Partial Relaxation Approach: An Eigenvalue-Based DOA Estimator Framework

In this paper, the partial relaxation approach is introduced and applied to DOA estimation using spectral search. Unlike existing methods like Capon or MUSIC which can be considered as single source approximations of multi-source estimation criteria, the proposed approach accounts for the existence of multiple sources. At each considered direction, the manifold structure of the remaining interfering signals impinging on the sensor array is relaxed, which results in closed form estimates for the interference parameters. The conventional multidimensional optimization problem reduces, thanks to this relaxation, to a simple spectral search. Following this principle, we propose estimators based on the Deterministic Maximum Likelihood, Weighted Subspace Fitting and covariance fitting methods. To calculate the pseudo-spectra efficiently, an iterative rooting scheme based on the rational function approximation is applied to the partial relaxation methods. Simulation results show that the performance of the proposed estimators is superior to the conventional methods especially in the case of low Signal-to-Noise-Ratio and low number of snapshots, irrespectively of any specific structure of the sensor array while maintaining a comparable computational cost as MUSIC.Comment: This work has been submitted to IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

The influence of random element displacement on DOA estimates obtained with (Khatri-Rao-)root-MUSIC

Although a wide range of direction of arrival (DOA) estimation algorithms has been described for a diverse range of array configurations, no specific stochastic analysis framework has been established to assess the probability density function of the error on DOA estimates due to random errors in the array geometry. Therefore, we propose a stochastic collocation method that relies on a generalized polynomial chaos expansion to connect the statistical distribution of random position errors to the resulting distribution of the DOA estimates. We apply this technique to the conventional root-MUSIC and the Khatri-Rao-root-MUSIC methods. According to Monte-Carlo simulations, this novel approach yields a speedup by a factor of more than 100 in terms of CPU-time for a one-dimensional case and by a factor of 56 for a two-dimensional case

Design of a Novel Antenna Array Beamformer Using Neural Networks Trained by Modified Adaptive Dispersion Invasive Weed Optimization Based Data

A new antenna array beamformer based on neural networks (NNs) is presented. The NN training is performed by using optimized data sets extracted by a novel Invasive Weed Optimization (IWO) variant called Modified Adaptive Dispersion IWO (MADIWO). The trained NN is utilized as an adaptive beamformer that makes a uniform linear antenna array steer the main lobe towards a desired signal, place respective nulls towards several interference signals and suppress the side lobe level (SLL). Initially, the NN structure is selected by training several NNs of various structures using MADIWO based data and by making a comparison among the NNs in terms of training performance. The selected NN structure is then used to construct an adaptive beamformer, which is compared to MADIWO based and ADIWO based beamformers, regarding the SLL as well as the ability to properly steer the main lobe and the nulls. The comparison is made considering several sets of random cases with different numbers of interference signals and different power levels of additive zero-mean Gaussian noise. The comparative results exhibit the advantages of the proposed beamformer

Angular CMA: A modified Constant Modulus Algorithm providing steering angle updates

Conventional blind beamforming algorithms have no direct notion of the physical Direction of Arrival angle of an impinging signal. These blind adaptive algorithms operate by adjusting the complex steering vector in the case of changing signal conditions and directions. This paper presents Angular CMA, a blind beamforming method that calculates steering angle updates (instead of weight vector updates) to keep track of the desired signal. Angular CMA and its respective steering angle updates are particularly useful in the context of mixed-signal hierarchical arrays as means to find and distribute steering parameters. Simulations of Angular CMA show promising convergence behaviour, while having a lower complexity than alternative methods (e.g., MUSIC)

Performance Investigation on Scan-On-Receive and Adaptive Digital Beam-Forming for High-Resolution Wide-Swath Synthetic Aperture Radar

The work investigates the performance of the Smart Multi-Aperture Radar Technique (SMART) Synthetic Aperture Radar (SAR) system for high-resolution wide-swath imaging based on Scan-on-Receive (SCORE) algorithm for receive beam steering. SCORE algorithm works under model mismatch conditions in presence of topographic height. A study on the potentiality of an adaptive approach for receive beam steering based on spatial spectral estimation is presented. The impact of topographic height on SCORE performance in different operational scenarios is examined, with reference to a realistic SAR system. The SCORE performance is compared to that of the adaptive approach by using the Cramèr Rao lower bound analysis

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