An improved beamforming method with low computational load

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time.

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Adaptive iterative image restoration with reduced computational load

Optical Engineering ◽

10.1117/12.55748 ◽

1990 ◽

Vol 29 (12) ◽

pp. 1458 ◽

Cited By ~ 16

Author(s):

Aggelos K. Katsaggelos

Keyword(s):

Image Restoration ◽

Computational Load

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A computational load-pull system for evaluating RF and microwave power amplifier technologies

2000 IEEE MTT-S International Microwave Symposium Digest (Cat No 00CH37017) MWSYM-00 ◽

10.1109/mwsym.2000.861072 ◽

2002 ◽

Cited By ~ 5

Author(s):

G.H. Loechelt ◽

P.A. Blakey

Keyword(s):

Power Amplifier ◽

Microwave Power ◽

Pull System ◽

Computational Load ◽

Microwave Power Amplifier ◽

Load Pull

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Multiscale Off-Road Mobility Simulation With Computational Load Balancing for Lower-Scale Discrete-Element Models

10.1115/1.0001873v ◽

2021 ◽

Author(s):

Guanchu Chen ◽

Hiroki Yamashita ◽

Yeefeng Ruan ◽

Paramsothy Jayakumar ◽

Hiroyuki Sugiyama

Keyword(s):

Load Balancing ◽

Discrete Element ◽

Computational Load

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Fused CLEAN deconvolution for compact and diffuse emission

Astronomy and Astrophysics ◽

10.1051/0004-6361/201833090 ◽

2018 ◽

Vol 618 ◽

pp. A117 ◽

Cited By ~ 2

Author(s):

L. Zhang

Keyword(s):

Poor Performance ◽

Point Of View ◽

Diffuse Emission ◽

Multi Scale ◽

Convolution Operation ◽

Computational Load ◽

Fitting Procedure ◽

The Poor ◽

Computationally Expensive ◽

Clean Algorithm

Context. CLEAN algorithms are excellent deconvolution solvers that remove the sidelobes of the dirty beam to clean the dirty image. From the point of view of the scale, there are two types: scale-insensitive CLEAN algorithms, and scale-sensitive CLEAN algorithms. Scale-insensitive CLEAN algorithms perform excellently well for compact emission and perform poorly for diffuse emission, while scale-sensitive CLEAN algorithms are good for both point-like emission and diffuse emission but are often computationally expensive. However, observed images often contain both compact and diffuse emission. An algorithm that can simultaneously process compact and diffuse emission well is therefore required. Aims. We propose a new deconvolution algorithm by combining a scale-insensitive CLEAN algorithm and a scale-sensitive CLEAN algorithm. The new algorithm combines the advantages of scale-insensitive algorithms for compact emission and scale-sensitive algorithms for diffuse emission. At the same time, it avoids the poor performance of scale-insensitive algorithms for diffuse emission and the great computational load of scale-sensitive algorithms for compact emission in residuals. Methods. We propose a fuse mechanism to combine two algorithms: the Asp-Clean2016 algorithm, which solves the computationally expensive problem of convolution operation in the fitting procedure, and the classical Högbom CLEAN (Hg-Clean) algorithm, which is faster and works equally well for compact emission. It is called fused CLEAN (fused-Clean) in this paper. Results. We apply the fused-Clean algorithm to simulated EVLA data and compare it to widely used algorithms: the Hg-Clean algorithm, the multi-scale CLEAN (Ms-Clean), and the Asp-Clean2016 algorithm. The results show that it performs better and is computationally effective.

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Depth Data Reconstruction Based on Gaussian Mixture Model

Cybernetics and Information Technologies ◽

10.1515/cait-2016-0089 ◽

2016 ◽

Vol 16 (6) ◽

pp. 207-219 ◽

Cited By ~ 1

Author(s):

Zhe Li ◽

Chen Ma ◽

Tian-Fan Zhang

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Point Cloud ◽

Intelligent Agent ◽

Calibration Method ◽

Gaussian Mixture ◽

Consensus Algorithm ◽

Actual Object ◽

Depth Data ◽

Computational Load

Abstract Depth data is an effective tool to locate the intelligent agent in space because it accurately records the 3D geometry information on the surface of the scanned object, and is not affected by factors like shadow and light. However, if there are many planes in the work scene, it is difficult to identify objects and process the resulting huge amount of data. In view of this problem and targeted at object calibration, this paper puts forward a depth data calibration method based on Gauss mixture model. The method converts the depth data to point cloud, filters the noise and collects samples, which effectively reduces the computational load in the following steps. Besides, the authors cluster the point cloud vector with the Gaussian mixture model, and obtain the target and background planes by using the random sampling consensus algorithm to fit the planes. The combination of target Region Of Intelligent agent (ROI) and point cloud significantly reduces the computational load and improves the computing speed. The effect and accuracy of the algorithm is verified by the test of the actual object.

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Thermal Analysis of Stacked Ni-MH Battery

10.1115/imece1999-1001 ◽

1999 ◽

Author(s):

Katsumi Hisano ◽

Hideo Iwasaki ◽

Masaru Ishizuka ◽

Tetsuya Yamane

Keyword(s):

Thermal Analysis ◽

Numerical Analysis ◽

Temperature Rise ◽

Test Case ◽

Charge Retention ◽

Calculated Temperature ◽

Computational Load ◽

Three Stages ◽

Mh Battery ◽

Good Agreement

Abstract Numerical analysis was carried out to evaluate the temperature rise and charge retention of Ni-MH batteries as pallet loads. In this paper, thermal analysis of pallet loads which contain 2400 mAh Ni-MH batteries is considered as a test case. To reduce computational load, thermal analysis was performed in three stages. Measured and calculated temperature rise of the load showed good agreement, and it can be observed that there exists an appropriate charge retention of the battery to sustain high retention during transportation.

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An integrated fault-tolerant control strategy for control surface failure in a fighter aircraft

The Aeronautical Journal ◽

10.1017/aer.2021.66 ◽

2021 ◽

pp. 1-30

Author(s):

İ. Gümüşboğa ◽

A. İftar

Keyword(s):

Control Strategy ◽

Fault Tolerant ◽

Detection System ◽

Fault Tolerant Control ◽

Failure Detection ◽

Computational Effort ◽

Control Surface ◽

Fighter Aircraft ◽

Control Structures ◽

Computational Load

Abstract Elevator failure may have fatal consequences for fighter aircraft that are unstable due to their high manoeuvrability requirements. Many studies have been conducted in the literature using active and passive fault-tolerant control structures. However, these studies mostly include sophisticated controllers with high computational load that cannot work in real systems. Considering the multi-functionality and broad operational prospects of fighter aircraft, computational load is very important in terms of applicability. In this study, an integrated fault-tolerant control strategy with low computational load is proposed without sacrificing the ability to cope with failures. This control strategy switches between predetermined controllers in the case of failure. One of these controllers is designed to operate in a non-failure condition. This controller is a basic controller that requires very little computational effort. The other controller operates when an asymmetric elevator failure occurs. This controller is a robust fault-tolerant controller that can fly the aircraft safely in case of elevator failure. The switching is decided by a failure detection system. The proposed integrated fault-tolerant control system is verified by non-linear F-16 flight simulations. These simulations show that the proposed method can cope with failures but requires less computational load because it uses a conventional controller in the case of no failure.

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