Stochastic framework for evaluating the effect of displaced antenna elements on DOA estimation

We establish a statistical framework for investigating the influence of correlated random displacements of antenna elements in a uniform circular antenna array (UCA) on the distribution of direction-of-arrival (DOA) estimates. More specifically, we apply a stochastic collocation method formodeling the sparse UCA root-MUSIC-DOA estimates as polynomial expansions of the random displacements. Compared to Monte-Carlo simulations, this approach yields a speedup of about 40 for the case of a displacement of two antenna elements

Mutual Coupling in Phased Arrays: A Review

The mutual coupling between antenna elements affects the antenna parameters like terminal impedances, reflection coefficients and hence the antenna array performance in terms of radiation characteristics, output signal-to-interference noise ratio (SINR), and radar cross section (RCS). This coupling effect is also known to directly or indirectly influence the steady state and transient response, the resolution capability, interference rejection, and direction-of-arrival (DOA) estimation competence of the array. Researchers have proposed several techniques and designs for optimal performance of phased array in a given signal environment, counteracting the coupling effect. This paper presents a comprehensive review of the methods that model and mitigate the mutual coupling effect for different types of arrays. The parameters that get affected due to the presence of coupling thereby degrading the array performance are discussed. The techniques for optimization of the antenna characteristics in the presence of coupling are also included

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

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

Sparse Active Rectangular Array with Few Closely Spaced Elements

Sparse sensor arrays offer a cost effective alternative to uniform arrays. By utilizing the co-array, a sparse array can match the performance of a filled array, despite having significantly fewer sensors. However, even sparse arrays can have many closely spaced elements, which may deteriorate the array performance in the presence of mutual coupling. This paper proposes a novel sparse planar array configuration with few unit inter-element spacings. This Concentric Rectangular Array (CRA) is designed for active sensing tasks, such as microwave or ultra-sound imaging, in which the same elements are used for both transmission and reception. The properties of the CRA are compared to two well-known sparse geometries: the Boundary Array and the Minimum-Redundancy Array (MRA). Numerical searches reveal that the CRA is the MRA with the fewest unit element displacements for certain array dimensions.Comment: 4+1 pages, 5 figures, 1 tabl

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

A Study on MIMO Wireless Communication Channel Performance in Correlated Channels

MIMO wireless communication system is gaining popularity by days due to its versatility and wide applicability. When signal travels through wireless link it gets affected due to the disturbances present in the channel i.e. different sorts of interference and noise. Plus because there may or may not be a Line of sight (LOS) path between transmitter and receiver signal copies leaving the transmitter at the same time reaches the receiver with different delays and attenuation due to multiple reflections and interfere with each other at the receiver. Therefore fading of received signal power is also observed in case of a wireless MIMO link. In case of wireless two most important objectives can be channel estimation and signal detection. The importance of the wireless channel estimation can be attributed to faithful signal detection and transmit beam forming, power allocation etc. when Channel state information (CSI) is communicated to the transmitter via feedback loop in case of uni-directional channel or by simultaneous estimation by the transmitter itself in case of bi-directional channel. This text introduces some aspects of signal detection and mostly different aspects of channel estimation and explains why it is important in context of signal detection, beam forming etc. A brief introduction to antenna arrays and beam forming procedures have been given here. The cause of occurrence of spatial and temporal correlations have been discussed and different ways of modelling the spatial and temporal correlations involved are also briefly introduced in this text. How different link and link-end properties e.g. antenna spacing, angular spread of radiation beam, mean angle of radiation, mutual coupling present between elements of an antenna array etc. affects the channel correlations thereby affecting the performance of the MIMO wireless communication channel. Modelling of antenna mutual coupling and different estimation and compensation techniques are also discussed here

Evaluation and compensation of mutual coupling and other non-idealities in small antenna arrays

Smart antenna technology is a challenging area in the development of wireless communications. Using smart antennas the quality of a radio link can be improved by many ways. Smart antennas are active antenna arrays or groups with changeable complex-valued weights at inputs and outputs. Good electrical matching of the array and the similarity and ideality of element patterns is usually expected. This dissertation focuses on the problems in the smart antenna arrays caused mainly by mutual coupling. Mutual coupling causes reflected power in the feeding system, input/output signal correlation and corruption of the element patterns. The arrays used in this thesis are small microstrip arrays. The used frequency is about 5.3 GHz. For several arrays the element patterns and scattering matrices are measured and used in calculations and measurements. Also simulated patterns and scattering matrices are used. Due to mutual coupling the element patterns in an array are usually corrupted and therefore pattern correction should be used in smart antennas to improve the use of adaptive algorithms. In linear pattern correction the element patterns are reshaped using all antenna elements in the array. It is a computational method using a correction matrix between true and idealized inputs/outputs of array branches. For this pattern correction two basically different methods are used. The least squares error method can be used to find the correction matrix if the actual element patterns and the wanted element patterns are known, whereas in the scattering matrix method the correction matrix is defined only with the scattering matrix. These methods are compared in this thesis and the least squares error method is found to result in clearly better array patterns. The disadvantage of the scattering matrix method is that it does not compensate ground plate diffraction. However, the scattering matrix is easier to obtain than the element patterns and its use can give better understanding of the coupling mechanisms and therefore help the antenna design. Thus its use in pattern correction is examined more accurately. An extension of the least squares pattern correction method is done by correcting the array to a virtual array with different element spacing. The results show, that the element spacing in the virtual array should not differ significantly from the spacing in the real array. In addition to the pattern correction with a correction matrix the use of the real patterns for beamforming is examined. In a modified least squares method for beamforming the weighting (cost function) is used. The beamforming with and without robust weighting is compared on the relative scale and the use of weighting give better results. When antenna elements in an array are placed closer to each other, mutual coupling increases. At the same time the correlation between received signals increases. However, the signal correlation is usually caused by the signal propagation, and the effect of mutual coupling is minor. But, when signals arrive from many different directions, the pattern correlation caused by mutual coupling gives a realistic estimate of the signal correlation. The pattern correlation is a pure array characteristic and can be found easily. In this thesis the connection between pattern correlation and mutual coupling is examined. Equations are derived for this connection using scattering parameters or reflected power. These equations allow estimate mutual coupling from pattern correlation and vice versa, which is important for antenna array development. A more detailed formulation of the connection is done for lossless two-element arrays. In practice, when there are losses in the array, mutual coupling is not necessarily usable in estimation of pattern correlation.reviewe