Neural network based moving target indicator for radar applications

Farid Amoozegar

doi:10.1117/12.205206

6 April 1995 Neural network based moving target indicator for radar applications

Farid Amoozegar

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205206
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

Although techniques of radar signal processing, including the moving target indicator (MTI), have been vastly improved by the availability of digital computers in recent years, these methods are generally based on complex mathematical procedures which make the engineering and design of radar receivers rather costly and vulnerable to electronic faults. On the other hand, biological systems (e.g., insects, birds) have capabilities far beyond those of the conventional MTI processors. This paper provides some evidence that Doppler shifts can easily be extracted with neural networks even in situations where only a limited number of noisy pulses are available for processing. Furthermore, it is easier to shape the frequency response of the neural network-based MTI (NN-MTI) as desired without needing the complex process of pole placement, which is traditionally required in both digital and analog filter design procedures. The nonlinear processing capability of a neural network is utilized to efficiently combine the Doppler processing and integration performed by conventional MTI and its coprocessor (i.e., integrator). The MTI implementation with neural networks reduces the number of required independent pulses for Doppler shift extraction in the presence of clutter. There are several other conflicting requirements for the optimum MTI design where the algorithmic procedures may not be as efficient. Therefore, shaping the magnitude frequency response of MTI filters demands the flexibilities offered by neural networks.

Citation Download Citation

Farid Amoozegar "Neural network based moving target indicator for radar applications", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205206

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KEYWORDS

Neural networks

Doppler effect

Radar

Digital filtering

Fourier transforms

Filtering (signal processing)

Modulation

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