Automatic target recognition

ATR can be used to identify man-made objects such as ground and air vehicles as well as for biological targets such as animals, humans, and vegetative clutter.

This can be useful for everything from recognizing an object on a battlefield to filtering out interference caused by large flocks of birds on Doppler weather radar.

Research has been done into using ATR for border security, safety systems to identify objects or people on a subway track, automated vehicles, and many others.

However, this idea of audibly representing the signal did provide a basis for automated classification of targets.

Several classifications schemes that have been developed use features of the baseband signal that have been used in other audio applications such as speech recognition.

The simplest method to obtain a function of frequency and time is to use the short-time Fourier transform (STFT).

However, more robust methods such as the Gabor transform or the Wigner distribution function (WVD) can be used to provide a simultaneous representation of the frequency and time domain.

This is done by modeling the received signal then using a statistical estimation method such as maximum likelihood (ML), majority voting (MV) or maximum a posteriori (MAP) to make a decision about which target in the library best fits the model built using the received signal.

Studies have been done that take audio features used in speech recognition to build automated target recognition systems that will identify targets based on these audio inspired coefficients.

After a model is obtained using the data collected, conditional probability is formed for each target contained in the training database.