	US 12,455,364 B2
	Detecting, tracking, and transmitting to identified objects using models in a modular system
Sam El-Akkad, Poway, CA (US); Christopher Fischer, Lake Forest, CA (US); Travis Whitaker, Costa Mesa, CA (US); Bryden Pearson, Irvine, CA (US); Todd Berk, Lake Forest, CA (US); Thao Pham, San Diego, CA (US); Jon Hsu, Yorba Linda, CA (US); and Cameron Dart, Fountain Valley, CA (US)
Assigned to Anduril Industries, Inc., Costa Mesa, CA (US)
Filed by Anduril Industries, Inc., Costa Mesa, CA (US)
Filed on Nov. 1, 2022, as Appl. No. 17/978,807.
Claims priority of provisional application 63/420,247, filed on Oct. 28, 2022.
Claims priority of provisional application 63/365,115, filed on May 20, 2022.
Prior Publication US 2024/0094375 A1, Mar. 21, 2024
Int. Cl. G01S 13/58 (2006.01); G01S 13/02 (2006.01); G01S 13/52 (2006.01); G01S 13/524 (2006.01)

CPC G01S 13/582 (2013.01) [G01S 13/0209 (2013.01); G01S 13/524 (2013.01)]

20 Claims

1. A computer-implemented method for applying a machine learning model to identify one or more RF signals, the computer-implemented method comprising, by one or more hardware processors executing program instructions:

receiving raw RF signal data through two or more directional antennas corresponding to a first RF system, wherein the two or more directional antennas are configured to be selectably activated, wherein the first RF system comprises a processing module, and wherein the processing module comprises a machine learning component;

sampling, by the processing module of the first RF system, the raw RF signal data to generate sampled RF signal data;

transmitting the sampled RF signal data to the machine learning component for input into a machine learning model;

receiving, from the machine learning component, an output from the machine learning model; and

based on RF signal attributes and the output, identifying a type of an object, wherein the RF signal attributes comprise bandwidth, channel, and signal rate that are each associated with the raw RF signal data.