Elias Aboutanios

Iterative frequency estimation by interpolation on Fourier coefficients

The estimation of the frequency of a complex exponential is a problem that is relevant to a large number of fields. In this paper, we propose and analyze two new frequency estimators that interpolate on the Fourier coefficients of the received signal samples. The estimators are shown to achieve identical asymptotic performances. They are asymptotically unbiased and normally distributed with a variance that is only 1.0147 times the asymptotic Crame/spl acute/r-Rao bound (ACRB) uniformly over the frequency estimation range.

A modified dichotomous search frequency estimator

The estimation of the frequency of a sinusoidal signal has been dealt with extensively in the literature. In this letter, we examine the dichotomous search of the periodogram peak algorithm. We provide an insight into the need to pad the data with zeros in order to achieve a performance that is comparable to the Cramer-Rao lower bound (CRB). We also propose a modified dichotomous search estimator that operates on the unpadded data sequence resulting in a computational saving. The modified dichotomous search is shown to have a performance that is comparable with the CRB.

A new algorithm for the estimation of the frequency of a complex exponential in additive Gaussian noise

The letter presents a new algorithm for the precise estimation of the frequency of a complex exponential signal in additive, complex, white Gaussian noise. The discrete Fourier transform (DFT)-based algorithm performs a frequency interpolation on the results of an N point complex fast Fourier transform. For large N and large signal to noise ratio, the frequency estimation error variance obtained is 0.063 dB above the Cramer-Rao bound. The algorithm has low computational complexity and is well suited for real time digital signal processing applications, including communications, radar and sonar.

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

The availability of location information has become a key factor in today’s communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the global positioning system (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.

Reconfigurable Adaptive Array Beamforming by Antenna Selection

Traditional adaptive array beamforming with a fixed array configuration can lead to significant inefficiencies and performance loss under different scenarios. As antennas become smaller and cheaper relative to front-ends, it becomes important to devise a reconfigurable adaptive antenna array (RAAA) strategy to yield high signal to noise and interference ratio using fewer antennas. This is achieved by selecting K from N antennas to minimize the Spatial Correlation Coefficient (SCC) between the desired signal and the interference. The lower bound of optimum SCC is formulated with two relaxation methods to give information about the suitable number of selected antennas K. A Correlation Measurement (CM) method is proposed to select the optimum subarray with K antennas, thereby reducing complexity. We carry out performance analysis and show that a 1/K2-suboptimum solution can be guaranteed with arbitrary shaped arrays. Furthermore, a Difference of Convex Sets (DCS) method is proposed to select the optimum subarray with controlled quiescent pattern in order to reduce the effect of interference during the reconfiguration time. The utility of the proposed array reconfiguration for performance improvement without increasing the cost is demonstrated using both simulated and experimental data.

Estimating the parameters of sinusoids and decaying sinusoids in noise

In this article, the estimation of the frequency of a complex exponential in noise is reviewed. We examined both the undamped and damped cases and presented robust, accurate, and computationally efficient estimators for the frequency of the former and the frequency and damping factor of the latter.

A STAP algorithm for radar target detection in heterogeneous environments

Traditional STAP processors for radar target detection, such as the GLRT and AMF, require an estimate of the noise covariance matrix. In practice, this estimate is obtained from a training data set that is usually constructed from range gates surrounding the test gate. The training data must be target free and statistically homogeneous with the test data. In heterogeneous and target rich environments, these assumptions do not necessarily hold and degradation in the detection performance results. In this paper, we propose a new detection algorithm, which we call the maximum likelihood estimation detector (MLED), and that operates only on the test data. We show that the new detector has the highly desirable CFAR property. We give the expressions for its probabilities of false alarm and detection and show that it has a performance that is comparable with the traditional algorithms

Estimation of the Frequency and Decay Factor of a Decaying Exponential in Noise

In this paper, we examine the estimation of the parameters of a decaying complex exponential in noise. The strategy adopted consists of a computationally simple two stage scheme where an interpolation stage refines the coarse estimate obtained from an initial maximum bin search. The interpolators of Quinn, and of Aboutanios and Mulgrew, developed for undamped exponentials are extended to the damped case. In the process, we show that Quinns estimator can be viewed as a linearized version of Bertoccos algorithm. Theoretical analysis demonstrates that the resulting estimators exhibit similar behavior to the undamped case, leading us to propose two alternative hybrid implementations that yield a significant improvement in the estimation performance. Unlike the undamped case, however, we show that there exists a finite number of samples for which the estimation performance is best, and which we determine in terms of the damping factor. This enables us to adjust the actual number of samples should it deviate significantly from the optimum. Extensive simulation results are presented to support the theoretical findings.

Hybrid Detection Approach for STAP in Heterogeneous Clutter

We address the problem of radar target detection under clutter heterogeneity. Traditional approaches, designated as the two-data set (TDS) algorithms, require a training data set in order to estimate the interference covariance matrix and implement the adaptive filter. This training data is usually drawn from range gates adjacent to the cell under test (CUT) that are deemed to be statistically homogeneous with it. When the training data exhibits statistical heterogeneity with respect to the test data, the performance of the TDS detectors degrades. The single-data set (SDS) detectors have been proposed to deal with this problem by operating solely on the test data. In this paper, we present a general hybrid approach that combines the SDS and TDS algorithms, taking the degree of heterogeneity into account. This makes the SDS and TDS detectors special cases of the more general hybrid formulation. We derive the hybrid detectors and propose the use of the generalised inner product as a heterogeneity measure. We analyse the new hybrid detectors and give expressions for the probabilities of false alarm and detection when the clutter is assumed homogeneous, and we assess their performance under heterogeneity using Monte Carlo simulations. The results show that the new detectors outperform both the TDS and SDS algorithms under both homogeneous and heterogeneous interference.

Sparse Arrays and Sampling for Interference Mitigation and DOA Estimation in GNSS

This paper establishes the role of sparse arrays and sparse sampling in antijam global navigation satellite systems (GNSS). We show that both jammer direction of arrival estimation methods and mitigation techniques benefit from the design flexibility of sparse arrays and their extended virtual apertures or coarrays. Taking advantage of information redundancy, significant reduction in hardware and computational cost materializes when selecting a subset of array antennas without sacrificing jammer nulling or localization capabilities. In addition to the spatial array sparsity, antijam can utilize sparsity of jammers in the spatio-temporal frequency domains. By virtue of their finite number, jammers in the field of view are sparse in the azimuth and elevation directions. For the class of frequency modulated jammers, sparsity is also exhibited in the joint time-frequency signal representation. These spatial and signal characteristics have called for the development of sparsity-aware antijam techniques for the accurate estimation of jammer space-time-frequency signature, enabling its effective sensing and excision. Both theory and simulation examples demonstrate the utility of coarrays, sparse reconstructions, and antenna selection techniques for antijam GNSS.