Deep learning methods and systems for detection of pulse signals

A method for decoding multipulse pulse position modulation (MPPM) symbols included in a received data signal, the method comprising:

• receiving an incoming target data signal, the incoming signal including a data bitstream represented as a series of pulses based on an MPPM symbol representative of a series of time slots where a pulse may or may not occur, and the incoming target data signal also including one or more noise elements,
• using the continuous wavelet transformation (CWT) process to obtain a time-frequency representation of the received incoming data signal;
• using a scalogram process to convert the time-frequency representation of the received training data signal into a plurality of training images, each training image associated with a single bit of information which includes either a high bit state or a low bit state;
• and
• using a trained deep learning architecture platform to assign probabilities in a decision matrix, the probabilities associated with the likelihood that a 1 or 0 bit was transmitted for specific time slots associated with the MPPM symbol,
• determining a location of the highest probability that a 1 bit (high) was received;
• comparing the location of a received bit with the highest probability to possible sequences to identify a matching bit sequence associated with a predetermined set of possible bit sequences to identify a matching bit sequence;
• and
• decoding the symbol based on the matching bit sequence.

A system for detecting pulsed communication signals, the system comprising:

• a data signal receiver processing a received training data signal, which includes a noise element, with a continuous wavelet transformation (CWT) process to obtain a time-frequency representation of the received training data signal, the received training data signal representative of a known/training bitstream associated with a transmitted pulse signal;
• a scalogram module converting the time-frequency representation of the received training data signal into a plurality of training images, each training image associated with a single bit of information which includes either a high bit state or a low bit state;
• a deep learning architecture platform operatively associated with the scalogram module, training the deep learning architecture platform with the plurality of training images to generate a classification model representative of a plurality of high bit states and a plurality of low bit states included in the plurality of training images;
• and the system for detecting pulse signals performing the method comprising:
• receiving an incoming target data signal, the incoming signal including a data bitstream represented as a series of pulses and the incoming signal also including one or more noise elements, using the CWT process to obtain a time-frequency representation of the received incoming data signal, and using the scalogram process to convert the time-frequency representation of the received incoming target data signal into a plurality of respective target images representative of each of the bit states in the incoming target data signal bitstream;
• using the trained deep learning architecture platform, classifying each of the plurality of respective target images as one of a high bit state and a low bit state, and
• generating an output bitstream based on the plurality of respective target images associated with the incoming data signal,
• wherein the incoming target data signal is one or both of turbo encoded and multipulse pulse position modulation (MPPM) encoded.