9,098 research outputs found
Joint ML calibration and DOA estimation with separated arrays
This paper investigates parametric direction-of-arrival (DOA) estimation in a
particular context: i) each sensor is characterized by an unknown complex gain
and ii) the array consists of a collection of subarrays which are substantially
separated from each other leading to a structured noise covariance matrix. We
propose two iterative algorithms based on the maximum likelihood (ML)
estimation method adapted to the context of joint array calibration and DOA
estimation. Numerical simulations reveal that the two proposed schemes, the
iterative ML (IML) and the modified iterative ML (MIML) algorithms for joint
array calibration and DOA estimation, outperform the state of the art methods
and the MIML algorithm reaches the Cram\'er-Rao bound for a low number of
iterations
Source bearing and steering-vector estimation using partially calibrated arrays
The problem of source direction-of-arrival (DOA) estimation using a sensor array is addressed, where some of the sensors are perfectly calibrated, while others are uncalibrated. An algorithm is proposed for estimating the source directions in addition to the estimation of unknown array parameters such as sensor gains and phases, as a way of performing array self-calibration. The cost function is an extension of the maximum likelihood (ML) criteria that were originally developed for DOA estimation with a perfectly calibrated array. A particle swarm optimization (PSO) algorithm is used to explore the high-dimensional problem space and find the global minimum of the cost function. The design of the PSO is a combination of the problem-independent kernel and some newly introduced problem-specific features such as search space mapping, particle velocity control, and particle position clipping. This architecture plus properly selected parameters make the PSO highly flexible and reusable, while being sufficiently specific and effective in the current application. Simulation results demonstrate that the proposed technique may produce more accurate estimates of the source bearings and unknown array parameters in a cheaper way as compared with other popular methods, with the root-mean-squared error (RMSE) approaching and asymptotically attaining the Cramer Rao bound (CRB) even in unfavorable conditions
Antenna array calibration in wireless communications
Imperial Users onl
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
Modelling Aspects of Planar Multi-Mode Antennas for Direction-of-Arrival Estimation
Multi-mode antennas are an alternative to classical antenna arrays, and hence
a promising emerging sensor technology for a vast variety of applications in
the areas of array signal processing and digital communications. An unsolved
problem is to describe the radiation pattern of multi-mode antennas in closed
analytic form based on calibration measurements or on electromagnetic field
(EMF) simulation data. As a solution, we investigate two modeling methods: One
is based on the array interpolation technique (AIT), the other one on wavefield
modeling (WM). Both methods are able to accurately interpolate quantized EMF
data of a given multi-mode antenna, in our case a planar four-port antenna
developed for the 6-8.5 GHz range. Since the modeling methods inherently depend
on parameter sets, we investigate the influence of the parameter choice on the
accuracy of both models. Furthermore, we evaluate the impact of modeling errors
for coherent maximum-likelihood direction-of-arrival (DoA) estimation given
different model parameters. Numerical results are presented for a single
polarization component. Simulations reveal that the estimation bias introduced
by model errors is subject to the chosen model parameters. Finally, we provide
optimized sets of AIT and WM parameters for the multi-mode antenna under
investigation. With these parameter sets, EMF data samples can be reproduced in
interpolated form with high angular resolution
Asymptotically Optimal Blind Calibration of Uniform Linear Sensor Arrays for Narrowband Gaussian Signals
An asymptotically optimal blind calibration scheme of uniform linear arrays
for narrowband Gaussian signals is proposed. Rather than taking the direct
Maximum Likelihood (ML) approach for joint estimation of all the unknown model
parameters, which leads to a multi-dimensional optimization problem with no
closed-form solution, we revisit Paulraj and Kailath's (P-K's) classical
approach in exploiting the special (Toeplitz) structure of the observations'
covariance. However, we offer a substantial improvement over P-K's ordinary
Least Squares (LS) estimates by using asymptotic approximations in order to
obtain simple, non-iterative, (quasi-)linear Optimally-Weighted LS (OWLS)
estimates of the sensors gains and phases offsets with asymptotically optimal
weighting, based only on the empirical covariance matrix of the measurements.
Moreover, we prove that our resulting estimates are also asymptotically optimal
w.r.t. the raw data, and can therefore be deemed equivalent to the ML Estimates
(MLE), which are otherwise obtained by joint ML estimation of all the unknown
model parameters. After deriving computationally convenient expressions of the
respective Cram\'er-Rao lower bounds, we also show that our estimates offer
improved performance when applied to non-Gaussian signals (and/or noise) as
quasi-MLE in a similar setting. The optimal performance of our estimates is
demonstrated in simulation experiments, with a considerable improvement
(reaching an order of magnitude and more) in the resulting mean squared errors
w.r.t. P-K's ordinary LS estimates. We also demonstrate the improved accuracy
in a multiple-sources directions-of-arrivals estimation task.Comment: in IEEE Transactions on Signal Processin
- …