A robust generalized sidelobe canceller via steering vector estimation

Xu, Wei; Xie, Jianliang; He, Zishu; Li, Huiyong

doi:10.1186/s13634-016-0358-7

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…The adaptive beamforming problem [6,13,14] is essentially to design the optimal weight vector w that minimizes the interference-plus-noise output power while maintaining unity response of the desired signal. min…”

Section: Steering Vector Mismatch Estimation For R-iaslc Algorithmmentioning

confidence: 99%

Robust Adaptive Sidelobe Canceller Using Sv Mismatch Estimation

Zuyi¹,

Shen²,

Liang³

et al. 2018

PIER Letters

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Abstract-In this paper, to overcome signal-to-interference-and-noise ratio (SINR) performance degradation in the presence of steering vector (SV) mismatch between beam pointing and desired signal's SVs, we study the mismatch of SV with adaptive uncertainty level. This estimation is derived based on the geometrical interpretation of the mismatch and can be expressed as a simple closed-form expression as a function of the presumed SV and the signal-subspace projection. Then, the adaptive uncertainty algorithm self-adjusts the uncertainty sphere according to the estimated mismatch SV at each iteration. Finally, the robust adaptive sidelobe canceller (R-IASLC) algorithm can accurately evaluate the mismatches between the actual and presumed SVs and improve the target SINR. Simulation results verify the effectiveness of this method.

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Section: Steering Vector Mismatch Estimation For R-iaslc Algorithmmentioning

confidence: 99%

Robust Adaptive Sidelobe Canceller Using Sv Mismatch Estimation

Zuyi¹,

Shen²,

Liang³

et al. 2018

PIER Letters

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“…In [12], the adaptive beamforming is transformed to a fast Fourier transform based weighted pattern synthesis problem with constraints on beamwidth and peak side lobe level. Robust adaptive beamforming methods based on steering vector estimation (SVE) can be found in [13][14][15][16][17][18][19][20][21]. The SVE-based methods can achieve high performance by adjusting the pattern look direction automatically to the SOI direction.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the adaptive monopulse algorithm should be robust to the look direction error. The methods in [11][12][13][14][15][16][17][18][19][20][21] are robust adaptive beamforming (RAB) methods. A shrinkage-based diagonal loading (DL) method is proposed in [11].…”

Section: Introductionmentioning

confidence: 99%

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Robust adaptive monopulse algorithm based on main lobe constraints and subspace tracking

Qiu

Sheng

et al. 2017

EURASIP J. Adv. Signal Process.

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In continuous wave (CW) radar and high pulse repetition frequency pulse-Doppler (HPRF-PD) radar, the interference plus noise sample snapshots are hard to be obtained. The desired signal in the received snapshots makes the LCMV-based adaptive monopulse algorithm sensitive to pattern look direction error. A linearly constrained subarray robust adaptive monopulse algorithm based on main lobe maintenance constraint and subspace tracking is developed in this paper. The constraint of main lobe maintenance is obtained by signal subspace projection. The bi-iterative least-square (Bi-LS) subspace tracking is used to update the signal subspace, and a power-associated method is developed to determine the dimension of the projection subspace automatically. The proposed robust adaptive monopulse algorithm can achieve high-angle estimation accuracy and good robustness to look direction error while expending only one additional degree of freedom compared to conventional LCMV-based method.Keywords: Robust adaptive monopulse algorithm, Main lobe maintenance, Subspace tracking, Dimension estimation BackgroundThe monopulse technique is utilized to perform high precision angle estimation for tracking radars. The target's angle is estimated using the ratio of difference to sum beam outputs, called monopulse ratio. Because of the linearity of monopulse ratio in 3dB beamwidth, the target's angle can be estimated bywhere θ 0 and θ s are the angle of the pattern look direction and the target direction, respectively, g (·) is the monopulse ratio, and K θ is the slope of the monopulse ratio.The adaptive array processing is first applied to monopulse technique by Davis [1]. The adaptive monopulse uses adaptive beamforming to form the sum and difference beams and suppress the spatial interferences. For the large-scale adaptive array, computation load and high data rate are two main bottlenecks for realization of the adaptive monopulse method. Subarrray *Correspondence: maxiaofeng@njust.edu.cn School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China adaptive monopulse method can effectively alleviate such pressures and ensure angle estimation performance when limited number of interferences exist. An example of the subarray geometry is illustrated in Fig. 1a, in which regular and irregular subarray geometries are comprised. Nickel extends the adaptive monopulse technique to subarray and arbitrary array geometries and provides modified formulas of monopulse ratio. Then he systematically analyzes the performance of the adaptive monopulse technique and extends it to space-time processing [2][3][4][5].

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Special issue: advanced techniques for radar signal processing

Orlando¹,

Chen²,

Aubry³

et al. 2017

EURASIP J. Adv. Signal Process.

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Recent advances in technology have led to the development of low-cost sensing devices capable of providing high performances in terms of both computational resources and measurement precision. More important, the reduced size of such devices have allowed to exploit them in diverse application fields (as, for instance, medical, military, manufacturing, transportation, and safety systems). However, in the context of military applications (e.g., radar and communication systems), the downside of this technology is that it is available for terrorist attacks aimed at denying targeting information and using radarguided missiles or small drones carrying dangerous (e.g., explosive or chemical) substances. Thus, it stems the need for innovative signal processing solutions to counteract these threats. Such techniques are applicable in ship and aircraft monitoring (for defense purposes), coastal surveillance, and, generally speaking, homeland security.This special issue focuses on radar signal processing techniques (target detection and tracking, interference estimation and suppression, adaptive beamforming, electronic warfare) that benefit from the mentioned advances to face the new challenging operating scenarios that naturally arise from nowadays technology advantages and disadvantages. More specifically, the emphasis is on (possibly distributed) radar systems equipped with arrays of sensors, which enable to capitalize the spatial diversity and power integration enabling significant improvements in performance.In general, radar systems perform three general functions, which are search, track, and imaging. The most important operation of a search radar is target detection. As a matter of fact, once the system declares the presence of a target, its resources are scheduled to estimate target parameters [1, 2] or for target tracking [3] which consists in the fine estimation of parameters as range, azimuth angle, elevation angle, and Doppler frequency offset.

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A robust generalized sidelobe canceller via steering vector estimation

Cited by 3 publications

References 27 publications

Robust Adaptive Sidelobe Canceller Using Sv Mismatch Estimation

Robust Adaptive Sidelobe Canceller Using Sv Mismatch Estimation

Robust adaptive monopulse algorithm based on main lobe constraints and subspace tracking

Special issue: advanced techniques for radar signal processing

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