Persymmetric adaptive subspace detectors for range-spread targets

Abstract This paper investigates the problem of adaptive detection of a range-spread target in colored Gaussian disturbance. The range-spread target is described by a multi-rank subspace model, which lies in a subspace but with unknown coordinates. The disturbance, usually including clutter and thermal noise, has an unknown covariance matrix. Under the above assumption, we design the Rao and generalized likelihood ratio test (GLRT) detectors by the two-step procedure, which incorporates persymmetric structure of received data. The two detectors are shown to coincide with each other. Remarkably, the proposed detector ensures constant false alarm rate property. Experimental results conducted by both simulation and real data verify that the proposed detector outperforms the existing counterparts in training-limited scenarios.