A Robust Angle Estimation Method for Bistatic MIMO Radar About Non-stationary Random Noise

Gong, Jian; Lou, Shun-Tian; Guo, Yiduo

doi:10.1007/s11277-019-06172-w

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…However, there are no second and higher-order moments in the SαS distribution, and the conventional direction-finding methods based on the second and higher-order moments are not able to work on the impulse noise. In this case, fractional low-order moment (FLOM) was applied to suppress the impulse noise, and the FLOM-ESPRIT method was proposed for direction finding of bistatic MIMO radar in [12]. A FLOM-unitary ESPRIT method for direction finding of bistatic MIMO radar was proposed in the impulse noise in [13].…”

Section: Introductionmentioning

confidence: 99%

Direction finding of bistatic MIMO radar based on quantum-inspired grey wolf optimization in the impulse noise

Gao

Diao

2018

EURASIP J. Adv. Signal Process.

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A novel direction-finding method is proposed for bistatic multiple-input-multiple-output (MIMO) radar in the impulse noise in this paper. The method has the capacity to suppress the impulse noise by means of infinite norm normalization and can obtain better performance for direction finding via the weighted signal subspace fitting algorithm. To solve the objective function of this method, we devise a quantum-inspired grey wolf optimization algorithm to acquire the global optimal solution. The proposed method based on QGWO can resolve the direction-finding difficulties of bistatic MIMO radar. Monte-Carlo experiments have confirmed the robustness and superiority of the proposed method for locating independent and coherent sources with a small number of snapshots in the impulse noise compared with some existing direction-finding methods in a series of scenarios. In addition, we present the Cramér-Rao bound (CRB) of angle estimation for bistatic MIMO radar in the impulse noise, which generalizes the Gaussian CRB for performance analysis.

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Section: Introductionmentioning

confidence: 99%