scholarly journals Processing-Efficient Distributed Adaptive RLS Filtering for Computationally Constrained Platforms

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Noor M. Khan ◽  
Hasan Raza
Keyword(s):  
Kalman Filter ◽  
Processing Time ◽  
Adaptive Signal Processing ◽  
Least Square ◽  
Mimo Channel ◽  
Recursive Least Square ◽  
Rls Algorithm ◽  
Time Alignment ◽  
Complex Adaptive ◽  
Novel Processing

In this paper, a novel processing-efficient architecture of a group of inexpensive and computationally incapable small platforms is proposed for a parallely distributed adaptive signal processing (PDASP) operation. The proposed architecture runs computationally expensive procedures like complex adaptive recursive least square (RLS) algorithm cooperatively. The proposed PDASP architecture operates properly even if perfect time alignment among the participating platforms is not available. An RLS algorithm with the application of MIMO channel estimation is deployed on the proposed architecture. Complexity and processing time of the PDASP scheme with MIMO RLS algorithm are compared with sequentially operated MIMO RLS algorithm and liner Kalman filter. It is observed that PDASP scheme exhibits much lesser computational complexity parallely than the sequential MIMO RLS algorithm as well as Kalman filter. Moreover, the proposed architecture provides an improvement of 95.83% and 82.29% decreased processing time parallely compared to the sequentially operated Kalman filter and MIMO RLS algorithm for low doppler rate, respectively. Likewise, for high doppler rate, the proposed architecture entails an improvement of 94.12% and 77.28% decreased processing time compared to the Kalman and RLS algorithms, respectively.

scholarly journals RSS Based Wi-Fi Positioning Method Using Recursive Least Square (RLS) Algorithm

2018 ◽  
Vol 7 (2.24) ◽  
pp. 492
Author(s):  
Sreevardhan Cheerla ◽  
D Venkata Ratnam
Keyword(s):  
Least Square ◽  
Recursive Least Square ◽  
Rls Algorithm ◽  
Positioning Systems ◽  
Indoor Location ◽  
Positioning Method ◽  
Accurate Position ◽  
Software And Hardware ◽  
Newton Raphson ◽  
Raphson Method

Due to rapid increase in demand for services which depends upon exact location of devices leads to the development of numerous Wi-Fi positioning systems. It is very difficult to find the accurate position of a device in indoor environment due to substantial development of structures. There are many algorithms to determine the indoor location but they require expensive software and hardware. Hence receiving signals strength (RSS) based algorithms are implemented to find the self-positioning. In this paper Newton-Raphson, Gauss-Newton and Steepest descent algorithms are implemented to find the accurate location of Wi-Fi receiver in Koneru Lakshmaiah (K L) University, Guntur, Andhra Pradesh, India. From the results it is evident that Newton -Raphson method is better in providing accurate position estimations. 

Adaptive Modeling of Laser Powder Deposition Process for Control and Monitoring Application

2005 ◽  
Author(s):  
Mohammad Durali ◽  
Alireza Fathi ◽  
Amir Khajepour ◽  
Ehsan Toyserkani
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Least Square Method ◽  
Deposition Process ◽  
Least Square ◽  
Filter Method ◽  
Recursive Least Square ◽  
Noise Elimination ◽  
Laser Powder Deposition ◽  
Powder Deposition

Laser Powder Deposition technique is an advanced production method with many applications. Despite this fact, reliable and accurate control schemes have not yet fully developed for this method. This article presents method for in time identification of the process for modeling and adaptation of proper control strategy. ARMAX structure is chosen for system model. Recursive least square method and Kalman Filter methods are adopted for system identification, and their performance are compared. Experimental data was used for system identification, and proper filtering schemes are devised here for noise elimination and increased estimation results. It was concluded that although both methods yield efficient performance and accurate results, Kalman Filter method gives better results in parameter estimations. The comparison of the results shows that this method can be used very efficiently in control and monitoring of Laser Powder Deposition process.

Adaptive Order Tracking Technique Using Recursive Least-Square Algorithm

2002 ◽  
Vol 124 (4) ◽  
pp. 502-511 ◽  
Author(s):  
Mingsian R. Bai ◽  
Jihjau Jeng ◽  
Chingyu Chen
Keyword(s):  
Least Square ◽  
Recursive Least Squares ◽  
Frequency Resolution ◽  
Shaft Speed ◽  
Recursive Least Square ◽  
Rls Algorithm ◽  
Tracking Technique ◽  
Order Tracking ◽  
Conventional Methods ◽  
Bandpass Signals

Order tracking technique is one of the important tools for diagnosis of rotating machinery. Conventional methods of order tracking are primarily based on Fourier analysis with reference to shaft speed. Resampling is generally required in the fast Fourier transform (FFT)-based methods to compromise between time and frequency resolution for varying shaft speeds. Conventional methods suffer from a number of shortcomings. In particular, smearing problems arise when closely spaced orders or crossing orders are present. Conventional methods also are ineffective for the applications involving multiple independent shaft speeds. This paper presents an adaptive order tracking technique based on the Recursive Least-Squares (RLS) algorithm to overcome the problems encountered in conventional methods. In the proposed method, the problem is treated as the tracking of frequency-varying bandpass signals. Order amplitudes can be calculated with high resolution by using the proposed method in real-time fashion. The RLS order tracking technique is applicable whether it is a single-axle or multi-axle system.

scholarly journals On Modelling and Comparative Study of LMS and RLS Algorithms for Synthesis of MSA

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Ahmad Kamal Hassan ◽  
Adnan Affandi
Keyword(s):  
Worked Examples ◽  
Analytical Framework ◽  
Least Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Rls Algorithm ◽  
Contour Plots ◽  
Comprehensive Comparison ◽  
Artificial Neural Network Ann ◽  
Spread Factor

This paper deals with analytical modelling of microstrip patch antenna (MSA) by means of artificial neural network (ANN) using least mean square (LMS) and recursive least square (RLS) algorithms. Our contribution in this work is twofold. We initially provide a tutorial-like exposition for the design aspects of MSA and for the analytical framework of the two algorithms while our second aim is to take advantage of high nonlinearity of MSA to compare the effectiveness of LMS and that of RLS algorithms. We investigate the two algorithms by using gradient decent optimization in the context of radial basis function (RBF) of ANN. The proposed analysis is based on both static and adaptive spread factor. We model the forward side or synthesis of MSA by means of worked examples and simulations. Contour plots, 3D depictions, and Tableau presentations provide a comprehensive comparison of the two algorithms. Our findings point to higher accuracies in approximation for synthesis of MSA using RLS algorithm as compared with that of LMS approach; however the computational complexity increases in the former case.

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