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Robust radar target classifier using artificial neural networks.

S Chakrabarti¹, N Bindal, K Theagharajan

¹Dept. of Electr. and Comput. Eng., Kansas Univ., Lawrence, KS.

IEEE Transactions on Neural Networks

|January 1, 1995

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This study introduces an artificial neural network (ANN) radar target classifier, demonstrating superior performance over conventional methods, especially in low signal-to-noise environments for aircraft identification.

Area of Science:

Electromagnetics
Artificial Intelligence
Signal Processing

Background:

Radar target classification is crucial for identification.
Conventional methods like minimum distance classifiers have limitations.
Artificial Neural Networks (ANNs) offer potential for improved classification.

Purpose of the Study:

To present an ANN-based radar target classifier.
To compare its performance against a conventional minimum distance classifier.
To evaluate performance under varying signal-to-noise ratios and with preemphasis filtering.

Main Methods:

Synthesizing realistic aircraft radar returns using a thin wire time domain electromagnetic code.
Processing time-varying backscattered electric fields.

Related Experiment Videos

Training a multilayer feedforward ANN using a backpropagation learning algorithm.

Comparing ANN performance with a conventional minimum distance classifier.

Main Results:

The ANN-based classifier achieved a higher percentage of successful classifications.
ANN performance was particularly effective in low signal-to-noise ratio environments.
Preemphasis filtering was applied to enhance target response contributions for both methods.

Conclusions:

Multilayer feedforward ANNs trained with backpropagation are effective for radar target classification.
ANNs outperform conventional methods, especially under challenging low signal-to-noise conditions.
The ANN approach shows significant promise for enhancing radar target identification capabilities.