Design of Multilayer Airborne Radar Data Processor

The paper proposes design of multiple-layer robust airborne radar target tracking system. The design also discusses the algorithms that aid in airborne tracking process, different techniques of clutter mitigation at data processor, false tracks suppression and techniques for improvising track maintenance functionality. The objective of the design is to develop a practical, ready to deploy, modular airborne multiple target tracking system. The design is based on layered approach wherein, each layer of tracking system is designed to meet specific function. The proposed approach has three layers with each layer performing a specific function. The first layer is called pre-processing layer with main functionality of clutter mitigation and suppression of unwanted plots. The second layer is targeted to track non-maneuvering targets and detection of target maneuvers. The main functionality of third layer is to track maneuvering targets. The paper also discusses about the interaction between these tracking layers. Some of the salient issues addressed in the paper are suppression of false tracks arising from clutter leaks, ground moving targets, windmills, ghosts and multipath detections. The false tracks could clutter air situation picture and penalize radar resources. The innovation of the paper is in evolving and partitioning of airborne target tracking algorithms into different layers and seamless integration of these layers. The algorithms focus on reduction of false and unwanted tracks in airborne radar, improvement of detection performance through feedback from non maneuver tracking layer and maneuver target tracking layer. The layered design provides advantages in terms of reusability, configurability, maintainability and is scalable and adaptive. The concept of layering facilitates efficient usage of all algorithms. The layered software concept alleviates the burden placed on combat mission commanders since some of the tasks intelligently collate all information along with historical data and hence resolving ambiguous tracks whenever necessary, rejecting the improbable data and adapt automatically to environmental changes.