Computerized ionospheric tomography with the IRI model

Abstract Computerized ionospheric tomography (CIT) is a method to estimate ionospheric electron density distribution by using the global positioning system (GPS) signals recorded by the GPS receivers. Ionospheric electron density is a function of latitude, longitude, height and time. A general approach in CIT is to represent the ionosphere as a linear combination of basis functions. In this study, the model of the ionosphere is obtained from the IRI in latitude and height only. The goal is to determine the best representing basis function from the set of Squeezed Legendre polynomials, truncated Legendre polynomials, Haar Wavelets and singular value decomposition (SVD). The reconstruction algorithms used in this study can be listed as total least squares (TLS), regularized least squares, algebraic reconstruction technique (ART) and a hybrid algorithm where the reconstruction from the TLS algorithm is used as the initial estimate for the ART. The error performance of the reconstruction algorithms are compared with respect to the electron density generated by the IRI-2001 model. In the investigated scenario, the measurements are obtained from the IRI-2001 as the line integral of the electron density profiles, imitating the total electron content estimated from GPS measurements. It has been observed that the minimum error between the reconstructed and model ionospheres depends on both the reconstruction algorithm and the basis functions where the best results have been obtained for the basis functions from the model itself through SVD

Wide-band maximum likelihood direction finding by using tree-structured EM algorithm

A tree structured Expectation Maximization (EM) algorithm is proposed and applied to the wide-band angle of arrival estimation. It may be seen as a generalization on EM using the ideas of Cascade EM algorithm and Space Alternating Generalized EM algorithm. Also, for passive data acquisition, robust and efficient alternatives for the estimation of the source signals are investigated

NOISE REMOVAL FOR PIECEWISE POLYNOMIAL SIGNALS BASED ON PARTICAL SWARM OPTIMİZATION

Parçalı sürekli yapıdaki sinyallerin üzerindeki gürültünün bastırımı amacıyla parçacık sürüsü optimizasyon tekniğinin kullanıldığı bir teknik geliştirilmiştir. Model sinyal olarak parçaların başlangıç noktaları ve her bir parçanın az sayıda parametreyle tanımlandığı bir sinyal kümesi kullanılmıştır. Önerilen yaklaşımda, parça sayısının bilindiği durumda yerel en iyi konumlandırmalardan kaçınmak amacıyla, parçalar arasındaki optimal geçiş sınırları Parçacık Sürüsü Optimizasyonu (PSO) ile bulunur. Her bir parça içerisindeki sinyal parametreleri ise optimal geçiş sınırlarına bağlı olarak En Büyük Olabilirlik (EBO) kestirimiyle elde edilir. Önerilen algoritma geçiş sınırlarının sayısı bilinmediği durumlarda kullanılabilecek şekilde genelleştirilmiştir. Sıklıkla kullanılan ve başarımı yüksek diğer tekniklerle yapılan kıyaslama sonunda önerilen tekniğin önemli başarım artışı sağladığı gösterilmiştir. Piecewise smooth signal denoising is cast as a non-linear optimization problem in terms of transition boundaries and a parametric signal family. To avoid locally optimal placement of boundaries, optimal positions of transition boundaries for a given number of transitions are obtained by using particle swarm optimization. The piecewise smooth section parameters are obtained as the maximum likelihood estimates conditioned on the optimal transition boundaries. The proposed algorithm is extended to the case where the number of transition boundaries are unknown by sequentially increasing number of sections until the residual error is at the level of noise standard deviation. Performance comparison with the state of the art techniques reveals the important advantages of the proposed technique

Efficient algorithm to extract components of a composite signal

An efficient algorithm is proposed to extract components of a composite signal. The proposed approach has two stages of processing in which the time-frequency supports of the individual signal components are identified and then the individual components are estimated by performing a simple time-frequency domain incision on the identified support of the component. The use of a recently proposed time-frequency representation [1] significantly improves the performance of the proposed approach by providing very accurate description on the auto-Wigner terms of the composite signal. Then, simple fractional Fourier domain incision provides reliable estimates for each of the signal components in O(N log N) complexity for a composite signal of duration N

Comparison of two methods for fusing information from a linear array of sonar sensors for obstacle localization

The performance of a commonly employed linear array of sonar sensors is assessed for point-obstacle localization intended for robotics applications. Two different methods of combining time-of-flight information from the sensors are described to estimate the range and azimuth of the obstacle: pairwise estimate method and the maximum likelihood estimator. The variances of the methods are compared to the Cramer-Rao Lower Bound, and their biases are investigated. Simulation studies indicate that in estimating range, both methods perform comparably; in estimating azimuth, maximum likelihood estimate is superior at a cost of extra computation. The results are useful for target localization in mobile robotics

Performance analysis of two linear array processing algorithms for obstacle localization

The performance of a commonly employed linear array of sonar sensors is assessed for point- target localization. Two different methods of combining time-of-flight information from the sensors are described to estimate the range and azimuth of the target: pairwise estimate method and the maximum likelihood estimator. The biases and variances of the methods are investigated and their combined effect is compared to the Cramer-Rao Lower Bound. Simulation studies indicate that in estimating range, both methods perform comparably; in estimating azimuth, maximum likelihood estimate is superior at a cost of extra computation

Theoretical investigation on exact blind channel and input sequence estimation

Recent work on fractionally spaced blind equalizers have shown that it is possible to exactly identify the channel and its input sequence from the noise-free channel outputs. However, the obtained results are based on a set of over-restrictive constrainst on the channel. In this paper it is shown that the exact identification can be achieved in a broader class of channels