1,720,998 research outputs found
2-D DOA estimation in case of unknown mutual coupling for multipath signals
No full textWOS: 000367895200005In this paper, two dimensional (2-D) direction-of-arrival (DOA) estimation problem in case of unknown mutual coupling and multipath signals is investigated for antenna arrays. A new technique is proposed which uses a special array structure consisting of parallel uniform linear array (PULA). PULA structure is complemented with auxiliary antennas in order to have a structured mutual coupling matrix (MCM). MCM has a symmetric banded Toeplitz structure which allows the application of the ESPRIT algorithm for 2-D paired DOA estimation. The advantage of the PULA structure is exploited by dividing it into overlapping linear sub-arrays (triplets) and spatial smoothing is employed to mitigate multipath signals. Closed form expressions are presented for search-free, paired and unambiguous 2-D DOA estimation. Two algorithms PULA-1 and PULA-2 are proposed to effectively solve the problem. Several simulations are done and the accuracy of the proposed solution is shown
Wind Speed Forecasting with Missing Values
No full text7th International Conference on Information Science and Technology (ICIST) -- APR 16-19, 2017 -- Da Nang, VIETNAMWOS: 000403402600010In this study, a new short term wind speed forecasting approach, which uses long term past observations, is proposed. The performance assessment of the wind speed forecasting framework is carried out using real data from meteorological stations in Marmara region of Turkey. The data sets are not complete due to equipment failures. The proposed approach builds on data de-trending, covariance-factorization via a subspace method, and one-step-ahead and multi-step-ahead Kalman filter prediction ideas. It is shown that trimming of diurnal, weekly, monthly, and annual patterns in data significantly enhances estimation accuracy. Experimental test results demonstrate that the proposed multi-step-ahead forecasting outperforms the benchmark values computed with the persistent forecasting models.City Univ Hong Kong, Vietnam Korea Friendship Informat Technol Coll, Hong Kong Web Soc, IEEE Syst, Man & Cybernet SocAnadolu University Scientific Research Projects Fund [1602F070]The first author was supported by the Anadolu University Scientific Research Projects Fund under Grant 1602F070
A geometrical closed form solution for RSS based far-field localization: Direction of Exponent Uncertainty
No full textWOS: 000457945500016In this study, a new powerful geometrical closed-form solution called Direction of Exponent Uncertainty (DEU) is proposed for received signal strength (RSS) based far-field localization when path loss exponent (PLE) and transmit power are both unknown. The uncertainty in the PLE due to environmental factors is a significant challenge for RSS based localization. DEU is built after careful investigation of geometrical behaviors of differential received signal strength circles, i.e. the locus of possible location of the emitter when transmit power is unknown. It is shown that the uncertainty in the PLE corresponds to a linear uncertainty for the location of the emitter in two dimensional space. This critical observation creates a basis for the sensor to move towards the emitter without estimating the emitter location after only three measurements. Furthermore, with only four different measurements, it is possible to effectively estimate the location of the emitter as well as the PLE by means of intersection of DEUs. Intersection of DEUs attains Cramer Rao Lower Bound with a dramatically reduced execution time compared to nonlinear least squares estimator. DEU is also proposed as an efficient route planning tool for moving sensors such as unmanned aerial vehicles.TUBITAK (The Scientific and Technological Research Council of Turkey) [115E185]; Anadolu University [1606F559]This study is funded by TUBITAK (The Scientific and Technological Research Council of Turkey) with the project number 115E185 and by Anadolu University with the project number 1606F559
A Sparse Approach for the Presence Detection of Long-code DS-SS Signals
No full text23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEYWOS: 000380500900426In this paper, the presence detection of long-code direct sequence spread spectrum signals (DS-SS) in low signal to noise ratio (SNR) using the sensor array are considered. A sparse method, which is fast and computationally efficient, is proposed for the presence detection of these signals. The proposed method is based on the phase linearity of the cross channel terms of the wideband spatial covariance (R) matrices. In addition, a new sample R matrix estimation technique, which is sparse and called as block of consecutive frequency bins (BCFB), is proposed. The proposed BCFB technique provides a fast signal detection which is the desirable for the real time applications. It is shown in various simulations that the proposed detection method can detect long-code DS-SS signals in very low SNR.Dept Comp Engn & Elect & Elect Engn, Elect & Elect Engn, Bilkent Uni
Short-Term Wind Speed Forecasting by Spectral Analysis
No full text29th Irish Signals and Systems Conference (ISSC) -- JUN 21-22, 2018 -- Belfast, IRELANDWOS: 000462519500049In this paper, we apply a wind speed forecasting framework developed earlier by the authors to the two-dimensional wind velocity measurements collected from a meteorological station in the Marmara region of Turkey over a short-term. In the application of the framework, first the measurements are de-trended for the diurnal and the weekly patterns. Other stages of the framework are covariance-factorization via a recently developed subspace method and one-step-ahead and/or multi-step-ahead Kalman filter predictions.Qeens Univ, IEEE, IEEE Computat Intelligence Soc, IEEE Signal Proc Chapter, Analog Devices, Rohde & Schwarz, MathWorks, Visit Belfast, Xilinx, Irish Mfg Res, Fest
Detection of Low Probability of Intercept Signals in Low SNR Using Multichannel Sensor Arrays
