A Novel Method for DOA and Time Delay Joint Estimation in Multipath OFDM Environment

Li, Xiangzhi; Cui, Weijia; Xu, Haiyun; Ba, Bin; Zhang, Yankui

doi:10.1155/2020/3952175

Cited by 5 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al [17] introduced a new approach for exploiting space-frequency features to estimate DOAs (Direction-of-Arrivals) and TDEs of multi-path OFDM (Orthogonal Frequency Division Multiplexing) signals. The study's scheme combined array structures and frequencies to generate extended virtual arrays.…”

Section: Literature Reviewmentioning

confidence: 99%

Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

Jagadesh¹,

Rani²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

RSs (Radar Systems) identify and trace targets and are commonly employed in applications like air traffic control and remote sensing. They are necessary for monitoring precise target trajectories. Estimations of RSs are nonlinear as the parameters TDEs (time delay Estimations) and Doppler shifts are computed on receipt of echoes where EKFs (Extended Kalman Filters) and UKFs (Unscented Kalman Filters) have not been examined for computations. RSs, certain times result in poor accuracies and SNRs (low signal to noise ratios) especially, while encountering complicated environments. This work proposes IUKFs (Iterated UKFs) to track online filter performances while using optimization techniques to enhance outcomes. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using MCEHOs (Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system non-linearity where OIUKFs (Optimized Iterative UKFs) predict a target's unknown parameters. To obtain optimal solutions theoretically, OIUKFs take less iteration, resulting in shorter execution times. The proposed OIUKFs provide numerical approximations which are derivative-free implementations. Simulation evaluation results with estimators show better performances in terms of reduced NMSEs (Normalized Mean Square Errors), RMSEs (Root Mean Squared Errors), SNRs, variances, and better accuracies than current approaches.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

Jagadesh¹,

Rani²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…A population agent using a typical Kalman filter framework to solve optimization issues comprised simulated measurement procedures [ 26 ]. Their findings from SKF were compared with other metaheuristic algorithms using statistical analysis where findings revealed that the suggested SKF algorithm was a promising technique that outperformed various well-known metaheuristic algorithms such as GAs (Genetic Algorithms), PSOs (Particle Swarm Optimizations), BHAs (Black Hole Algorithms), and GWOs (Grey Wolf Optimizers) [ 27 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services

Jain

Alsanie

Gago

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

ECG (electrocardiogram) identifies and traces targets and is commonly employed in cardiac disease detection. It is necessary for monitoring precise target trajectories. Estimations of ECG are nonlinear as the parameters TDEs (time delays) and Doppler shifts are computed on receipt of echoes where EKFs (extended Kalman filters) and electrocardiogram have not been examined for computations. ECG, certain times, results in poor accuracies and low SNRs (signal-to-noise ratios), especially while encountering complicated environments. This work proposes to track online filter performances while using optimization techniques to enhance outcomes with the removal of noise in the signal. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using IMCEHOs (Improved Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system nonlinearity where multi-iterative function (Optimized Iterative UKFs) predicts a target’s unknown parameters. To obtain optimal solutions theoretically, multi-iterative function takes less iteration, resulting in shorter execution times. The proposed multi-iterative function provides numerical approximations, which are derivative-free implementations. Signals are updated in the cloud environment; the updates are received by the patients from home. The simulation evaluation results with estimators show better performances in terms of reduced NMSEs (normalized mean square errors), RMSEs (root mean squared errors), SNRs, variances, and better accuracies than current approaches. Machine learning algorithms have been used to predict the stages of heart disease, which is updated to the patient in the cloud environment. The proposed work has a 91.0% accuracy rate with an error rate of 0.05% by reducing noise levels.

show abstract

“…(iv) Compared with the existing RIS-and OFDM-based localization algorithms [23,24], the proposed algorithm can obtain the required parameters for localization based on a single station node in a coherent environment. Simulation results show that the proposed algorithm avoids the aperture loss of current smoothing algorithms [22,25] when processing coherent signals and thus has more advantages in terms of estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation for Time Delay and Direction of Arrival in Reconfigurable Intelligent Surface with OFDM

Cui

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Recently, the joint estimation for time delay (TD) and direction of arrival (DOA) has suffered from the high complexity of processing multi-dimensional signal models and the ineffectiveness of correlated/coherent signals. In order to improve this situation, a joint estimation method using orthogonal frequency division multiplexing (OFDM) and a uniform planar array composed of reconfigurable intelligent surface (RIS) is proposed. First, the time-domain coding function of the RIS is combined with the multi-carrier characteristic of the OFDM signal to construct the coded channel frequency response in tensor form. Then, the coded channel frequency response covariance matrix is decomposed by CANDECOMP/PARAFAC (CPD) to separate the signal subspaces of TD and DOA. Finally, we perform a one-dimensional (1D) spectral search for TD values and a two-dimensional (2D) spectral search for DOA values. Compared to previous efforts, this algorithm not only enhances the adaptability of coherent signals, but also greatly decreases the complexity. Simulation results indicate the robustness and effectiveness for the proposed algorithm in independent, coherent, and mixed multipath environments and low signal-to-noise ratio (SNR) conditions.

show abstract

A Novel Method for DOA and Time Delay Joint Estimation in Multipath OFDM Environment

Cited by 5 publications

References 26 publications

Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services

Joint Estimation for Time Delay and Direction of Arrival in Reconfigurable Intelligent Surface with OFDM

Contact Info

Product

Resources

About