Maximum Likelihood Estimation of the Double-Directional Parameters in the Multiple-Input-Multiple-Output Communication System Using the Particle Swarm Optimization

2008 ◽  
Vol 28 (6) ◽  
pp. 401-410 ◽  
Author(s):  
Mohamed A. Taha ◽  
Dia I. Abu-Al-Nadi ◽  
Taisir H. Ismail
Keyword(s):  
Particle Swarm Optimization ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Communication System ◽  
Multiple Input Multiple Output ◽  
Particle Swarm ◽  
Likelihood Estimation ◽  
Swarm Optimization ◽  
Multiple Input ◽  
Input Multiple Output

scholarly journals Novel Methods for Approximate Maximum Likelihood Estimation of Multiple Superimposed Undamped Tones and Their Application to Radar Systems

2021 ◽  
Author(s):  
Pasquale Di Viesti ◽  
ALESSANDRO DAVOLI ◽  
Giorgio Guerzoni ◽  
Giorgio Matteo Vitetta
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Multiple Input Multiple Output ◽  
Likelihood Estimation ◽  
Approximate Maximum Likelihood ◽  
Radar Systems ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Novel Methods

<div>In this manuscript, novel methods for the detection of multiple superimposed tones in noise and the estimation of their parameters are derived, and their application to colocated multiple-input multiple-output radar systems is investigated.</div>

scholarly journals Novel Methods for Approximate Maximum Likelihood Estimation of Multiple Superimposed Undamped Tones and Their Application to Radar Systems

2021 ◽  
Author(s):  
Pasquale Di Viesti ◽  
ALESSANDRO DAVOLI ◽  
Giorgio Guerzoni ◽  
Giorgio Matteo Vitetta
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Multiple Input Multiple Output ◽  
Likelihood Estimation ◽  
Approximate Maximum Likelihood ◽  
Radar Systems ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Novel Methods

<div>In this manuscript, novel methods for the detection of multiple superimposed tones in noise and the estimation of their parameters are derived, and their application to colocated multiple-input multiple-output radar systems is investigated.</div>

scholarly journals Energy Efficient Fractional Particle Swarm Optimization Based Power Allocation in MIMO-NOMA System

2019 ◽  
Vol 8 (11) ◽  
pp. 1933-1939
Keyword(s):  
Particle Swarm Optimization ◽  
Power Allocation ◽  
Optimization Algorithm ◽  
Spectral Power ◽  
Multiple Input Multiple Output ◽  
Particle Swarm ◽  
Achievable Rate ◽  
Swarm Optimization ◽  
Multiple Input ◽  
Input Multiple Output

Non-Orthognal Multiple Access (NOMA) is a key technology used for improving the achievable rate in Multiple Input Multiple Output (MIMO) wireless networks of the next generation. In MIMO-NOMA systems the Energy Efficiency (EE) needs to be improved by a Fractional Particle Swarm Optimization Algorithm (FPSO) based on user ordering. To recommend capable energy and power allocation in the platform efficiently, the proposed optimization algorithm prioritizes the users based on satisfying the quality of service (QoS) and maximum power constraints. The FPSO algorithm prioritizes the users in optimal way by using objective function. The simulated results are analyze using the assessment metrics, like Bit Error Rate (BER), achievable rate, energy and spectral power. The performance of the FPSO-based power allocation approach is showing the higher spectral power, energy, achievable rate are 113.1915dB, 19.4898dB, 81.19153Mbps and lower BER of 0.0000152 respectively.

scholarly journals Novel Methods for Approximate Maximum Likelihood Estimation of Multiple Superimposed Undamped Tones and Their Application to Radar Systems

2021 ◽  
Author(s):  
Pasquale Di Viesti ◽  
ALESSANDRO DAVOLI ◽  
Giorgio Guerzoni ◽  
Giorgio Matteo Vitetta
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Multiple Input Multiple Output ◽  
Likelihood Estimation ◽  
Approximate Maximum Likelihood ◽  
Radar Systems ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Novel Methods

<div>In this manuscript, novel methods for the detection of multiple superimposed tones in noise and the estimation of their parameters are derived, and their application to colocated multiple-input multiple-output radar systems is investigated.</div>

scholarly journals A Barrage Jamming Strategy Based on CRB Maximization against Distributed MIMO Radar

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2453 ◽  
Author(s):  
Guangyong Zheng ◽  
Siqi Na ◽  
Tianyao Huang ◽  
Lulu Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Parameters Estimation ◽  
Swarm Optimization ◽  
Counter Measures ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Simulation Results ◽  
Cramer Rao Bound

Distributed multiple input multiple output (MIMO) radar has attracted much attention for its improved detection and estimation performance as well as enhanced electronic counter-counter measures (ECCM) ability. To protect the target from being detected and tracked by such radar, we consider a barrage jamming strategy towards a distributed MIMO. We first derive the Cramer–Rao bound (CRB) of target parameters estimation using a distributed MIMO under barrage jamming environments. We then set maximizing the CRB as the criterion for jamming resource allocation, aiming at degrading the accuracy of target parameters estimation. Due to the non-convexity of the CRB maximizing problem, particle swarm optimization is used to solve the problem. Simulation results demonstrate the advantages of the proposed strategy over traditional jamming methods.

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