Comprehensive Integration of Modeling, Detection and Tracking in Cognitive Radar Systems

This paper presents a comprehensive study on cognitive radar systems, emphasizing the integration of advanced modelling, adaptive detection, and intelligent tracking techniques to improve performance in complex and dynamic environments, also it reviews key frameworks such as the Fully Adaptive Radar (FAR), likelihood ratio-based target detection, and Track-Before-Detect (TBD) techniques that enhance detection under low signal-to-noise ratio (SNR) conditions, also the study also analyses recent innovations including the Cognitive Structure Adaptive Particle Filter with Probabilistic Data Association (CSAPF-PDA), cost-aware particle filters, and Deep Reinforcement Learning (Deep RL) models. These approaches enable cognitive radars to dynamically manage waveform parameters and sensor resources, significantly improving detection probability, reducing false alarms, and increasing tracking accuracy. A detailed performance comparison is conducted across five cognitive radar models using simulation metrics including probability of detection (Pd), probability of false alarm (Pfa), and root mean square error (RMSE). Results demonstrate that models such as Deep RL and Cognitive Cost-Reference Particle Filter (CCRPF) offer superior performance, especially in cluttered and low SNR environments. The paper concludes by identifying open challenges in real-time adaptation, spectrum coexistence, and interference mitigation, offering guidance for future research in building highly autonomous and efficient radar systems.