System and method for real-time radar range-doppler map learning
AssigneeHRL Laboratories, LLC
InventorsPeter Petre, Amir M. Rahimi
Life of this patent
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Abstract
Described is a system for real-time radar range-doppler map learning. The system includes a physics enhanced dynamic reservoir core operable for receiving as an input signal a time delayed copy of a transmitted complex in-phase and quadrature (I/Q) radar waveform and mapping the input signal onto a neural network to determine complex-valued output weights of neural states of the neural network. An online learning layer is included that adapts the complex-valued output weights of the neural states to predict a most likely next value of the input signal. A neural combiner is used to combine a set of delayed neural state vectors with the complex-valued output weights of the online learning layer to compute a denoised and decluttered output signal.
Claims12
A system for real-time radar range-doppler map learning, the system comprising:
- a physics enhanced dynamic reservoir core operable for receiving as an input signal a time delayed copy of a transmitted complex in-phase and quadrature (I/Q) radar waveform, and mapping the input signal onto a neural network to determine complex-valued output weights of neural states of the neural network;
- an online learning layer that adapts the complex-valued output weights of the neural states to predict a most likely next value of the input signal, wherein the complex-valued output weights are continuously updated via a gradient descent online learning algorithm;
- a neural combiner for combining a set of delayed neural state vectors with the complex-valued output weights of the online learning layer to compute a denoised and decluttered output signal, the output signal being separate in-phase and quadrature signals.
A computer program product for real-time radar range-doppler map learning, the computer program product comprising:
- a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions by one or more processors, the one or more processors perform operations of:
- receiving as an input signal a time delayed copy of a transmitted complex in-phase and quadrature (I/Q) radar waveform;
- mapping the input signal onto a neural network to determine complex-valued output weights of neural states of the neural network;
- adapting the complex-valued output weights of the neural states to predict a most likely next value of the input signal, wherein the complex-valued output weights are continuously updated via a gradient descent online learning algorithm;
- and
- combining a set of delayed neural state vectors with the complex-valued output weights of the online learning layer to compute a denoised and decluttered output signal, the output signal being separate in-phase and quadrature signals.
A computer implemented method for real-time radar range-doppler map learning, the method comprising an act of:
- causing one or more processers to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of:
- receiving as an input signal a time delayed copy of a transmitted complex in-phase and quadrature (I/Q) radar waveform;
- mapping the input signal onto a neural network to determine complex-valued output weights of neural states of the neural network;
- adapting the complex-valued output weights of the neural states to predict a most likely next value of the input signal, wherein the complex-valued output weights are continuously updated via a gradient descent online learning algorithm;
- and
- combining a set of delayed neural state vectors with the complex-valued output weights of the online learning layer to compute a denoised and decluttered output signal, the output signal being separate in-phase and quadrature signals.
Dependencies are inferred from the claim text.