Methods and systems for acoustic machine perception for an aircraft

A method comprising:

• causing an array of microphones arranged on an aircraft to acquire target acoustic data associated with a target object within an environment of the aircraft;
• and
• executing, by a processor, a machine learning model to predict, based on the target acoustic data, an azimuth of the target object, a range of the target object, an elevation of the target object, and a type of the target object,
• wherein the machine learning model was trained by (i) causing one or more sensors arranged on the aircraft to acquire first data associated with a first object that was within the environment of the aircraft, wherein the one or more sensors include one or more of a light detection and ranging sensor, a radar sensor, or a camera, (ii) causing the array of microphones arranged on the aircraft to acquire, over approximately the same window of time as the first data was acquired, first acoustic data associated with the first object, (iii) the processor using the first acoustic data as an input value to the machine learning model, and (iv) the processor using an azimuth of the first object, a range of the first object, an elevation of the first object, and a type of the first object identified from the first data as ground truth output labels for the machine learning model.

A system comprising:

• an aircraft;
• one or more sensors arranged on the aircraft, wherein the one or more sensors include one or more of a light detection and ranging sensor, a radar sensor, or a camera;
• an array of microphones arranged on the aircraft;
• and
• a computing device having a processor and memory storing instructions executable by the processor to perform a set of operations comprising:
• causing the one or more sensors to acquire, over a window of time, first data associated with a first object that is within an environment of the aircraft;
• causing the array of microphones to acquire, over approximately the same window of time as the first data is acquired, first acoustic data associated with the first object;
• and
• training a machine learning model by using the first acoustic data as an input value to the machine learning model and by using an azimuth of the first object, a range of the first object, an elevation of the first object, and a type of the first object identified from the first data as ground truth output labels for the machine learning model, wherein the machine learning model is configured to predict, based on target acoustic data subsequently acquired by the array of microphones and associated with a target object within the environment of the aircraft, an azimuth of the target object, a range of the target object, an elevation of the target object, and a type of the target object.