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Cognitive Electronic Warfare System Market To Receive Overwhelming Hike In Future To 2028

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BIS RSRCH
Cognitive Electronic Warfare System Market To Receive Overwhelming Hike In Future To 2028

During a war, army officials often complain about false signals and intercepts, which are produced in order to mask one’s military equipment and strategy. Adaptive electronic warfare system can only combat a list of known threats and are not effective when a new unknown threat is detected. Hence, defense experts suggest that the usage of artificial intelligence and machine learning techniques in electronic warfare can lessen the potential risk. As the electromagnetic spectrum is complex, the introduction of AI and ML is critical to eradicate the difficulties associated with the electromagnetic spectrum and empower new military capabilities. As military equipment is making a transition toward artificial intelligence, the demand for cognitive electronic warfare system is expected to rise during the forecast period.

The major countries expanding their strategies to implement artificial intelligence in the military are Russia, the U.S., and China. AI is needed to handle the data and information of huge sensors and communication networks, as the quantity of information is increasing. AI and machine learning offer potential benefits to military organizations by providing fast and high-quality data which can help in deep analysis of complex and strategic data. Since cognitive electronic warfare system make use of artificial intelligence and machine learning techniques, they are expected to overcome most of the drawbacks of electronic warfare system, such as incapability of handling excessive load and being non-responsive to unknown threats.

Machine learning (ML) needs computers for predicting its models however, computers don’t explain their predictions which is a limitation to adopt machine learning. Interpretation of machine learning models is prominent for humans to understand the cause of decision and result of the model. The higher the interpretability, the easier it is to understand the predictions of the model. AI and ML models are used by military for storing data, deep analysis of data and qualitative data of sensors and communication networks.

Current military systems face a challenge of not being able to store voluminous data and require frequent re-programming of the data. The usage of AI and ML models are expected to help overcome this challenge. However, machine learning and AI have their own drawbacks, such as not being able to define all the variables and parameters on which the machine learning model depends on. Army and navy officials would need to go under rigorous algorithm training programs to understand complex machine learning models. The military equipment that function on the cognition principles are expected to encounter difficulties in the interpretation of ML models. Even the simplified form of representation provided by machine learning models, such as decision trees, are very hard to interpret given the volume of data and variables the system depends on. Additionally, systems that use machine learning models can easily receive wrong information as minute errors in data input can produce false signals that are unknown to the users. Complexity in comprehending interpretability of machine learning models, frequent validation procedures, and rigorous training to understand and implement machine learning models make it a major challenge for the adoption of these systems.

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The electronic warfare system technology relies on the signal generation and reception from another source. The history of electronic warfare dates to the 20th century, which played an important role in modernizing warfare. As time and technology progressed, electronic warfare saw major advancements. However, in the present generation, the adaptive electronic warfare system is used and developing the cognitive electronic warfare system, for future use. Adaptive electronic warfare system have the ability of recognizing a change in the environment and act accordingly to jam signals. Cognitive electronic warfare system will be introduced by 2023 in the market, which will be advance and better version of electronic warfare system. These systems are based on learning action framework, which is way ahead of the traditional mechanism. These system use machine learning algorithms and pattern recognition to imitate human perception of learning, memory, and judgement with the help of artificial intelligence.

The report constitutes an extensive study of the cognitive electronic warfare system market. The report focuses largely on providing market information for cognitive electronic warfare system by covering different segments, platforms, capabilities, and regions. In addition to this, the study focuses on the major driving forces, challenges, and growth opportunities. The major players have been identified on the basis of their revenue generation, geographical presence, and company developments related to the cognitive electronic warfare system market. Details of company profiles have been included in order to understand the strategic behaviours of the market players. The cognitive electronic warfare system market is further explained and analyzed on the basis of region, which is categorized into four regions, namely: North America, Europe, Asia-Pacific, and the Rest-of-the-World. Moreover, the country analysis has also been done in order to have a clear picture of the cognitive electronic warfare system market. Regional developments experienced by the manufacturers and undertaken by governments are some of the factors based on which the growth rates of the countries have been calculated.

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