Abstract
To deal with the problem of signal detection in correlated subspace, this paper presents an augmented generalized likelihood ratio test (AGLRT) detector. Compared with traditional GLRT detector, this AGLRT detector has the advantage of incorporating the intersection component between signal and clutter subspace. Signal-contribution-ratio (SCR) and clutter-contribution-ratio (CCR) are defined to characterize the contribution of signal and clutter in the common subspace, respectively. The closed forms of both probability of detection and false alarm for the AGLRT detector are presented. It is shown that the detection performance of the AGLRT detector is better than the GLRT detector by choosing appropriate weight and threshold parameters. Then, analytical formulas of the suboptimal weight and threshold to maximize the detection performance are obtained. Finally, the effectiveness of the proposed detector is demonstrated by various examples.
Highlights
Detection is a classical problem with wide application in radar [1], [2] and communication systems [3]
The main contributions are as follows: 1) We present an augmented generalized likelihood ratio test (AGLRT) detector, which is formulated by additively capitalizing the intersection component between signal subspace and clutter subspace
OF AGLRT DETECTOR To verify the effectiveness of AGLRT detector in (7), Fig. 2(a) shows the relationship between probability density function and threshold corresponding to each hypothesis with given parameters SNR = −12dB, CNR = −15dB, ks = 1, kc = 0.5 and w = 1, which clearly illustrates the influence of threshold on probability of false alarm Pfa(w, T ) and detection Pd (w, T )
Summary
Detection is a classical problem with wide application in radar [1], [2] and communication systems [3]. A common assumption of signal detection in clutter and noise is that signal subspace and clutter subspace are linearly independent. With the opening of low altitude, the explosive growth of consumer grade Drones poses a serious threat on aviation safety, social security and public safety. Drones’ salient features are low altitude, small RCS, slow speed [4]–[7]. There is a relatively high probability that slow targets (like Drones) may fall into the subspace of nonstationary clutter. The dependency of signals and clutters brings new challenges to traditional detection methods
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