Cross-correlation system and method for spatial detection using a network of RF repeaters

A cross-correlation method, comprising:

• inputting, by control circuitry, training sensor data from a plurality of sensors into an input layer of a deep neural network;
• training, by the control circuitry, the deep neural network for:
• cross-correlating the training sensor data from the plurality of sensors at an input sampling stage,
• determining a relationship between the cross-correlated sensor data and an identity of each of one or more users represented in the cross-correlated sensor data, and
• tracking subsequent movements of the one or more users based on the cross-correlated sensor data;
• obtaining, by the control circuitry, first sensor data of a first user from a communication system and second sensor data from a first portable device carried by the first user;
• obtaining, by the control circuitry, third sensor data from a plurality of user-devices associated with the first user;
• obtaining, by the control circuitry, cross-correlated information of the first user by utilizing the trained deep neural network;
• recognizing, by the control circuitry, a first gesture specific to the first user based on the cross-correlated information of the first user, wherein the first gesture corresponds to a single hand gesture;
• detecting, by the control circuitry, a first configuration of a first hand of the first user;
• splitting, by the control circuitry, the first gesture into a first component and a second component, wherein a configuration of a defined number of fingers of the first hand form the first component of the first gesture, and a configuration of remaining fingers form the second component of the first gesture;
• identifying, by the control circuitry, a first controllable device from a plurality of controllable devices based on the first component, and identify a first action based on the second component;
• and
• controlling, by the control circuitry, the first controllable device identified from the plurality of controllable devices to execute the first action based on the first gesture.

A non-transitory computer-readable medium having stored thereon, computer implemented instructions, which when executed by a computer in a communication apparatus, causes the communication apparatus to execute operations, the operations comprising:

• inputting training sensor data from a plurality of sensors into an input layer of a deep neural network;
• training the deep neural network for:
• cross-correlating the training sensor data from the plurality of sensors at an input sampling stage,
• determining a relationship between the cross-correlated sensor data and an identity of each of one or more users represented in the cross-correlated sensor data, and
• tracking subsequent movements of the one or more users based on the cross-correlated sensor data;
• obtaining first sensor data of a first user from a communication system and second sensor data from a first portable device carried by the first user;
• obtaining third sensor data from a plurality of user-devices associated with the first user;
• obtaining cross-correlated information of the first user by utilizing the trained deep neural network;
• recognizing a first gesture specific to the first user based on the cross-correlated information of the first user, wherein the first gesture corresponds to a single hand gesture;
• detecting a first configuration of a first hand of the first user;
• splitting the first gesture into a first component and a second component, wherein a configuration of a defined number of fingers of the first hand form the first component of the first gesture, and a configuration of remaining fingers form the second component of the first gesture;
• identifying a first controllable device from a plurality of controllable devices based on the first component, and identify a first action based on the second component;
• and
• controlling the first controllable device identified from the plurality of controllable devices to execute the first action based on the first gesture.