A Novel Orthonormalization Matrix Based Fast and Stable DPM Algorithm for Principal and Minor Subspace Tracking

Wang, Rong; Mao, Yao; Zhang, Daoming; Zou, Hongxing

doi:10.1109/tsp.2011.2169406

Cited by 22 publications

(23 citation statements)

References 20 publications

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“…In the literature, one can find different ways to perform the orthonormalization step [18,[37][38][39]. The most stable of them are presented in [38,18].…”

Section: Thameri Et Al / Digital Signal Processing ••• (••••) •••mentioning

confidence: 99%

“…The most stable of them are presented in [38,18]. The technique used in the FDPM algorithm [38] costs 6np flops per iteration while the proposed algorithm FSDPM (Fast and Stable Data Projection Method) in [18] costs 3np flops per iteration.…”

Section: Thameri Et Al / Digital Signal Processing ••• (••••) •••mentioning

confidence: 99%

“…The technique used in the FDPM algorithm [38] costs 6np flops per iteration while the proposed algorithm FSDPM (Fast and Stable Data Projection Method) in [18] costs 3np flops per iteration. Hence, we propose to use the same orthonormalization technique as the one presented in [18] which is the less expensive one that preserves the algorithm's stability.…”

Section: Thameri Et Al / Digital Signal Processing ••• (••••) •••mentioning

confidence: 99%

“…-Most low complexity algorithms [12,11,[18][19][20][21] are based on gradient techniques. Unfortunately, many of these algorithms suffer from low convergence rate or numerical instability.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Low complexity adaptive algorithms for Principal and Minor Component Analysis

Thameri

Abed-Meraim²,

Belouchrani³

2013

Digital Signal Processing

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“…In the literature, one can find different ways to perform the orthonormalization step [18,[37][38][39]. The most stable of them are presented in [38,18].…”

Section: Thameri Et Al / Digital Signal Processing ••• (••••) •••mentioning

confidence: 99%

Section: Thameri Et Al / Digital Signal Processing ••• (••••) •••mentioning

confidence: 99%

Section: Thameri Et Al / Digital Signal Processing ••• (••••) •••mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Low complexity adaptive algorithms for Principal and Minor Component Analysis

Thameri

Abed-Meraim²,

Belouchrani³

2013

Digital Signal Processing

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“…For the proposed method, the optimal projection subspace is the signal subspace. The subspace tracking methods in [20] and [41][42][43][44][45] are not able to update the dimension of the signal subspace when subspace tracking. The dimension is fixed when subspace tracking.…”

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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Automatic detection of three cell types in a microscope image based on deep learning

Zhang

Zhou

et al. 2022

Journal of Biophotonics

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With the continuous integration of deep learning and the technique of molecular biology, target detection models must accurately detect the position of each cell in the image and classify it correctly. We present a model for the multi‐scale feature fusion of the existing human cell image dataset based on Gaussian mixedly clustering. First, a novel feature extraction network for extracting preliminary features at picture multi scales was presented, which was based on a residual neural network with Instance Normalization and a Mish activation function. Second, the presented model adopts the idea of feature fusion and introduced a new type of feature fusion network to integrate feature graphs on different scales. Furthermore, a Gaussian hybrid clustering algorithm was proposed to cluster the hyperparameters. Based on the experimental results, the average accuracy of the proposed model in the human cell image dataset exceeds 0.96, which improves by 11.9% compared with the existing target detection methods in the same field. Experiments show that the proposed model had been adapted to datasets with uneven sample distribution, providing new ideas for research on medical images.

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A Novel Orthonormalization Matrix Based Fast and Stable DPM Algorithm for Principal and Minor Subspace Tracking

Cited by 22 publications

References 20 publications

Low complexity adaptive algorithms for Principal and Minor Component Analysis

Low complexity adaptive algorithms for Principal and Minor Component Analysis

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

Automatic detection of three cell types in a microscope image based on deep learning

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