Hybrid Localization Method Research Articles

Hybrid TOA/AOA localization for indoor multipath-assisted next-generation wireless networks

The next-generation wireless communication systems (NGWCs) necessitate integrating effective communication and localization techniques. However, the multipath badly affects both communication and localization, especially in indoor NGWCs. Therefore, we propose a novel hybrid localization method based on time and angle-based ranging estimation to address this issue. This method employs an improved matrix enhancement matrix pencil algorithm (IMEMP) to obtain the time of arrival (TOA) and corresponding angle of arrival (AOA) of each path. Two distinct localization methods are presented: First arrival path-based maximum likelihood (ML) and scatterer-assisted. Both methods are designed to mitigate the adverse effects of serious non-line-of-sight (NLOS) scenarios. The proposed scatterer-assisted method demonstrates a noteworthy 20% improvement in performance compared to scenarios where only the first path is considered, particularly in specific areas. These findings highlight the potential of the proposed approach to enhance localization accuracy in challenging wireless environments, offering valuable insights for the advancement of NGWCs.

GF-SLAM: A Novel Hybrid Localization Method Incorporating Global and Arc Features

GF-SLAM: A Novel Hybrid Localization Method Incorporating Global and Arc Features

ACGA: Adaptive Conjugate Gradient Algorithm for non-line-of-sight hybrid TDOA-AOA localization

This study proposes a novel hybrid localization method that combines the time difference of arrival (TDOA) and angle of arrival (AOA) measurements to achieve improved accuracy with two base stations. Unlike other subspace-based methods that require calculating the eigenbasis, our proposed method constructs a Krylov subspace basis using the residual vectors of the Conjugate Gradient (CG) algorithm, which reduces computational complexity. In our approach, the measurements obtained from AOA estimation serve as initial estimates in our Adaptive Conjugate Gradient Algorithm (ACGA). ACGA incorporates an innovative adaptation mechanism that utilizes the residual error for updating and refining the estimated target position. This adaptation strategy allows our algorithm to iteratively improve localization accuracy, especially in the presence of NLOS errors. To assess the performance of our method, we conduct exhaustive simulations, considering various NLOS error scenarios and comparing them against conventional methods and extensively developed algorithms. The results demonstrate that our proposed method surpasses existing techniques, achieving a more densely spaced estimated position of the target in noisy environments with only two base stations. Furthermore, our method approaches the Cramer Rao Lower Bound (CRLB), indicating its high accuracy and efficiency in estimating the target position in typical NLOS environments. The effectiveness and potential of the proposed hybrid TDOA-AOA localization method are evident from the superior performance demonstrated in our simulations. These findings highlight the practical significance of our approach for real-world applications where accurate target localization is crucial, such as wireless communication, radar systems, and autonomous navigation.

Underwater Transmitter Localization Based on TDOA and FDOA Considering the Unknown Time-Varying Emission Frequency

This article considers locating a noncooperative underwater transmitter utilizing multiple receivers, such that each receiver can measure the frequency difference of arrival (FDOA) as well as the time difference of arrival (TDOA) of the transmitter’s sound. This article considers the case where the unknown emission frequency of the transmitter changes as time goes. This article addresses hybrid TDOA-FDOA localization, under the assumption that the transmitter’s maximum speed is known in advance. To the best of our knowledge, this article is unique in tackling hybrid TDOA-FDOA localization, considering the case where the unknown emission frequency changes as time goes on. Under MATLAB simulations, this article shows that the proposed hybrid localization method is comparable to the ideal case, where the time-varying emission frequency is known in advance. Furthermore, we show that the proposed localization approach outperforms the case where the emission frequency is estimated as a wrong value.

A human hybrid tracking and localization method for mixed reality simulation of complex system

In order to obtain stable and high-precision motion tracking in mixed reality (MR) simulation of the complex system, an optical-inertial hybrid tracking method is proposed, which combines the accuracy of optical motion capture equipment and the robustness of inertial motion capture equipment, and can reduce the occlusion problem of entity scene on the premise of ensuring the localization accuracy. The method has been applied to the ergonomic evaluation of large civil aircraft development processes, and the effectiveness and usability of the method have been verified.

