Efficient cumulant-based methods for joint angle and frequency estimation using spatia-temporal smoothing

Most non-Gaussian signals in wireless communication array systems contain temporal correlation under a high sampling rate, which can offer more accurate direction of arrival (DOA) and frequency estimates and a larger identifiability. However, in practice, the estimation performance may severely degrade in coloured noise environments. To tackle this issue, we propose real-valued joint angle and frequency estimation (JAFE) algorithms for non-Gaussian signals using fourth-order cumulants. By exploiting the temporal correlation embedded in signals, a series of augmented cumulant matrices is constructed. For independent signals, the DOA and frequency estimates can be obtained, respectively, by leveraging a dual rotational invariance property. For coherent signals, the dual rotational invariance is constructed to estimate the generalized steering vectors, which associates the coherent signals into different groups. Then, the coherent signals in each group can be resolved by performing the forward-backward spatial smoothing. The proposed schemes not only improve the estimation accuracy, but also resolve many more signals than sensors. Besides, it is computationally efficient since it performs the estimation by the polynomial rooting in the real number field. Simulation results demonstrate the superiorities of the proposed estimator to its state-of-the-art counterparts on identifiability, estimation accuracy and robustness, especially for coherent signals.Yuexian Wang, Ling Wang, Xin Yang, Jian Xie, Brian W.-H. Ng and Peng Zhan

Approximate maximum likelihood estimation of two closely spaced sources

The performance of the majority of high resolution algorithms designed for either spectral analysis or Direction-of-Arrival (DoA) estimation drastically degrade when the amplitude sources are highly correlated or when the number of available snapshots is very small and possibly less than the number of sources. Under such circumstances, only Maximum Likelihood (ML) or ML-based techniques can still be effective. The main drawback of such optimal solutions lies in their high computational load. In this paper we propose a computationally efficient approximate ML estimator, in the case of two closely spaced signals, that can be used even in the single snapshot case. Our approach relies on Taylor series expansion of the projection onto the signal subspace and can be implemented through 1-D Fourier transforms. Its effectiveness is illustrated in complicated scenarios with very low sample support and possibly correlated sources, where it is shown to outperform conventional estimators

Multipath and interference errors reduction in gps using antenna arrays

The Global Positioning System (GPS) is a worldwide satellite based positioning system that provides any user with tridimensional position, speed and time information. The measured pseudorange is affected by the multipath propagation, which probably is the major source of errors for high precision systems. After a presentation of the GPS and the basic techniques employed to perform pseudorange measurements, the influence of the multipath components on the pseudorange measurement is explained. Like every system the GPS is also exposed to the errors that can be caused by the interferences, and a lot of civil applications need robust receivers to interferences for reasons of safety. In this paper some signal array processing techniques for reducing the code measurement errors due to the multipath propagation and the interferences are presented. Firstly, a non-adaptive beamforming is used. Secondly, a variant of the MUSIC and the maximum likelihood estimator can be used to estimate the DOA of the reflections and the interferences, and then a weight vector that removes these signals is calculated. In the third place, a beamforming with temporal reference is presented; the reference is not the GPS signal itself, but the output of a matched filter to the code. An interesting feature of the proposed techniques is that they can be applied to an array of arbitrary geometry.Peer ReviewedPostprint (published version