Target priority based optimisation of radar resources for networked air defence systems

Tuncer O., Çırpan H. A.

IET RADAR SONAR AND NAVIGATION, cilt.16, sa.7, ss.1212-1224, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 7
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1049/rsn2.12255
  • Dergi Adı: IET RADAR SONAR AND NAVIGATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1212-1224
  • Anahtar Kelimeler: air defence, Bayesian Cramer-Rao lower bound (BCRLB), fuzzy logic, networked radar resource management, sensor management, threat assessment, MANAGEMENT
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Air Defence System (ADS) effectiveness relies not only on weapon and sensor performances but also on effective usage of those resources. In an ADS Network, several ADS are deployed in an overlapping coverage in order to increase the protection of defended assets (DA). Limited tracking capacity of ADS fire control radars leads to a limited engagement capability of the whole ADS network. In this paper, a radar resource optimisation algorithm is proposed for networked ADS fire control radars in order to apply an adaptive tracking strategy for threatening targets under the constraint of tracking capacity limitation for each fire control radar. Threat posed by the targets is calculated using a Fuzzy Logic System (FLS) that takes into account the variables related to threat as well as DAs. An optimisation strategy is utilised in order to select the minimum number of tracking radars that will minimise the cost function formed by tracking quality metrics related to threat priorities and Bayesian Cramer-Rao lower bound (BCRLB). Proposed algorithm dynamically selects target tracking quality and minimises tracking resources in an ADS by utilising surrogate optimisation suitable for real-time implementation. Simulation results are given in order to show the improvement in defence effectiveness of networked ADS.