Cognitive Electronic Warfare System Market May Set New Growth Story, Forecast to 2033

Cognitive electronic warfare (EW) systems use machine learning algorithms to detect new or unknown threats. These systems are capable of understanding different patterns based on the high-quality signal data. Militaries as well as prominent companies from different parts of the world such as the U.S., Russia, the U.K., France, China, and Australia, are actively striving to develop advanced cognitive EW systems for future battlefield platforms.

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The electronic support segment currently dominates the global cognitive electronic warfare system market due to the demand for tactical sensing for real-time response in future across the globe. The SWEIP Block II Antennas and Receivers (U.S.) and F-16 Scalable Agile Beam Radar (SABR) Fir Control Radar (U.S.) are the two key programs in this regard. The cognitive electronic warfare system sector has been witnessing numerous developments over the past years due to various factors such as increasing need for artificial intelligence in military coupled with rising need for improving situational awareness in defense technologies. The cognitive electronic warfare system sector is currently driven by a series of defense capability modernization programs.

The computers are unable to predict machine learning models by their own, which acts as a limitation. There is an increased need for humans to understand the output from a machine learning model as it helps in analyzing important patterns and predictions out of it. Defense authorities are increasingly using machine learning models for storing and analyzing high quality data gathered from sensor and communication networks. The systems being used by the military are not technologically advanced and are hence, not able to store large amounts of data and require frequent re-programing. Machine learning based electronic warfare systems are capable of overcoming this challenge. However, these systems have their own drawbacks associated with improper variable and parameter consideration for developing the machine learning model. Models such as decision trees require significant amount of understanding, owing to which the defense authorities are increasingly focusing toward training staff on predictive analytics. In addition, minute errors in the data produce false output in machine learning models and act as a threat to the end user. Complexity in comprehending interpretability of machine learning models, frequent validation procedures, and requirement of rigorous training to understand and implement machine learning models make it a major challenge for the adoption of these systems.

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North America is the key region witnessing significant implementation of artificial intelligence technology in the electronic warfare system. The expenditure on emerging technologies, including cyber warfare, is expected to grow at a substantial pace during the forecast period. The U.S. is another major country that is increasingly investing in the development of new and technologically advanced electronic warfare systems. RF Machine Learning Systems Program., Radio Frequency Machine Learning Systems Program, and ARC Program are the major programs launched by Defense Advanced Research Projects Agency (DARPA), on cognitive electronic warfare systems.

Russia is also an important country where cognitive electronic warfare system market is getting significant traction. The increasing demand for electronic warfare systems, which are highly mobile and technologically advanced from various end users such as commercial, defense, and government, is the key reason fueling the market growth in the country.

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