A Hybrid AOA and TDOA-Based Localization Method Using Only Two Stations

Target localization plays an important role in the application of radar, sonar, and wireless sensor networks. In order to improve the localization performance using only two stations, a hybrid localization method based on angle of arrival (AOA) and time difference of arrival (TDOA) measurements is proposed in this paper. Firstly, the optimization model for localization based on AOA and TDOA are built, respectively, in the sensor network. Secondly,the majorization-minimization (MM) method is employed to create surrogate functions for solving the multiple objective optimization problem. Next, the hybrid localization problem is solved by the projected gradient decent (PGD) method. Finally, the Cramer–Rao lower bound (CRLB) for the joint AOA and TDOA method is derived for the comparison. Simulations proved that the proposed method has improved localization performance using AOA and TDOA measurements from only two base stations.

Hybrid Localization Method based on MCL and NDT Scan Matching for Mobile Robot Navigation

Hybrid Localization Method based on MCL and NDT Scan Matching for Mobile Robot Navigation

TDOA and RSSD Based Hybrid Passive Source Localization with Unknown Transmit Power

ABSTRACT Passive source localization is an important issue as there are numerous applications including search and rescue and public safety. Hybrid passive source localization combining time difference of arrival (TDOA) and received signal strength difference (RSSD) is investigated in this paper. First, a TDOA and RSSD based linear weighted least-squares (WLS) estimator for passive source localization is presented. Second, the maximum likelihood (ML) estimator and Cramer-Rao lower bound (CRLB) for the hybrid localization scheme are derived as performance benchmarks. Numerical results are presented which demonstrate that the proposed hybrid localization method provides significantly better accuracy than using only the TDOA.

A Novel Hybrid Localization Method for Wireless Sensor Network

Abstract Wireless sensor network is a kind of brand-new information acquisition platform, which is realized by the introduction of self-organizing and auto-configuration mechanisms. Node localization technology represents a crucial component of wireless sensor network. In this paper, a localization method based on kernel principal component analysis and particle swarm optimization back propagation algorithm is carefully discussed. First of all, taking KPCA as the front-end system to extract the main components of the localization information, and then regarding the nonlinear principal components extracted from distance vectors as the input samples, and meanwhile taking the coordinates of vertices in addition to the region boundary as the output samples, the PSO-BP neural network is trained to achieve the localization model. Finally the localization of unknown nodes can be estimated. The simulation experiment result showed that the method has high ability of stability and precision, and meets the practical need of localization.

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.

An Indoor Hybrid Localization Approach Based on Signal Propagation Model and Fingerprinting

RFID-based location awareness is becoming the most important issue in many fields in recent years, such as ubiquitous computing, mobile computing. In indoor localization systems RSSI-based methods are usually used in office buildings. However, RSSI is susceptible to external influences, and performances unstably due to the environmental factors affecting signal propagation. In this paper, we propose a new hybrid localization method for tracking moving object using the two typical methods which are signal propagation model and fingerprinting. According to a threshold which is defined as an effective working distance of signal propagation model between target tag and RFID reader, we choose the different localization algorithm to estimate the location of moving object. The threshold is obtained by calculating the slop of signal attenuation curve. If the distance is within the effective reading range of RFID reader, we revise signal propagation model by maximum likelihood estimation and use it to calculate the object position by minimum cumulative error. Otherwise, the fingerprinting location method is used in the external area, and the particle filter is also used as the core algorithm. The experimental results show that our method not only reduces the computation complexity but also ensures the accuracy in large indoor area.

