Direction finding in the presence of a more realistic environment model

Direction-of-arrival (DOA) estimation is susceptible to errors introduced by the presence of real-ground and resonant size scatterers in the vicinity of the antenna array. To compensate for these errors pre-calibration and auto-calibration techniques are presented. The effects of real-ground constituent parameters on the mutual coupling (MC) of wire type antenna arrays for DOA estimation are investigated. This is accomplished by pre-calibration of the antenna array over the real-ground using the finite element method (FEM). The mutual impedance matrix is pre-estimated and used to remove the perturbations in the received terminal voltage. The unperturbed terminal voltage is incorporated in MUSIC algorithm to estimate DOAs. First, MC of quarter wave monopole antenna arrays is investigated. This work augments an existing MC compensation technique for ground-based antennas and proposes reduction in MC for antennas over finite ground as compared to the perfect ground. A factor of 4 decrease in both the real and imaginary parts of the MC is observed when considering a poor ground versus a perfectly conducting one for quarter wave monopoles in the receiving mode. A simulated result to show the compensation of errors direction of arrival (DOA) estimation with actual realization of the environment is also presented. Secondly, investigations for the effects on received MC of λ/2 dipole arrays placed near real-earth are carried out. As a rule of thumb, estimation of mutual coupling can be divided in two regions of antenna height that is very near ground

Antenna array calibration in wireless communications

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Enhanced Direction of Arrival Estimation through Electromagnetic Modeling

Engineering is a high art that balances modeling the physical world and designing meaningful solutions based on those models. Array signal processing is no exception, and many innovative and creative solutions have come from the field of array processing. However, many of the innovative algorithms that permeate the field are based on a very simple signal model of an array. This simple, although powerful, model is at times a pale reflection of the complexities inherent in the physical world, and this model mismatch opens the door to the performance degradation of any solution for which the model underpins. This dissertation seeks to explore the impact of model mismatch upon common array processing algorithms. To that end, this dissertation brings together the disparate topics of electromagnetics and signal processing. Electromagnetics brings a singular focus on the physical interactions of electromagnetic waves and physical array structures, while signal processing brings modern computational power to solve difficult problems. We delve into model mismatch in two ways; first, by developing a blind array calibration routine that estimates model mismatch and incorporates that knowledge into the reiterative superresoluiton (RISR) direction of arrival estimation algorithm; second, by examining model mismatch between a transmitting and receiving array, and assessing the impact of this mismatch on prolific direction of arrival estimation algorithms. In both of these studies we show that engineers have traded algorithm performance for model simplicity, and that if we are willing to deal with the added complexity we can recapture that lost performance

DOA estimation and tracking of ULAs with mutual coupling

A class of subspace-based methods for direction-of-arrival (DOA) estimation and tracking in the case of uniform linear arrays (ULAs) with mutual coupling is proposed. By treating the angularly-independent mutual coupling as angularly-dependent complex array gains, the middle subarray is found to have the same complex array gains. Using this property, a new way for parameterizing the steering vector is proposed and the corresponding method for joint estimation of DOAs and mutual coupling matrix (MCM) using the whole array data is derived based on subspace principle. Simulation results show that the proposed algorithm has a better performance than the conventional subarray-based method especially for weak signals. Furthermore, to achieve low computational complexity for online and time-varying DOA estimation, three subspace tracking algorithms with different arithmetic complexities and tracking abilities are developed. More precisely, by introducing a better estimate of the subspace to the conventional tracking algorithms, two modified methods, namely modified projection approximate subspace tracking (PAST) (MPAST) and modified orthonormal PAST (MOPAST), are developed for slowly changing subspace, whereas a Kalman filter with a variable number of measurements (KFVM) method for rapidly changing subspace is introduced. Simulation results demonstrate that these algorithms offer high flexibility and effectiveness for tracking DOAs in the presence of mutual coupling. © 2006 IEEE.published_or_final_versio

DOA estimation and mutual coupling calibration with the SAGE algorithm

AbstractIn this paper, a novel algorithm is presented for direction of arrival (DOA) estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array output and mutual coupling coefficients, we present a novel model of the array output with the unknown mutual coupling coefficients. Based on this model, we use the space alternating generalized expectation-maximization (SAGE) algorithm to jointly estimate the DOA parameters and the mutual coupling coefficients. Unlike many existing counterparts, our method requires neither calibration sources nor initial calibration information. At the same time, our proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. By numerical experiments we demonstrate that our proposed method outperforms the existing method for DOA estimation and mutual coupling calibration

