Inspired by the process of migration and reproduction of flora, this paper proposes a novel artificial flora (AF) algorithm. This algorithm can be used to solve some complex, non-linear, discrete optimization problems. Although a plant cannot move, it can spread seeds within a certain range to let offspring to find the most suitable environment. The stochastic process is easy to copy, and the spreading space is vast; therefore, it is suitable for applying in intelligent optimization algorithm. First, the algorithm randomly generates the original plant, including its position and the propagation distance. Then, the position and the propagation distance of the original plant as parameters are substituted in the propagation function to generate offspring plants. Finally, the optimal offspring is selected as a new original plant through the selection function. The previous original plant becomes the former plant. The iteration continues until we find out optimal solution. In this paper, six classical evaluation functions are used as the benchmark functions. The simulation results show that proposed algorithm has high accuracy and stability compared with the classical particle swarm optimization and artificial bee colony algorithm.

Due to the localization accuracy of global positioning system (GPS) cannot meet the requirements in indoor environment, the wireless sensor network (WSN) techniques are efficient methods to cope with this problem. The WSN based indoor localization techniques have become effective methods to solve the problem of indoor localization. Since the non-line-of sight (NLOS) effect could severely induce the localization accuracy, the primary challenge in indoor localization is the handling of NLOS errors. Due to multipath effect, near-far effect, obstacle occlusion, etc., the NLOS errors become very sophisticated. Aiming at this problem, a modified joint probabilistic data association localization (MJPDA) algorithm is proposed in this paper. Firstly, MJPDA obtains a series of pre-processing virtual points by grouping the measurements. Then the measurements are divided into two categories (LOS and NLOS) by virtual points' density. In the case of LOS, Extended Kalman filter (EKF) is used for processing. For the NLOS case, a series of particles are firstly generated around the prediction point, and then modified JPDA is used to data association of the virtual points and the particles. Simulations results illustrate that MJPDA is superior to MPDA algorithm and the traditional EKF algorithm in localization accuracy and robustness. Finally, we perform the real experiment to verify the performance of MJPDA. The experimental results demonstrate that MJPDA has prominent performance in mitigating large NLOS errors.

Wireless sensor network (WSN) is composed of many micro sensor nodes, and the localization technology is one of the most important applications of the WSN technology. At present, many positioning algorithms have high positioning accuracy in line-of-sight environment, but poor positioning accuracy in non-line-of-sight (NLOS) environment. In this article, we propose a modified generalized probability data association algorithm based on arrival of time. We divided the range measurements into N different groups, and each group obtained the corresponding position estimation, model probabilities, and covariance matrix of the mobile node through IMM-EKF. We used model probability and hypothesis test to perform NLOS identification for N groups, in which the model probability provided by each group was used for the first NLOS identification, and the innovation and innovation covariance matrix were used for the second NLOS identification in the hypothesis test. Position estimation contaminated by NLOS error is discarded. The correct position estimation is weighted with the corresponding association probability. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve higher localization accuracy when compared with the existing methods.

With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization is one of the most essential techniques for WSN. However, the NLOS propagation of WSN is largely influenced by many factors. Hence, a triple filters mixed Kalman Filter (KF) and Unscented Kalman Filter (UKF) voting algorithm based on Fuzzy-C-Means (FCM) and residual analysis (TF-FCM) has been proposed to cope with this problem. Firstly, an NLOS identification algorithm based on residual analysis is used to identify NLOS errors. Then, an NLOS correction algorithm based on voting and NLOS errors classification algorithm based on FCM are used to process the NLOS measurements. Hard NLOS measurements and soft NLOS measurements are classified by FCM classification. Secondly, KF and UKF are applied to filter two categories of NLOS measurements. Thirdly, maximum likelihood localization (ML) is employed to estimate the position of mobile nodes. The simulation result confirms that the accuracy and robustness of TF-FCM are better than IMM, UKF and KF. Finally, an experiment is conducted to test and verify our algorithm which obtains higher localization accuracy.

In recent years, the rapid development of microelectronics, wireless communications, and electro-mechanical systems has occurred. The wireless sensor network (WSN) has been widely used in many applications. The localization of a mobile node is one of the key technologies for WSN. Among the factors that would affect the accuracy of mobile localization, non-line of sight (NLOS) propagation caused by a complicated environment plays a vital role. In this paper, we present a hierarchical voting based mixed filter (HVMF) localization method for a mobile node in a mixed line of sight (LOS) and NLOS environment. We firstly propose a condition detection and distance correction algorithm based on hierarchical voting. Then, a mixed square root unscented Kalman filter (SRUKF) and a particle filter (PF) are used to filter the larger measurement error. Finally, the filtered results are subjected to convex optimization and the maximum likelihood estimation to estimate the position of the mobile node. The proposed method does not require prior information about the statistical properties of the NLOS errors and operates in a 2D scenario. It can be applied to time of arrival (TOA), time difference of arrival (TDOA), received signal (RSS), and other measurement methods. The simulation results show that the HVMF algorithm can efficiently reduce the effect of NLOS errors and can achieve higher localization accuracy than the Kalman filter and PF. The proposed algorithm is robust to the NLOS errors.

As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines the application prospects of WSNs to a great extent. The positioning errors of wireless sensor networks are mainly caused by the non-line of sight (NLOS) propagation, occurring in complicated channel environments such as the indoor conditions. Traditional techniques such as the extended Kalman filter (EKF) perform unsatisfactorily in the case of NLOS. In contrast, the robust extended Kalman filter (REKF) acquires accurate position estimates by applying the robust techniques to the EKF in NLOS environments while losing efficiency in LOS. Therefore it is very hard to achieve high performance with a single filter in both LOS and NLOS environments. In this paper, a localization method using a robust extended Kalman filter and track-quality-based (REKF-TQ) fusion algorithm is proposed to mitigate the effect of NLOS errors. Firstly, the EKF and REKF are used in parallel to obtain the location estimates of mobile nodes. After that, we regard the position estimates as observation vectors, which can be implemented to calculate the residuals in the Kalman filter (KF) process. Then two KFs with a new observation vector and equation are used to further filter the estimates, respectively. At last, the acquired position estimates are combined by the fusion algorithm based on the track quality to get the final position vector of mobile node, which will serve as the state vector of both KFs at the next time step. Simulation results illustrate that the TQ-REKF algorithm yields better positioning accuracy than the EKF and REKF in the NLOS environment. Moreover, the proposed algorithm achieves higher accuracy than interacting multiple model algorithm (IMM) with EKF and REKF.