거리정보 기반 무선위치추정을 위한 혼합 폐쇄형 해

Several estimation methods used in the range measurement based wireless localization area have individual problems. These problems may not occur according to certain application areas. However, these problems may give rise to serious problems in particular applications. In this paper, three methods, ILS (Iterative Least Squares), DS (Direct Solution), and DSRM (Difference of Squared Range Measurements) methods are considered. Problems that can occur in these methods are defined and a simple hybrid solution is presented to solve them. The ILS method is the most frequently used method in wireless localization and has local minimum problems and a large computational burden compared with closed-form solutions. The DS method requires less processing time than the ILS method. However, a solution for this method may include a complex number caused by the relations between the location of reference nodes and range measurement errors. In the near-field region of the complex solution, large estimation errors occur. In the DSRM method, large measurement errors occur when the mobile node is far from the reference nodes due to the combination of range measurement error and range data. This creates the problem of large localization errors. In this paper, these problems are defined and a hybrid localization method is presented to avoid them by integrating the DS and DSRM methods. The defined problems are confirmed and the performance of the presented method is verified by a Monte-Carlo simulation.

Calculation Of Mobile Location Based On TOA/SS Measurements

Localization of mobile station (MS) has now gained considerable attention. Since the hybrid measurements can help to improve the positioning accuracy, several hybrid localization methods have been proposed in the literature. However, the high performance estimator with the closed-form solution and complete performance analysis for time-of-arrival/signal strength (TOA/SS) localization technique is still an opening issue. Two TOA/SS localization algorithms with the closed-form solutions are proposed for the cases with or without uncertainty in the positions of base stations. Furthermore, performance analysis for the TOA/SS localization technique is presented. Both the theoretical variances and Cramer-Rao lower bounds (CRLBs) are derived and the relationship between the cases with or without uncertainty is given. The paper also proves that the TOA/SS scheme has a lower CRLB than the TOA (or SS) scheme. Theoretical analysis and simulations show that the proposed method can reach its CRLB.

Nonlinear Filtering for Hybrid GPS/GSM Mobile Terminal Tracking

The Global Positioning System (GPS) has become one of the state-of-the-art location systems that offers reliable mobile terminal (MT) location estimates. However, there exist situations where GPS is not available, for example, when the MT is used indoors or when the MT is located close to high buildings. In these scenarios, a promising approach is to combine the GPS-measured values with measured values from the Global System for Mobile Communication (GSM), which is known as hybrid localization method. In this paper, three nonlinear filters, namely, an extended Kalman filter, a Rao-Blackwellized unscented Kalman filter, and a modified version of the recently proposed cubature Kalman filter, are proposed that combine pseudoranges from GPS with timing advance and received signal strengths from GSM. The three filters are compared with each other in terms of performance and computational complexity. Posterior Cramér-Rao lower bounds are evaluated in order to assess the theoretical performance. Furthermore, it is investigated how additional GPS reference time information available from GSM influences the performance of the hybrid localization method. Simulation and experimental results show that the proposed hybrid method outperforms the GSM method.

Towards image guided robotic surgery: multi-arm tracking through hybrid localization

Use of the robotic assisted surgery has been increasing in recent years, due both the continuous increase in the number of applications and the clinical benefits that surgical robots can provide. Currently robotic assisted surgery relies on endoscopic video for navigation, providing only surface visualization, thus limiting subsurface vision. To be able to visualize and identify subsurface information, techniques in image-guidance can be used. As part of designing an image guidance system, all arms of the robot need to be co-localized in a common coordinate system. In order to track multiple arms in a common coordinate space, intrinsic and extrinsic tracking methods can be used. First, the intrinsic tracking of the daVinci, specifically of the setup joints is analyzed. Because of the inadequacy of the setup joints for co-localization a hybrid tracking method is designed and implemented to mitigate the inaccuracy of the setup joints. Different both optical and magnetic tracking methods are examined for setup joint localization. The hybrid localization method improved the localization accuracy of the setup joints. The inter-arm accuracy in hybrid localization was improved to 3.02 mm. This inter-arm error value was shown to be further reduced when the arms are co-registered, thus reducing common error.