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Ma S.,National University of Defense Technology | Wu H.,Guilin Military Representative Bureau | Liu Z.,National University of Defense Technology | Jiang W.,National University of Defense Technology
Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | Year: 2012

In multiple platform electronic reconnaissance system, time difference of arrival (TDOA) can be used for pulse sorting. A method for TDOA sorting based on recursive extended histogram is proposed to deal with both the problem of misleading TDOA clusters caused by high pulse repetitive frequency (PRF) emitters and the problem of less pulse accumulation caused by ultra-low PRF emitters. TDOA data were formed into an extended histogram structure, which is processed recursively to detect and sort out the pulses of each emitter. By defining the extension operator, the misleading TDOA clusters could be removed, and the histogram noise level decreased step by step as well, so the method improved the performance of TDOA sorting effectively. Simulation results show that the method is applicable and effective.

Sha Z.-C.,National University of Defense Technology | Wu H.-B.,Guilin Military Representative Bureau | Ren X.-T.,National University of Defense Technology | Huang Z.-T.,National University of Defense Technology | Zhou Y.-Y.,National University of Defense Technology
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | Year: 2013

Correlation estimator is widely exploited to realize spread spectrum signal detection, because of many advantages, such as less computation cost and making full use of the statistic character of signal and noise. A unified signal model, which is suitable to direct sequence spread spectrum (DS/SS) signal, direct sequence code division multiple access (DS/CDMA) signal, multi-rate DS/CDMA signal and multi-carrier DS/CDMA signal, is established. Then, according to the principle of second-order moment of autocorrelation, the theoretical value which is contributed to the unified model signal is deduced and the establishment of condition for classic literature is pointed out. Finally, simulation results demonstrate the validity of the algorithm.

Liu Z.,State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System | Liu Z.,National University of Defense Technology | Zhou Y.,National University of Defense Technology | Wu H.,Guilin Military Representative Bureau
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | Year: 2014

The performance of the subspace-based direction of arrival (DOA) estimation methods can be improved significantly via effective exploitation of the non-circularity of the incident signals, but the shortcomings of these methods in adaptation to demanding scenarios, such as low signal-to-noise ratio (SNR) and limited snapshots, can hardly be made up. The sparse Bayesian learning (SBL) technique is introduced in this paper to deal with the DOA estimation problem of non-circular signals. The spatial sparsity of the incident signals is exploited together with their non-circularity property, and the covariance and conjugate covariance matrices of the array outputs of non-circular signals are decomposed jointly under a sparsity constraint to reconstruct the spatial spectrum of the incident signals, and the DOA estimates are finally obtained according to the spectrum peak locations. This method is robust against inter-signal correlation, and its superiorities in adaptation to demanding scenarios as well as in DOA estimation precision are demonstrated by the simulation results.

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