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Electronic Warfare is one of the major deciding factors in the success of conventional and unconventional warfare. As RF technology is advancing with a very high pace, the conventional Electronic Warfare equipment are getting obsolete along with. Pakistan Army has indoctrinated a large quantity of Electronic Warfare equipment and still more is required to compete current
requirement, owing to high tension scenario at borders and operational areas. This high-cost equipment are prone to frequent upgradation and maintenance as well as being highly resource dependent. Considering above mentioned issues related to the EW equipment that Pak Army is using, they are seldom utilized for difficult terrains and border areas and are to be kept as reserve
for conventional warfare. Automatic detection and classification of radio frequency (RF) signals is a critical task in modern communication systems. [1] With the increasing complexity of wireless communication systems and the growing demand for spectrum usage, it has become increasingly important to develop efficient and effective methods for detecting and classifying RF signals. This paper presents a novel approach for automatic detection of RF signals based on GNU radio software. The proposed method uses a combination of feature extraction and classification techniques to identify different types of RF signals. [1] GNU Radio is an open-source software-defined radio framework that is used to develop signal processing applications. It provides a range of signal processing blocks that can be used to build signal processing pipelines. These blocks are used to perform a variety of signal processing tasks, including filtering, modulation, demodulation, and decoding. To implement automatic detection and classification of RF signals using GNU Radio, one can use the software to build a signal processing pipeline that includes blocks for signal acquisition, feature extraction, and classification. The signal acquisition block can be used to capture RF signals from a radio receiver, while the feature extraction block can be used to extract relevant features from the captured signals. These features can include signal frequencies, bandwidth, modulation type and other relevant parameters.