A track-before-detect strategy based on sparse data processing for air surveillance radar applications

Nicomino Fiscante, Pia Addabbo, Carmine Clemente, Filippo Biondi, Gaetano Giunta, Danilo Orlando

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Abstract

In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we present a novel approach based on a three-stage processing chain that involves the Sparse Learning via Iterative Minimization algorithm, the k-means clustering method and the ad hoc detector by exploiting the sparse nature of the operating scenario. Under the latter assumption, the detection strategy declares the presence of targets subsequently to the retrieval of their corresponding tracks performed by jointly processing the received echoes of multiple consecutive radar scans. Simulation results show that the proposed approach is able to provide good tracking and detection capabilities for different multiple target trajectories with low Signal-to-Interference-plus-Noise ratio and results in providing advantages when compared to a number of other reference Track-Before-Detect strategies based on sparse data processing techniques.
Original languageEnglish
Article number662
Number of pages19
JournalRemote Sensing
Volume13
Issue number4
DOIs
Publication statusPublished - 12 Feb 2021

Keywords

  • air surveillance radar
  • clustering
  • machine learning
  • multiple-target tracking
  • radar
  • sparse data recovery
  • Track-Before-Detect

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