Spiking Neuromorphic Network for SAR Target Recognition
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Solution Overview
Problem
Existing synthetic aperture radar (SAR) systems face challenges in real-time target recognition due to computationally intensive image synthesis processes, which require significant computing power and are not suitable for applications with size, weight, and power (SWAP) constraints, such as unmanned aerial vehicles (UAVs).
Innovation Solution
A system utilizing a multi-layer recurrent spiking neuromorphic network that extracts features from raw SAR data, encodes them as spiking signals, and processes these signals to identify targets without the need for intermediate image synthesis, implemented using a neuromorphic chip for efficient power consumption and real-time processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image synthesis methods are used to process SAR data, then target recognition accuracy is improved, but computing power requirements and SWAP consumption increase significantly
Solution Approach 1:
The patent replaces traditional computational image synthesis with a spiking neuromorphic network that uses biological-inspired neural computing. This substitution of mechanical/computational systems with bio-inspired systems enables real-time processing with reduced SWAP requirements while maintaining target recognition accuracy.
Solution Approach 2:
The patent changes the fundamental parameters of data representation from conventional pixel-based images to spiking neural network representations. This parameter transformation allows the system to process SAR data directly in the spiking domain, eliminating the need for computationally intensive image synthesis while preserving recognition capability.
2Productivity
If conventional neural networks are used for SAR target recognition, then recognition capability is achieved, but power consumption is high
Solution Approach 1:
The spiking neuromorphic network employs periodic spiking actions rather than continuous computation. Neurons fire spikes periodically based on input signals, which dramatically reduces power consumption compared to continuous operations of conventional neural networks while maintaining effective target recognition.
Solution Approach 2:
The spiking neural network utilizes event-driven processing where computation occurs only when spikes are generated by input signals. This self-service mechanism eliminates unnecessary computational operations, reducing power consumption while preserving recognition capability for SAR targets.
3Device complexity
If real-time processing is implemented with limited hardware resources, then SWAP constraints are satisfied, but processing speed may be reduced
Solution Approach 1:
The spiking neuromorphic network performs preliminary feature extraction and encoding directly from raw SAR data into spiking representations. This preliminary action eliminates the need for subsequent image synthesis steps, enabling real-time processing with reduced SWAP requirements while maintaining high processing speed.
Data Source
AI summary
A system configured to identify a target in a synthetic aperture radar signal includes: a feature extractor configured to extract a plurality of features from the synthetic aperture radar signal; an input spiking neural network configured to encode the features as a first plurality of spiking signals; a multi-layer recurrent neural network configured to compute a second plurality of spiking signals based on the first plurality of spiking signals; a readout neural layer configured to compute a signal identifier based on the second plurality of spiking signals; and an output configured to output the signal identifier, the signal identifier identifying the target.