SAGE-Based Algorithm for Direction-of-Arrival Estimation and Array Calibration

Most existing array processing algorithms are very sensitive to model uncertainties caused by the mutual coupling and sensor location error. To mitigate this problem, a novel method for direction-of-arrival (DOA) estimation and array calibration in the case of deterministic signals with unknown waveforms is presented in this paper. The analysis begins with a comprehensive perturbed array output model, and it is effective for various kinds of perturbations, such as mutual coupling and sensor location error. Based on this model, the Space Alternating Generalized Expectation-Maximization (SAGE) algorithm is applied to jointly estimate the DOA and array perturbation parameters, which simplifies the multidimensional search procedure required for finding maximum likelihood (ML) estimates. The proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. At the same time, it forms a unified framework for DOA and array perturbation parameters estimation in the presence of mutual coupling and sensor location error. The simulation results demonstrate the effectiveness of the algorithm

Simple system for locating ground loops

A simple low-cost system for rapid identification of the cables causing ground loops in complex instrumentation configurations is described. The system consists of an exciter module that generates a 100 kHz ground loop current and a detector module that determines which cable conducts this test current. Both the exciter and detector are magnetically coupled to the ground circuit so there is no physical contact to the instrumentation system under test

TECHNIQUES AND INSTRUMENTATION FOR PHASED ARRAY CALIBRATION

Active phased arrays suffer the inherent problem of excitation errors, i.e., incorrect phase and amplitude excitation of the antenna elements. Excitation errors degrade critical performance parameters since they increase sidelobe level and reduce antenna gain and beam pointing accuracy. To ensure the correct operation of the array, it is necessary to quantify and compensate the phase and amplitude errors of each antenna element. The compensation is accomplished by calibrating the phased array radar. Calibration challenges include the quantification and compensation of errors initially, as well as maintenance of the calibration state once the system is fielded. This dissertation presents research on improving the calibration of the active phased array front-end for radar systems. A combination of custom-made instrumentation with initial and in-situ calibration techniques is proposed to calibrate an active array test-bed. The test-bed consists of an 8$\times$8 elements C-band array, and was developed in collaboration with NCAR-EOL to provide software and hardware features that enable the proposed calibration schemes. Different calibration techniques were experimentally tested. First, an initial calibration technique for phased array prototypes is proposed. The technique employs a planar NF scanner to sample the excitation of each antenna element, and also to scan the embedded element antenna patterns of the prototype. The novelty of the approach is that it combines the collected excitation data with the scanned embedded elements to allow the prediction of both the co- and cross-polar pattern components of the array. On the other hand, to explore techniques that do not rely on external equipment and use built-in feedback mechanisms instead, mutual coupling-based calibration is reviewed and implemented. Two techniques were tested: an initial type, proposed by Bekers et al., and a proposed in-situ type, conceived specifically for analog architectures, to track errors during fielded operation. It was found that mutual coupling calibration techniques are excellent options for in-situ applications, with a root mean squared error (RMSE) in phase and amplitude of 0.75$^\circ$ and 0.12 dB, respectively. Whereas, for initial type calibration, the tested mutual coupling-based technique yields a RMSE of 2.5$^\circ$ and $\geq$ 1 dB, respectively, which is not accurate enough to replace conventional park and probe for initial calibration of small arrays. Finally, to complement calibration theory, the required calibration instrumentation is reviewed, and more importantly, a novel scanner, designed exclusively for phased array front-end characterization, is introduced

Direction finding with partly calibrated uniform linear arrays

A new method for direction finding with partly calibrated uniform linear arrays (ULAs) is presented. It is based on the conventional estimation of signal parameters via rotational invariance techniques (ESPRIT) by modeling the imperfections of the ULAs as gain and phase uncertainties. For a fully calibrated array, it reduces to the conventional ESPRIT algorithm. Moreover, the direction-of-arrivals (DOAs), unknown gains, and phases of the uncalibrated sensors can be estimated in closed form without performing a spectral search. Hence, it is computationally very attractive. The Cramér-Rao bounds (CRBs) of the partly calibrated ULAs are also given. Simulation results show that the root mean squared error (RMSE) performance of the proposed algorithm is better than the conventional methods when the number of uncalibrated sensors is large. It also achieves satisfactory performance even at low signal-to-noise ratios (SNRs). © 2011 IEEE.published_or_final_versio