Performance Of Localization Methods Research Articles

Grid-free algorithms for direction-of-arrival trajectory localization.

Direction-of-arrival (DOA) estimation algorithms are crucial in localizing acoustic sources. Traditional localization methods rely on block-level processing to extract the directional information from multiple measurements processed together. However, these methods assume that DOA remains constant throughout the block, which may not be true in practical scenarios. Also, the performance of localization methods is limited when the true parameters do not lie on the parameter search grid. In this paper, two trajectory models are proposed, namely the polynomial and harmonic trajectory models, to capture the DOA dynamics. To estimate trajectory parameters, two gridless algorithms are adopted: (i) Sliding Frank-Wolfe (SFW), which solves the Beurling LASSO problem, and (ii) Newtonized orthogonal matching pursuit (NOMP), which is improved over orthogonal matching pursuit (OMP) using cyclic refinement. Furthermore, our analysis is extended to include multi-frequency processing. The proposed models and algorithms are validated using both simulated and real-world data. The results indicate that the proposed trajectory localization algorithms exhibit improved performance compared to grid-based methods in terms of resolution, robustness to noise, and computational efficiency.

Proposing a measurement criterion to evaluate the border problem in localization algorithms in WSNs

Localization algorithms are one of the most important protocols that may be used in many different fields of wireless sensor networks. The border problem is a considerable challenge for localization algorithms. In this regard, the sensor nodes that are placed on the boundary of a deployment area have a larger localization error as compared to the other sensor nodes. In this article, a new criterion is proposed for measuring the impact of the border problem on the performance of localization algorithms in isotropic networks. The performance of some range-free localization algorithms is studied by simulation. The results show that, contrary to LSVM and NN algorithms, the impacts of the border problem are reduced by a reduction in the dimensions of the deployment environment in the DV-hop algorithm. Besides, the effect of the border problem on the performance of the localization algorithms can be reduced by an increase in the number of anchor nodes. On the other hand, the number of sensor nodes does not have a significant impact on reducing the effect of the border problem in localization algorithms. Finally, a solution is proposed to reduce the negative impact of this issue on the performance of localization methods.

A Novel Localization Algorithm Based on Isomap and Partial Least Squares for Wireless Sensor Networks

Node localization is a main application of wireless sensor networks. However, the measurement error and noise in a real environment make the positioning of information nonlinear and have a great effect on the performance of localization methods. In this paper, we propose a novel isometric mapping (Isomap) node localization algorithm based on partial least squares (PLS-Isomap). For topological stability, the critical outlier points are eliminated by comparing the contribution rate of all data points. Then, we employ the PLS method to solve the Isomap. The adoption of PLS reduces the noise sensitivity of Isomap, which achieves solution by least squares. Moreover, the proposed approach applies a projection method to construct a new kernel matrix between new and original data points. Compared with Isomap and the multidimensional scale method, experimental and simulation results indicate that the PLS-Isomap algorithm has good topological stability, robustness, positioning accuracy, and lower computational complexity.

Evaluation and error analysis: Kalman gain regularization versus covariance regularization

Ensemble size is critical to the efficiency and performance of the ensemble Kalman filter, but when the ensemble size is small, the Kalman gain generally cannot be well estimated. To reduce the negative effect of spurious correlations, a regularization process applied on either the covariance or the Kalman gain seems to be necessary. In this paper, we evaluate and compare the estimation errors when two regularization methods including the distance-dependent localization and the bootstrap-based screening are applied on the covariance and on the Kalman gain. The investigations were carried out through two examples: 1D linear problem without dynamics but for which the true Kalman gain can be computed and a 2D highly nonlinear reservoir fluid flow problem. The investigation resulted in three primary conclusions. First, if localizations of two covariance matrices are not consistent, the estimate of the Kalman gain will generally be poor at the observation location. The consistency condition can be difficult to apply for nonlocal observations. Second, the estimate of the Kalman gain that results from covariance regularization is generally subject to greater errors than the estimate of the Kalman gain that results from Kalman gain regularization. Third, in terms of removing spurious correlations in the estimation of spatially correlated variables, the performance of screening Kalman gain is comparable as the performance of localization methods (applied on either covariance or Kalman gain), but screening Kalman gain outperforms the localization methods in terms of generality for application, as the screening method can be used for estimating both spatially correlated and uncorrelated variables, and moreover, no assumption about the prior covariance is required for the screening method.

Study and performance of localization methods in IP based networks: Vivaldi algorithm

This paper deals with station localization in distributed applications in the Internet. Several localization techniques are described with their advantages and disadvantages. This paper is focused on the sophisticated Vivaldi algorithm and its variations. A special simulation tool was developed in order to simulate the influence of configuration parameters and setting to algorithm performance. Several tests were performed to examine how both convergence and accuracy of localization process are affected by different settings of algorithm constants and by the number of reference points.