Robust adaptive beamforming using an iterative FFT algorithm

Yang, Kai; Zhao, Zhiqin; Liu, Jiazhou; Liu, Qing

doi:10.1016/j.sigpro.2013.09.003

Cited by 15 publications

(5 citation statements)

References 25 publications

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“…In adaptive beamforming, the ultimate goal is to maximise the output SINR given in Equation (18), which is composed of two sub-objectives. The first goal is to minimise the output interference-plus-noise power, and the second one is to maximise the output energy of the desired signal.…”

Section: Response Constraint Of Desired Signalmentioning

confidence: 99%

“…In addition to the time-domain method, the frequencydomain approach generally decomposes the time-domain signals into several sets of narrowband data and then applies narrowband beamforming to each subband; the final wideband beamforming output can be obtained by synthesising the output data of all narrowband beamformers accordingly. Although general filter banks can be employed for subband decomposition of the wideband signals [17], Fourier transform implemented by fast Fourier transform (FFT) with higher computational efficiency is normally adopted [18,19]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

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Robust wideband adaptive beamforming based on covariance matrix reconstruction in the spatial‐frequency domain

Lü

Yang

Yao

et al. 2021

IET Microwaves Antenna & Prop

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Without the response variation constraint, the main‐lobe responses of wideband beamforming usually vary with the frequencies of interest, and the beam pointing may deviate from the incident angle of the desired signal. If the interference‐plus‐noise covariance matrix (INCM) is directly replaced by the sample covariance matrix, the desired signal in the sample data may result in self‐null problem, which can lead to the degradation of the performance of wideband beamformers. Through frequency division, a novel wideband adaptive beamforming is proposed by reconstructing the INCM and solving the convex optimisation problem. Using fast Fourier transform (FFT), the time‐domain samples are transformed into the frequency field, and the covariance matrix of each subband can be obtained from the frequency‐domain data. In the spatial‐frequency domain, the INCM and signal‐plus‐noise covariance matrix can be respectively reconstructed by integrating the stacked steering vector and the Capon spatial spectrum over the corresponding angular region. Then, we establish the optimisation criteria, that is, minimising the output interference‐plus‐noise power, minimising the main‐lobe spatial response variation, and constraining the response deviation of the desired signal. A novel convex optimisation model can be established, and then the weight coefficients for time‐domain wideband beamforming can be obtained by solving the optimisation problem. The simulation and experimental results demonstrate that the proposed beamformer can provide better performance compared with other tested beamformers and maintain good robustness in various applications.

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Section: Response Constraint Of Desired Signalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust wideband adaptive beamforming based on covariance matrix reconstruction in the spatial‐frequency domain

Lü

Yang

Yao

et al. 2021

IET Microwaves Antenna & Prop

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“…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%

“…The loading level to the covariance matrix can be determined automatically. 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].…”

Section: Introductionmentioning

confidence: 99%

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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“…Many methods can be adopted for array pattern synthesis of minimum Peak Sidelobe Level (PSL) [1]- [4]. These methods are proposed for nonlinear arrays, alternatively termed as aperiodic arrays, to search a sensors position distribution solution and they can use less number of sensors to meet similar pattern specifications [5]- [8].…”

Section: Introductionmentioning

confidence: 99%