Robust Single-Step Locations of Strictly Noncircular Sources in the Impulsive Noise Environment
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Direct position determination (DPD) is a promising technique, which localizes transmitters directly from original sensor outputs without estimating intermediate parameters in a single step. Therefore, DPD improves the location accuracy and avoids the data association problem, compared with the conventional two-step methods. However, most of the existing DPDs are investigated for complex circular sources, neglecting the property of complex noncircular signals, and rely on the assumption that the noise is Gaussian distributed with finite second-order moments. This paper presents a robust single-step location algorithm for strictly noncircular sources intercepted by a moving array in the impulsive noise environment. First, an extended lower order infinity-norm covariance matrix is proposed by exploiting the noncircularity of signals. We prove that it is bounded and has the extended subspace structure without strict restrictions on the noise distribution, thus the extended noise subspaces are obtained at all positions of the moving array. Then in the light of the subspace data fusion idea, we extend it to the noncircular version and directly localize noncircular sources. Simulation results demonstrate that the proposed algorithm is effective in the Gaussian noise, and significantly outperforms other location algorithms in the impulsive noise.
KeywordsDirect position determination (DPD) Impulsive noise Noncircular signal Extended lower order infinity-norm covariance (ELOIC) matrix Subspace data fusion (SDF)
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61201381, 61401513 and 61772548), China Postdoctoral Science Foundation (Grant No. 2016M592989), the Outstanding Youth Foundation of Information Engineering University (Grant No. 2016603201), and the Self-Topic Foundation of Information Engineering University (Grant No. 2016600701).
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