Design and Testing of Antennae for Automatic Target Recognition in UWD Radar

The means of sending and receiving information without physical interaction has always been an area of interest. Communication engineers are keen to improve the
quality of the process of exchange of information using wireless methods. Antennas are
the most vital part of a wireless network. The reliability of information exchange through a
wireless system much depends on the functionality of the antennas, both at the transmitter and
receiver. Thus, the design of a near-to-perfect antenna has always been an inevitable ambition of
a communication engineer. Design of microstrip anten- nas and slot antennas have remained a
globally trending area of research for several decades now. Many novel approaches have
evolved for designing microstrip antennas and slot antennas of di¿erent physical and
electromagnetic properties at the microwave frequencies. As the need for bandwidth is increasing
over the years, researchers are ex- ploring paths to design THz antennas. The already developed
know-how for the design of microstrip antennas is found to be handy in this direction.
Metamaterials are the arti¿cial structures whose properties are not easily found in nature. From
this general- ized notion, the focus of the research and development of metamaterial can be in any
part, if the proposed arti¿cial structure follows the unique electromagnetic properties.

The capability to recognize a targeted object accurately by its radar return alone
under all environmental conditions is of great concern to automatic target recognition (ATR)
systems. ATR generally refers to the use of computer processing to detect and recognize the target
signature in sensor data. ATR has become increasingly important in modern defence strategy because
it permits precision strikes against speci¿c tactical targets with minimized risk and maximized
e¿ciency while decreasing surety damage to other objects. A key advantage of using tools such as
Arti¿cial Neural Network (ANN) is that, since the algorithms automatically remember the available
data, the resulting high-performance algorithms are customized to the variable data.