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An Adaptive Detector with Mismatched Signals Rejection in Compound Gaussian Clutter
DOI: 10.12000/JR19030
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Abstract
Because of the improvement in radar resolution and decrease in grazing angle, the amplitude distribution of sea clutter obviously deviates from the Rayleigh distribution and presents a significant non-Gaussian feature. In this case, the compound Gaussian model is widely used. This study investigates the problem of detecting a target when signal mismatches occur in compound Gaussian clutter and proposes a selective detector to reject mismatched signals embedded in compound Gaussian clutter based on the so-called two-step Generalized Likelihood Ratio Test (GLRT). To design the selective detector, we modified the original hypothesis test by injecting a fictitious interference under the null hypothesis. These unwanted signals are assumed to be orthogonal to the nominal steering vector in the whitened subspace. The proposed detector has a Constant False Alarm Rate (CFAR) with respect to the statistics of the texture and covariance matrix. Finally, to demonstrate the effectiveness of the proposed detector, a Monte Carlo simulation is conducted to assess its performance based on the simulated and measured sea clutter data. The experimental results show that the proposed detector effectively improves the selectivity of the mismatched signals together with the detection of matched signals in a range spread target of 1～3 dB. -
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References
[1] KELLY E J. An adaptive detection algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, AES-22(2): 115–127. doi: 10.1109/TAES.1986.310745[2] ROBEY F C, FUHRMANN D R, KELLY E J, et al. A CFAR adaptive matched filter detector[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 208–216. doi: 10.1109/7.135446[3] DE MAIO A. Rao test for adaptive detection in Gaussian interference with unknown covariance matrix[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3577–3584. doi: 10.1109/TSP.2007.894238[4] CONTE E, DE MAIO A, and RICCI G. GLRT-based adaptive detection algorithms for range-spread targets[J]. IEEE Transactions on Signal Processing, 2001, 49(7): 1336–1348. doi: 10.1109/78.928688[5] GINI F and GRECO M. Texture modelling, estimation and validation using measured sea clutter data[J]. IEE Proceedings - Radar, Sonar and Navigation, 2002, 149(3): 115–124. doi: 10.1049/ip-rsn:20020272[6] SANGSTON K J, GINI F, GRECO M V, et al. Structures for radar detection in compound Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(2): 445–458. doi: 10.1109/7.766928[7] XU Shuwen, XUE Jian, and SHUI Penglang. Adaptive detection of range-spread targets in compound Gaussian clutter with the square root of inverse Gaussian texture[J]. Digital Signal Processing, 2016, 56: 132–139. doi: 10.1016/j.dsp.2016.06.009[8] SHANG Xiuqin, SONG Hongjun, WANG Yu, et al. Adaptive detection of distributed targets in compound-Gaussian clutter with inverse gamma texture[J]. Digital Signal Processing, 2012, 22(6): 1024–1030. doi: 10.1016/j.dsp.2012.05.002[9] SANGSTON K J, GINI F, and GRECO M S. Coherent radar target detection in heavy-tailed compound-Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 64–77. doi: 10.1109/TAES.2012.6129621[10] DONG Y. Optimal coherent radar detection in a K-distributed clutter environment[J]. IET Radar, Sonar & Navigation, 2012, 6(5): 283–292. doi: 10.1049/iet-rsn.2011.0273[11] SHUI Penglang, LIU Ming, and XU Shuwen. Shape-parameter-dependent coherent radar target detection in K-distributed clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(1): 451–465. doi: 10.1109/TAES.2015.140109[12] PULSONE N B and RADER C M. Adaptive beamformer orthogonal rejection test[J]. IEEE Transactions on Signal Processing, 2001, 49(3): 521–529. doi: 10.1109/78.905870[13] BANDIERA F, BESSON O, and RICCI G. An ABORT-like detector with improved mismatched signals rejection capabilities[J]. IEEE Transactions on Signal Processing, 2008, 56(1): 14–25. doi: 10.1109/TSP.2007.906690[14] LIU Weijian, LIU Jun, DU Qinglei, et al. Distributed target detection in partially homogeneous environment when signal mismatch occurs[J]. IEEE Transactions on Signal Processing, 2018, 66(14): 3918–3928. doi: 10.1109/TSP.2018.2841860[15] RANGASWAMY M, WEINER D D, and OZTURK A. Non-Gaussian random vector identification using spherically invariant random processes[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1): 111–124. doi: 10.1109/7.249117[16] YAO K. A representation theorem and its applications to spherically-invariant random processes[J]. IEEE Transactions on Information Theory, 1973, 19(5): 600–608. doi: 10.1109/TIT.1973.1055076[17] RICHMOND C D. Performance of a class of adaptive detection algorithms in nonhomogeneous environments[J]. IEEE Transactions on Signal Processing, 2000, 48(5): 1248–1262. doi: 10.1109/78.839973[18] RICHMOND C D. Statistics of adaptive nulling and use of the generalized eigenrelation (GER) for modeling inhomogeneities in adaptive processing[J]. IEEE Transactions on Signal Processing, 2000, 48(5): 1263–1273. doi: 10.1109/78.839974[19] CONTE E and DE MAIO A. Mitigation techniques for non-Gaussian sea clutter[J]. IEEE Journal of Oceanic Engineering, 2004, 29(2): 284–302. doi: 10.1109/JOE.2004.826901[20] CONTE E, LOPS M, and RICCI G. Adaptive matched filter detection in spherically invariant noise[J]. IEEE Signal Processing Letters, 1996, 3(8): 248–250. doi: 10.1109/97.511809 -
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Figure 1. Detection threshold of the proposed detector under different
$\nu ,\;b\;{\rm and}\;\rho $ -
Figure 2. Detection performance curve of different detectors for matched signal (N=8, H=1, K=32,
${\cos ^2}\theta = 1$ $\nu $ -
Figure 3. Detection performance curve of different detectors for matched signal (N=8, H=4, K=32,
${\cos ^2}\theta = 1$ $\nu $ -
Figure 4. Detection probability of different detectors versus
${\cos ^2}\theta $ $\nu $ -
Figure 5. Detection probability of different detectors versus
${\cos ^2}\theta $ $\nu $ - Figure 6. Amplitude probability density function fitting of the measured data
- Figure 7. Detection performance curve of different detectors for matched signal under measured data (N=8, H=4, K=24)
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Figure 8. Detection probability of different detectors versus
${\cos ^2}\theta $