Method and device for predicting impact point based on non-linear maneuver identification of flying object

A device for predicting an impact point, the device comprising:

• a communication interface;
• a memory including an instruction;
• and
• a processor configured to perform, by executing the instruction:
• acquiring detection information related to a flying object detected by a radar linked to the device through the communication interface;
• acquiring prior information including information about an enemy unit, a friendly unit, and a friendly facility;
• determining whether the detected flying object is a flying object capable of non-linear maneuver based on the detection information and the prior information, using a neural network model pre-trained using training data so that the neural network model determines whether the flying object is the flying object capable of non-linear maneuver based on detection information and prior information;
• and
• determining an impact point based on the detection information, the prior information, and a determining result of the neural network model on whether the detected flying object is the flying object capable of non-linear maneuver,
• wherein the neural network model includes an input layer set to input detection information and prior information of a flying object, an output layer outputting the determining result, and a model parameter trained to have a correlation between the detection information, the prior information, and the determining result based on a supervised training by using label information specifying whether the flying object is capable of non-linear maneuver.

A non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, comprises an instruction for causing the processor to perform a method comprising:

• acquiring detection information related to a flying object detected by a radar;
• acquiring prior information including information about an enemy unit, a friendly unit, and a friendly facility;
• determining whether the detected flying object is a flying object capable of non-linear maneuver based on the detection information and the prior information, using a neural network model pre-trained using training data so that the neural network model determines whether the flying object is the flying object capable of non-linear maneuver based on detection information and prior information;
• and
• determining an impact point based on the detection information, the prior information, and a determining result of the neural network model on whether the detected flying object is the flying object capable of non-linear maneuver,
• wherein the neural network model includes an input layer set to input detection information and prior information of a flying object, an output layer outputting the determining result, and a model parameter trained to have a correlation between the detection information, the prior information, and the determining result based on a supervised training by using label information specifying whether the flying object is capable of non-linear maneuver.