Performance Analysis of ESPRIT Algorithm for Smart Antenna System

ESPRIT is a high-resolution signal parameter estimation technique based on the translational invariance structure of a sensor array. The ESPRIT algorithm is an attractive solution to many parameter estimation problems due to its low computational cost. The performance of DOA using estimation signal parameter via a rotational invariant technique is investigated in this paper. By exploiting invariance’s designed into the sensor array, parameter estimates are obtained directly, without knowledge of the array response and without computation or search of some spectral measure. The exact number of samples and elements used is the most important parameter in the algorithms in order to sustain the accuracy of the direction of arrival of the incident signals. This algorithm is more robust with respect to array imperfections than MUSIC.

Download Full-text

Signal parameter estimation and localization via maximum likelihood using a sensor array in the presence of Levy noise

6th International Conference on Signal Processing, 2002. ◽

10.1109/icosp.2002.1181070 ◽

2003 ◽

Cited By ~ 1

Author(s):

P.G. Georgiou ◽

C. Kyriakakis

Keyword(s):

Parameter Estimation ◽

Maximum Likelihood ◽

Sensor Array ◽

Lévy Noise ◽

Signal Parameter ◽

Levy Noise

Download Full-text

Direction of Arrival Estimation via ESPRIT Algorithm for Smart Antenna System

International Journal of Computer Applications ◽

10.5120/20747-3135 ◽

2015 ◽

Vol 118 (6) ◽

pp. 5-7 ◽

Cited By ~ 3

Author(s):

Deven Pradhan ◽

Rabindranath Bera

Keyword(s):

Smart Antenna ◽

Direction Of Arrival ◽

Antenna System ◽

Direction Of Arrival Estimation ◽

Esprit Algorithm ◽

Smart Antenna System

Download Full-text

Direction of Arrival (DOA) and Channel Estimation

Advances in Wireless Technologies and Telecommunication - Self-Organized Mobile Communication Technologies and Techniques for Network Optimization ◽

10.4018/978-1-5225-0239-5.ch009 ◽

2016 ◽

pp. 216-235 ◽

Cited By ~ 2

Author(s):

Shouman Barua ◽

Robin Braun

Keyword(s):

Parameter Estimation ◽

Communication System ◽

Smart Antenna ◽

Direction Of Arrival ◽

Antenna System ◽

Technical Solution ◽

Path Delay ◽

Rectangular Array ◽

Channel Characteristics ◽

Channel Information

Future fit demand combined with a flexible technical solution that is by latest wireless technology stands for. Estimating of the user's location is going to be an integral system with the upcoming mobile technology. This chapter shows some techniques for estimating the direction of arrival (DOA) with mathematical elaboration and simulation results as well. Estimating the DOA in this chapter is regarded to the purpose of using Smart antenna system. It is possible to estimate the location of a user by considering the uplink transmission system of the mobile communication system. Estimating the channel and accurate path delay is also an important task which might be done by using 1D Uniform Linear Array (ULA) or 2D Uniform Rectangular (URA) array antenna system. In this chapter, 1D ULA is considered in order to utilize some popular techniques. The performance of a communication system between two ends is substantially determined by the behaviors of the channel characteristics. It determines signal transformation while propagating through the channel between receivers and transmitters. Accurate channel information is crucial for both the transmitter and receiver ends to perform their best services. The ultimate focus of this chapter is to estimate the channel based on 2D parameter estimation. Uniform Rectangular Array (URA) is used to perform the 2D parameter estimation. It is possible to estimate Azimuth and Elevation of a source by using URA model.

Download Full-text

Approximate, Computationally Efficient Online Learning in Bayesian Spiking Neurons

Neural Computation ◽

10.1162/neco_a_00560 ◽

2014 ◽

Vol 26 (3) ◽

pp. 472-496 ◽

Cited By ~ 3

Author(s):

Levin Kuhlmann ◽

Michael Hauser-Raspe ◽

Jonathan H. Manton ◽

David B. Grayden ◽

Jonathan Tapson ◽

...

Keyword(s):

Parameter Estimation ◽

Online Learning ◽

Learning Algorithm ◽

Computational Cost ◽

Spiking Neurons ◽

Estimation Accuracy ◽

Parameter Estimates ◽

Computationally Efficient ◽

True Parameter ◽

Parameter Values

Bayesian spiking neurons (BSNs) provide a probabilistic interpretation of how neurons perform inference and learning. Online learning in BSNs typically involves parameter estimation based on maximum-likelihood expectation-maximization (ML-EM) which is computationally slow and limits the potential of studying networks of BSNs. An online learning algorithm, fast learning (FL), is presented that is more computationally efficient than the benchmark ML-EM for a fixed number of time steps as the number of inputs to a BSN increases (e.g., 16.5 times faster run times for 20 inputs). Although ML-EM appears to converge 2.0 to 3.6 times faster than FL, the computational cost of ML-EM means that ML-EM takes longer to simulate to convergence than FL. FL also provides reasonable convergence performance that is robust to initialization of parameter estimates that are far from the true parameter values. However, parameter estimation depends on the range of true parameter values. Nevertheless, for a physiologically meaningful range of parameter values, FL gives very good average estimation accuracy, despite its approximate nature. The FL algorithm therefore provides an efficient tool, complementary to ML-EM, for exploring BSN networks in more detail in order to better understand their biological relevance. Moreover, the simplicity of the FL algorithm means it can be easily implemented in neuromorphic VLSI such that one can take advantage of the energy-efficient spike coding of BSNs.

Download Full-text