Drone detection and classification with compensation for background clutter sources

A system configured to detect drones comprising:

• a drone detection device including:
• a microphone configured to receive a sound signal;
• a sound card configured to record a digital sound sample of the sound signal;
• and
• a frequency processor configured to:
• apply a frequency transformation to the digital sound sample to create a sample time-frequency spectrum,
• determine a sample power spectral density vector from the sample time-frequency spectrum, and
• transmit the sample power spectral density vector for further analysis;
• and
• a server communicatively coupled to the drone detection device including:
• a memory storing a plurality of drone sound signatures and a plurality of background sound signatures;
• and
• a detection processor configured to
• determine which of the plurality of sound signatures in the memory are activated using a non-negative matrix factorization algorithm by determining a combination of the drone and background sound signatures stored in the memory that most closely match the sample power spectral density vector,
• determine if at least one of the plurality of drone sound signatures are activated, and
• conditioned on the at least one of the plurality of drone sound signatures being activated, at least one of (i) transmit an alert indicative of a drone, and (ii) activate a beamformer in proximity of the drone detection device to determine a location of a drone associated with the sound signal.

A method for detecting drones comprising:

• receiving, via an interface, a digital sound sample;
• partitioning, via a processor, the digital sound sample into segments;
• applying, via the processor, a frequency and power spectral density transformation to each of the segments to produce respective sample vectors;
• for each of the sample vectors, determining, via the processor, a combination of drone sound signatures and background sound signatures stored in a memory that most closely match the sample vector;
• applying for each of the sample vectors, via the processor, a sparsity constraint to the combination of drone sound signatures and background sound signatures to select a minimal combination of sound signatures needed to closely match the respective sample vector;
• determining, via the processor, for the sample vectors, if the drone sound signatures in relation to the background sound signatures that are included within the respective minimal combinations are indicative of a drone;
• and
• conditioned on determining the drone sound signatures are indicative of a drone, transmitting an alert message indicative of the drone.