ARIA: ADAPTIVE RADAR INTELLIGENCE ARCHITECTURE
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Abstract
Some of the common operational challenges in modern radar systems are high false alarm rates, detection-to-track latency, terrain-induced blind zones, and vulnerability to Electronic Counter Measures (ECM). This paper presents ARIA (Adaptive Radar Intelligence Architecture), an AI-augmented framework that integrates five complementary technologies: CNN-based target classification, LSTM predictive tracking, multi-static distributed sensor mesh networking, cognitive Electronic Counter-Countermeasures (ECCM), and autonomous threat assessment. The proposed architecture combines SAR and Doppler radar data using a ResNet-inspired CNN with multi-head attention and integrates LSTM-based trajectory prediction with an Interacting Multiple Model (IMM) framework. Simulation-based evaluation against published literature suggests a projected target classification accuracy of 85–92% under operational conditions, compared with current field baselines of 72–83%. These performance values are presented as research hypotheses requiring field validation. The primary contribution of ARIA is not a validated operational system, but a unified architectural framework that integrates existing AI and radar technologies into a coherent design for next-generation intelligent radar systems.
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No new datasets were generated or analysed during this
study. This research is based on a theoretical framework
development and simulation analysis. All relevant data
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