Analyses of the performance of adaptive subspace detector on fluctuating target detection in system-dependent clutter background

The adaptive subspace detector in system-dependent clutter background (SDC-ASD) is proved to be able to improve the detection performance for deterministic target detection in the authors' previous study. However, the exact performance of the SDC-ASD for fluctuating target detection is still unknown. In this study, with the aid of matrix decomposition theory, the analytical performance of the SDC-ASD for detecting fluctuating target is considered and assessed. At the design stage, the rigorous mathematical derivation processes for the exact theoretical detection performance of the SDC-ASD for fluctuating target detection is derived. The theoretical results, which have explicitly expressions for both the false alarm probability and the detection probability, not only provide an effective mathematical method to analyse the fluctuating target detection performance, but also can further simplify the deterministic target detection assessments. At the analysis stage, Monte Carlo simulations are resorted to validate the analytical results. Numerical experiments are conducted to assess the effectiveness of the SDC-ASD for fluctuating target detection. Results show that the SDC-ASD, by dealing separately with the clutter and the noise, performs the best in comparison with its published counterparts, i.e. the generalised likelihood ratio detectors, the adaptive matched filters, the adaptive subspace detectors and the low-rank detectors based on sample covariance matrix.