DOA Estimation for Local Scattered CDMA Signals by Particle Swarm Optimization

Chang, Jhih-Chung

doi:10.3390/s120303228

Cited by 11 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the PSO algorithm has memory and distributed search features [ 17 – 19 ], it has high accuracy in the optimization of complicated systems, so the PSO algorithm is used in our system to solve the parametric problem of the PID controller. The PID control system consists of a master chaotic system, a slave chaotic system, a PID controller, and the PSO algorithm.…”

Section: Solve Optimization Problem Using Pso Algorithmmentioning

confidence: 99%

Bluetooth Based Chaos Synchronization Using Particle Swarm Optimization and Its Applications to Image Encryption

Yau¹,

Hung²,

Hsieh³

2012

Sensors

View full text Add to dashboard Cite

This study used the complex dynamic characteristics of chaotic systems and Bluetooth to explore the topic of wireless chaotic communication secrecy and develop a communication security system. The PID controller for chaos synchronization control was applied, and the optimum parameters of this PID controller were obtained using a Particle Swarm Optimization (PSO) algorithm. Bluetooth was used to realize wireless transmissions, and a chaotic wireless communication security system was developed in the design concept of a chaotic communication security system. The experimental results show that this scheme can be used successfully in image encryption.

show abstract

Section: Solve Optimization Problem Using Pso Algorithmmentioning

confidence: 99%

Bluetooth Based Chaos Synchronization Using Particle Swarm Optimization and Its Applications to Image Encryption

Yau¹,

Hung²,

Hsieh³

2012

Sensors

View full text Add to dashboard Cite

show abstract

“…In this modern era of technology, this issue is resolved using meta-heuristic algorithms. Nobody can deny the significance of heuristic techniques such as genetic algorithm (GA) [25], particle swarm optimization (PSO) [26], differential evolution (DE) [27], simulated annealing (SA) [28], ant colony optimization (ACO) [29], bee colony optimization (BCO) [30], flower pollination algorithm (FPA) [31], cuckoo search algorithm [32] and so on. These techniques are population based, and these methods are efficient and robust.…”

Section: Introductionmentioning

confidence: 99%

Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm

et al. 2022

View full text Add to dashboard Cite

For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).

show abstract

“…Direction of arrival (DOA) estimation using sensor arrays plays an important role in many applications of radar, sonar, ultrasonic, acoustic, and communication systems to track and localize sources [ 1 – 4 ]. Conventional DOA estimation methods obtain high freedoms by using arrays with large aperture.…”

Section: Introductionmentioning

confidence: 99%

Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays

Liang

Ying

et al. 2014

Sensors

View full text Add to dashboard Cite

In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed compressed sensing to obtain larger array aperture. We focus on the complex source distribution in the practical applications and classify the sources into common and innovation parts according to whether a signal of source can impinge on all the SLAs or a specific one. For each SLA, we construct a corresponding virtual uniform linear array (ULA) to create the relationship of random linear map between the signals respectively observed by these two arrays. The signal ensembles including the common/innovation sources for different SLAs are abstracted as a joint spatial sparsity model. And we use the minimization of concatenated atomic norm via semidefinite programming to solve the problem of joint DOA estimation. Joint calculation of the signals observed by all the SLAs exploits their redundancy caused by the common sources and decreases the requirement of array size. The numerical results illustrate the advantages of the proposed approach.

show abstract

DOA Estimation for Local Scattered CDMA Signals by Particle Swarm Optimization

Cited by 11 publications

References 15 publications

Bluetooth Based Chaos Synchronization Using Particle Swarm Optimization and Its Applications to Image Encryption

Bluetooth Based Chaos Synchronization Using Particle Swarm Optimization and Its Applications to Image Encryption

Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm

Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays

Contact Info

Product

Resources

About