Entity

Time filter

Source Type

Wuhan, China

Chen J.,Wuhan Radar Institute
Proceedings of 2011 IEEE CIE International Conference on Radar, RADAR 2011 | Year: 2011

In high frequency (HF) skywave over-the-horizon radar (OTHR), radar signals are subjected to phase contamination as they propagate through the ionosphere, leading to severe degradation capability of OTHR radar, so that it needs to be corrected. According to the high spatial correlativity of the ionosphere contamination and the adjacent range cells are subjected to the same contamination, a new approach is applied to the ionosphere decontamination of OTHR echoes based on maximum likelihood estimation auto-focus method in ISAR and clutter suppression. The decontamination procedure is divided into two steps: firstly eigenvector projection is used to preprocess signals, and secondly using maximum likelihood estimation method to estimate contamination. The simulation and real data processing results show that the method is of effectiveness and robust performance on different kinds of ionospheric phase contaminations. © 2011 IEEE.


Li X.-T.,Shantou University | Wang S.-Y.,Wuhan Radar Institute
Tien Tzu Hsueh Pao/Acta Electronica Sinica | Year: 2011

Aimed at the problem that α-stable distribution has no closed form expression for the probability density function (PDF), an approximate expression is suggested. Such model is a mixture of Gaussian and Cauchy with bi-parameter. The mixture ratio is determined by fractional low order moment (FLOM). Proposed model has a complete closed form and provides analytical convenience. Based on such model, this paper further proposes a Rao statistical test method for the detection of sine signal under the α-stable noise environment. We illustrate the detection performances of the proposed Rao test for various α, and compare them with the Rao test that based on Gaussian assumption. Simulation results show that the proposed Rao detector distinctively outperforms the Rao detector that based on Gaussian assumption.


Zhu X.-B.,Wuhan Radar Institute | Wang S.-Y.,Wuhan Radar Institute | Li X.-T.,Shantou University | Fang Q.-X.,Wuhan Radar Institute
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | Year: 2010

To address the problem of multiple input multiple output (MIMO) radar signal separation with traditional matched filter (MF) in non-Gaussian clutter, a new MF based on fractional lower order statistics (FLOS) is proposed. The proposed method assumes that the MIMO radar clutter satisfies symmetric alpha stable (SαS) distribution, and then with the rule of maximizing the output fractional lower order signal-to-clutter power ratio, the optimum filter coefficients are obtained. The MIMO radar signal separation in coherent SαS and non-coherent SαS clutter are simulated respectively. Simulation results show that the proposed method is very efficient for MIMO radar signal separation in non-Gaussian clutter, and it has the same good performance as traditional MF in Gaussian clutter.


Zhu X.,Wuhan Radar Institute | Wang S.,Wuhan Radar Institute
Chinese Journal of Electronics | Year: 2011

This paper mainly deals with the problem of Multiple-input multiple-output (MIMO) radar in non-Gaussian clutter modeled as a complex isotropic Symmetric α-stable (SαS) process. The widely separated MIMO radar or Statistical MIMO (S-MIMO) radar is adopted. First, the signal model is developed to the complex isotropic SαS clutter, and then, the S-MIMO Rao detector is derived. To demonstrate the efficiency of the new detector, its performance is compared to that of the well-known Optimum Gaussian (OG) detector of S-MIMO Radar. Monte Carlo simulations are given for the cases of different clutter parameters and transmit/receive antennas. Via several numerical examples, it is shown that the S-MIMO Rao detector can provide excellent detection performance in heavy-tailed clutter compared with the S-MIMO OG detector.


Wan Y.,Wuhan Radar Institute | Wang S.,Wuhan Radar Institute
Chinese Journal of Electronics | Year: 2011

This paper addresses the problem of the signal detection and tracking of targets with low signal-to-noise ratio, a new track-before-detect method based on extended H∞ particle filter is proposed. The proposed method overcomes the degeneracy problem of particle filter, because the system's latest observations are considered by the importance density function of the extended H∞ particle filter. Therefore, the acquired particles are more convergent to the true samples. The simulation results exhibit the robustness of the proposed method which shows much better performance than track-before-detect based on traditional particle filter.

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