In this paper, we address the problem of tracking the direction-of-arrival (DOA) of coherently distributed (CD) sources using subspace updating. The conventional multiple signal classification (MUSIC) algorithm, which should estimate covariance matrix, can not perform DOA tracking until it acquires covariance matrix. In addition, MUSIC algorithm needs spectrum peaks search and inverse matrix operator, so it can not estimate DOA of distributed sources rapidly. By contrast, the proposed algorithm uses subspace tracking to estimate signal subspace iteratively, then, the central angles are estimated using TLS-ESPRIT for CD sources. This algorithm can rapidly track DOA of CD sources even when their angular spreads are slightly wide.
Abshncf -In this paper, two complex nonlinear operators on a single snapshot data are used to estimate the parameters of a spatially distributed source. The nominal direction of arrival is estimated based on the space-invariant property of the modulus of one nonlinear operator. The angular spread is estimated by using the dillerenee between the modulus of the two nonlinear operators. Unlike the previous estimators based on multi-dimensional searching or their low-complexity versions, the proposed method gives the closed-form estimation using a single snapshot data and the calculation of either multi-dimensional searching or eigenvalue decomposition is not necessary.Kpyvordr: spatially distributed source; nominal direction of arrival; angular spread; nonlinear operator. I. TNTRODUCTIONIn general, a spatially distributed source can be described by stochastic model where a number of wavefronts impinge on the antenna array with random directions [i-31. The mean of these random directions is the nominal Direction of Arrival (DOA) and the standard deviation is the angular spread of the source.The latter is one of the important parameters in the design of efficient transmit and receive algorithms using this model [4]. Some optimal methods have been published for estimation of the two parameters. The disadvantage of all these algorithms is the computational complexity, as a heavy numerical search is necessary [S-7]. In [SI, the impact of local scattering on the estimates given by subspace-based algorithms is studied. Although the scattering model gives a full rank data covariance matrix, these algorithms can be used as is. It was shown that if the algorithm is used to locate two point sowces in the data from a scattered source, the two values will be located symmetrically around the nominal DOA and the separation between the values is a function of the angular spread. This can be used to calculate consistent estimates of both the nominal DOA and the spread angle. Though the resulting algorithm has significantly lower computational complexity than previously published algorithms, the associated eigenvalue decomposition is often very intensive, which may make these spread-subspace algorithms prohibitive. However, the results in [SI provide a framework for development of other methods with lower computational complexity.In this paper, we consider the problem of parameter estimation of a spatially distributed source using a single snapshot data from a uniform linear array (ULA). Two complex-valued nonlinear operators are used to reduce the computation. They are determinants of the data matrix and have the same form as Teager's energy operator [9]. The latter was fust introduced for measuring the real physical energy of a system. It was found that this nonlinear operator exhibits several attractive features such as simplicity, efficiency and ability to track instantaneously varying modulation patterns. Since its introduction, several applications have been derived for signal processing [IO]. Unlike the real-valued Teager's en...
As most components of sparse multi-path channel (SMPC) are zero, impulse response of SMPC can be recovered from a short training sequence. Although the ordinary orthogonal matching pursuit (OMP) algorithm provides a very fast implementation of SMPC estimation, it suffers from inter-atom interference (IAI), especially in the case of SMPC with a large delay spread and a short training sequence. In this paper, an adaptive IAI mitigation method is proposed to improve the performance of SMPC estimation based on a general OMP algorithm. Unlike the ordinary OMP algorithm, a sensing dictionary is designed adaptively and posterior information is utilized efficiently to prevent false atoms from being selected due to serious IAI. Numeral experiments illustrate that the proposed general OMP algorithm based on adaptive IAI mitigation outperforms both the ordinary OMP algorithm and the general OMP algorithm based on non-adaptive IAI mitigation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.