No full text22nd IEEE Signal Processing and Communications Applications Conference (SIU) -- APR 23-25, 2014 -- Karadeniz Teknik Univ, Trabzon, TURKEYWOS: 000356351400441In this paper, a method is proposed for the detection of low probability of intercept, LPI signals in high noise without prior knowledge and assumption using spatially distributed multichannel wideband sensor array. The proposed detection method uses eigenvalue ratios (EVR) of wideband spatial covariance matrix as test statistic. It is shown in simulations that the proposed method can detect long code spread spectrum signals in a stable way even in low SNR In addition, it is also shown in simulations that the proposed method is inherently resistive to the multipath reflections.IEEE, Karadeniz Tech Univ, Dept Comp Engn & Elect & Elect Eng
RSS Based Direction Finding via Array of Directional Antennas with Normal Density Distribution in Magnitude
No full text10th International Conference on Electrical and Electronics Engineering (ELECO) -- NOV 30-DEC 02, 2017 -- Bursa, TURKEYWOS: 000426978800111In this study, the theoretical framework of received signal strength (RSS) based direction finding with array of directional antennas with normal density distribution in magnitude are established. Directional antenna systems offer simple and effective solutions, when the antennas must be placed within a limited area, such as on a car. While some important studies exist, the theoretical framework to create such systems where the radiation patterns of directional antennas are modeled as von Mises distributions (i.e. circular version of normal distribution) in magnitude are lacking in the literature. Therefore, in this study first, Cramer Rao Lower Bound (CRLB) is explicitly calculated for joint estimation of angle of arrival and incident signal power. It is found that CRLB is not a function of angle of arrival, and it decreases with the increasing directivity of the antennas. Finally, maximum likelihood estimation (MLE) is discussed by analyzing its ability of convergence.Chamber Elect Engineers Bursa Branch, Uludag Univ, Fac Engn, Dept Elect & Elect Engn, Istanbul Tech Univ, Fac Elect & Elect Engn, Sci & Technolog Res Council Turkey, IEEE Turkey SectAnadolu University [1606F559]This study is funded by Anadolu University with the project number 1606F559
Presence Detection of Long-and-Short-Code DS-SS Signals Using the Phase Linearity of Multichannel Sensors
No full text19th International Conference on Digital Signal Processing (DSP) -- AUG 20-23, 2014 -- Hong Kong, PEOPLES R CHINAWOS: 000361019500057In this study, the detection of direct sequence spread spectrum (DS-SS) signals in white Gaussian noise without prior knowledge is investigated for spatially distributed multichannel wideband sensor array. A novel technique for the presence detection of both the long-and-short-code DS-SS signals is proposed which uses the frequency domain spatial covariance matrix of the sensor array. The technique is based on the linearity of the phase response of the wideband spatial covariances of cross sensors along the bandwidth of the signal. It is shown in simulations that the technique can detect DS-SS signals in a stable way at low signal to noise ratio (SNR) without any prior information and assumption on target. In addition, the proposed method can detect DS-SS signals in the presence of narrow band interference and multipath reflections
Contactless Respiration Rate Estimation Using MUSIC Algorithm
No full text10th International Conference on Electrical and Electronics Engineering (ELECO) -- NOV 30-DEC 02, 2017 -- Bursa, TURKEYWOS: 000426978800101In some diseases (sleep apnea, sudden infant death syndrome etc.), continuous monitoring of respiration rate of patient at home during sleep is critically important. Nowadays wireless communications signals are widely used in our homes. In this paper, we propose a contactless respiration monitoring system which uses only ambient wireless communications signals to estimate the respiration rate of a person. Laboratory experiments show that the strentgh of the received radio frequency (RF) signals changes due to inhaling/exhaling of a person between the propagation path of the transmitter and the receiver. In this study, a subspace based MUSIC algorithm is proposed to estimate the respiration rate of a person using ambient wireless signals. It is shown in various laboratory experiments, where real data is collected with software defined radios, the MUSIC algorithm can successfully estimate the respiration rate with minimum error compared to the FFT-based Maximum Likelihood Estimation (MLE) approach.Chamber Elect Engineers Bursa Branch, Uludag Univ, Fac Engn, Dept Elect & Elect Engn, Istanbul Tech Univ, Fac Elect & Elect Engn, Sci & Technolog Res Council Turkey, IEEE Turkey Sec
Wind speed forecasting by subspace and nuclear norm optimization based algorithms
No full textIn this paper, we study properties of two wind speed forecasting schemes from mid-to-short term wind velocity measurements. The measurements are assumed to be collected over time intervals shorter than the ones previously studied by the authors. Two application examples illustrate the properties of these schemes. In the first example, historical data originating from five meteorological stations are considered. This example demonstrates that the first scheme outperforms persistence and artificial neural network (ANN) predictors by a large margin for all step sizes considered. This result enlarges domain of application of the first forecasting scheme from multi-step-ahead only to one and multi-step-ahead and from long-term observations only to long and mid-term observations. A local wildly fluctuating dense data set obtained from a renewable energy research home unit is studied in the second example to check the performance of the first scheme under non-standard operating conditions. The second scheme is a compressive subspace algorithm developed recently for innovation models.The second scheme complements the first scheme in that it uses short-term observations and outperforms the multi-step-ahead persistence and the ANN predictor
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