and negative gradients;averaging the real-time spectral sweep, removing areas identified by the matched positive and negative gradients, and connecting points between removed areas to determine a baseline;subtracting the baseline from the real-time spectral sweep to reveal the at least one signal; andwherein one or more of the at least one signal is a narrowband signal hidden in a wideband signal. 2. The method of claim 1, wherein the knowledge map comprises an array of normal distributions, wherein each normal distribution corresponds to how often a power level at each frequency has been at a particular level. 3. The method of claim 1, further comprising creating a profile of the RF environment based on the knowledge map, wherein the profile comprises a highest power level at each frequency during the predetermined period of time. 4. The method of claim 1, wherein the at least one signal is transmitted from a remote signal emitting device and has a low power level. 5. The method of claim 1, wherein the narrowband signal has a bandwidth ranging from 1 kHz to 60 kHz and is inside the wideband signal across a spectrum up to about 6 GHz. 6. The method of claim 1, further comprising automatically fine-tuning a threshold of power level on a segmented basis while extracting the at least one temporal feature from the knowledge map. 7. The method of claim 1, wherein a frequency resolution of the knowledge map is based on a Fast Fourier Transform (FFT) size setting. 8. The method of claim 1, further comprising periodically reevaluating the RF environment and updating the knowledge map. 9. The method of claim 1, further comprising automatically recording relevant information in high definition when the at least one signal is detected. 10. The method of claim 1, further comprising sending a notification and/or an alarm to an operator after detecting the at least one signal. 11. A system for automatic signal detection in a radio-frequency (RF) environment, comprising: at least one apparatus for detecting signals in the RF environment;wherein the at least one apparatus is operable to sweep and learn the RF environment in a period of time to a settled percent of at least 99.95% based on statistical learning techniques, thereby creating learning data including power level measurements of the RF environment;wherein the at least one apparatus is operable to index the power level measurements for each frequency interval in a spectrum section in the period of time;wherein the at least one apparatus is operable to form a knowledge map based on the power level measurements of the RF environment;wherein the at least one apparatus is operable to automatically extract at least one temporal feature of the RF environment from the knowledge map;wherein the at least one apparatus is operable to scrub a real-time spectral sweep against the knowledge map;wherein the at least one apparatus is operable to calculate a first derivative of the power level measurements and a second derivative of the power level measurements;wherein the at least one apparatus is operable to select most prominent derivatives of the first derivative and the second derivative;wherein the at least one apparatus is operable to perform a squaring function on the most prominent derivatives;wherein the at least one apparatus is operable to detect at least one signal in the RF environment based on matched positive and negative gradients;wherein the at least one apparatus is operable to average the real-time spectral sweep, remove areas identified by the matched positive and negative gradients, and connect points between removed areas to determine a baseline;wherein the at least one apparatus is operable to subtract the baseline from the real-time spectral sweep to reveal the at least one signal; andwherein one or more of the at least one signal is a narrowband signal hidden in a wideband signal. 12. The system of claim 11, further comprising a remote device in network-based communication with the at least one apparatus, wherein the knowledge map and detecting results are displayed on a remote device in real time. 13. The system of claim 11, wherein the knowledge map comprises an array of normal distributions, wherein each normal distribution corresponds to how often a power level at each frequency has been at a particular level. 14. The system of claim 11, wherein the apparatus is operable to create a profile of the RF environment based on the knowledge map, wherein the profile comprises a highest power level at each frequency during the period of time. 15. The system of claim 11, wherein the apparatus is operable to send a notification and/or an alarm to an operator after detecting the at least one signal. 16. An apparatus for detecting at least one signal in a radio-frequency (RF) environment, comprising: at least one processor coupled with at least one memory, and at least one sensor;wherein the apparatus is operable to sweep and learn the RF environment in a period of time based on statistical learning techniques, thereby creating learning data including power level measurements of the RF environment;wherein the apparatus is operable to index the power level measurements for each frequency interval in a spectrum section in the period of time;wherein the apparatus is operable to form a knowledge map of the RF environment based on the power level measurements of the RF environment;wherein the apparatus is operable to automatically extract at least one temporal feature of the RF environment from the knowledge map;wherein the apparatus is operable to scrub a real-time spectral sweep against the knowledge map;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 derivatives of the first derivative and the second derivative;wherein the apparatus is operable to perform a squaring function on the most prominent derivatives;wherein the apparatus is operable to detect at least one signal in the RF environment based on matched positive and negative gradients;wherein the apparatus is operable to average the real-time spectral sweep, remove areas identified by the matched positive and negative gradients, and connect points between removed areas to determine a baseline;wherein the apparatus is operable to subtract the baseline from the real-time spectral sweep to reveal the at least one signal; andwherein one or more of the at least one signal is a narrowband signal hidden in a wideband signal. 17. The apparatus of claim 16, wherein at least one knowledge map is stored in the apparatus. 18. The apparatus of claim 16, wherein the apparatus is operable to obtain a different knowledge map by communicating with another apparatus. 19. The apparatus of claim 16, wherein the apparatus is automatic and unmanned. 20. The apparatus of claim 16, wherein the apparatus is water resistant.
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