scholarly journals A Novel MIMO Radar Orthogonal Waveform Design Algorithm Based on Intelligent Ions Motion

2021 ◽  
Vol 13 (10) ◽  
pp. 1968
Author(s):  
Lei Zhang ◽  
Fangqing Wen
Keyword(s):  
Cross Correlation ◽  
State Of The Art ◽  
Waveform Design ◽  
Mimo Radar ◽  
Local Optima ◽  
Design Algorithm ◽  
Guiding Principle ◽  
Evaluation Functions ◽  
Motion Optimization ◽  
Np Hard Problem

Orthogonal waveform design is one of the key technologies that affects the detection performance of MIMO radars. Most of the existing methods indirectly tackle this problem as an intractable nonconvex optimization and an NP-hard problem. In this work, we propose a novel waveform design algorithm based on intelligent ions motion optimization (IMO) to directly obtain a set of polyphase codes with good orthogonality. The autocorrelation sidelobe and cross-correlation sidelobe are first derived and subsequently integrated into evaluation functions for evaluating the orthogonality of polyphase codes. In order to effectively cope with the aforementioned problem, we present a strengthened IMO that is highly robust and converges rapidly. In the liquid state, an optimal guiding principle of same-charge ions is suggested to enhance global search ability and avoid falling into local optima. An ion updating strategy based on fitness ranking is presented to improve the search efficiency in the crystal state. Finally, the improved algorithm is employed to optimize the polyphase codes. The experimental results, compared with other state-of-the-art algorithms, show that the polyphase codes obtained by the proposed algorithm have better orthogonality.

A Scalable Redefined Stochastic Blockmodel

2021 ◽  
Vol 15 (3) ◽  
pp. 1-28
Author(s):  
Xueyan Liu ◽  
Bo Yang ◽  
Hechang Chen ◽  
Katarzyna Musial ◽  
Hongxu Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Science ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Real World Data ◽  
Computational Overhead ◽  
Stochastic Blockmodel ◽  
Np Hard Problem ◽  
Large Scale Networks ◽  
The Cost

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

Waveform design for TDM-MIMO radar systems

2020 ◽  
Vol 167 ◽  
pp. 107307
Author(s):  
M. Bagher Alaie ◽  
Seyed Ahmad Olamaei
Keyword(s):  
Waveform Design ◽  
Mimo Radar ◽  
Radar Systems

MIMO Radar Waveform Design With PAPR and Similarity Constraints

2018 ◽  
Vol 66 (4) ◽  
pp. 968-981 ◽  
Author(s):  
Ziyang Cheng ◽  
Zishu He ◽  
Bin Liao ◽  
Min Fang
Keyword(s):  
Waveform Design ◽  
Mimo Radar

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

2021 ◽  
Vol 12 (4) ◽  
pp. 98-116
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Local Optima ◽  
Portfolio Optimization Problem

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

Adaptive Waveform Design for Separated Transmit/Receive ULA-MIMO Radar

2010 ◽  
Vol 58 (9) ◽  
pp. 4936-4942 ◽  
Author(s):  
Jindong Zhang ◽  
Haiqing Wang ◽  
Xiaohua Zhu
Keyword(s):  
Waveform Design ◽  
Mimo Radar

An Algorithm for Learning the Skeleton of Large Bayesian Network

2015 ◽  
Vol 24 (04) ◽  
pp. 1550012
Author(s):  
Yanying Li ◽  
Youlong Yang ◽  
Wensheng Wang ◽  
Wenming Yang
Keyword(s):  
Bayesian Network ◽  
Structure Learning ◽  
State Of The Art ◽  
Large Network ◽  
Dependency Analysis ◽  
Learning From Data ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning ◽  
Np Hard Problem ◽  
Key Steps

It is well known that Bayesian network structure learning from data is an NP-hard problem. Learning a correct skeleton of a DAG is the foundation of dependency analysis algorithms for this problem. Considering the unreliability of the high order condition independence (CI) tests and the aim to improve the efficiency of a dependency analysis algorithm, the key steps are to use less number of CI tests and reduce the sizes of condition sets as many as possible. Based on these analyses and inspired by the algorithm HPC, we present an algorithm, named efficient hybrid parents and child (EHPC), for learning the adjacent neighbors of every variable. We proof the validity of the algorithm. Compared with state-of-the-art algorithms, the experimental results show that EHPC can handle large network and has better accuracy with fewer number of condition independence tests and smaller size of conditioning set.

Minimax Robust MIMO Radar Waveform Design

2007 ◽  
Vol 1 (1) ◽  
pp. 147-155 ◽  
Author(s):  
Yang Yang ◽  
Rick S. Blum
Keyword(s):  
Waveform Design ◽  
Mimo Radar
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