Multidimensional Scaling-Based TDOA Localization Scheme Using an Auxiliary Line

Jiang, Wuyang; Xu, Changqing; Pei, Ling; Wang, Yu

doi:10.1109/lsp.2016.2537371

Cited by 57 publications

(31 citation statements)

References 10 publications

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“…However, in distributed MDS a local cost function is minimized which does not depend on the global topology of the network and therefore can support mobility. A number of distributed MDS based localization have been Computation Complexity Topology Accuracy [17], [18], [163], [165]- [171] Centralized O(N 3 ) Uniform 1.2-2.21 m [17], [18], [163], [165], [167]- [171] Centralized O(N 3 ) Irregular 10-14.5 m [19], [20], [24], [172]- [184] Semi-centralized O(N k 3 ) Uniform 0.6-1.2 m [19], [20], [24], [172]- [184] Semi-centralized O(N k 3 ) Irregular 6.2-8.4 m [25]- [30], [186]- [198] Distributed O(N L) Uniform/Irregular 4.3-7 m developed to determine the location of a moving user/node using different tracking filters. For example, in [193] the authors used extended Kalman filter and unscented Kalman filter with MDS to track mobile sensors.…”

Section: Distributed Mds Based Localizationmentioning

confidence: 99%

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

Saeed

Nam

Al-Naffouri

et al. 2019

IEEE Commun. Surv. Tutorials

147

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Current and future wireless applications strongly rely on precise real-time localization. A number of applications such as smart cities, Internet of Things (IoT), medical services, automotive industry, underwater exploration, public safety, and military systems require reliable and accurate localization techniques. Generally, the most popular localization/ positioning system is the Global Positioning System (GPS). GPS works well for outdoor environments but fails in indoor and harsh environments. Therefore, a number of other wireless local localization techniques are developed based on terrestrial wireless networks, wireless sensor networks (WSNs) and wireless local area networks (WLANs). Also, there exist localization techniques which fuse two or more technologies to find out the location of the user, also called signal of opportunity based localization. Most of the localization techniques require ranging measurements such as time of arrival (ToA), time difference of arrival (TDoA), direction of arrival (DoA) and received signal strength (RSS). There are also range-free localization techniques which consider the proximity information and do not require the actual ranging measurements. Dimensionality reduction techniques are famous among the range free localization schemes. Multidimensional scaling (MDS) is one of the dimensionality reduction technique which has been used extensively in the recent past for wireless networks localization. In this paper, a comprehensive survey is presented for MDS and MDS based localization techniques in WSNs, Internet of Things (IoT), cognitive radio networks, and 5G networks. 2 MDS is much popular among all these techniques because of its simplicity and many application areas. MDS analysis finds the spatial map for objects given that the similarity or dissimilarity information between the objects is available [16].In the recent past, MDS is widely used for localization and mapping of wireless sensor networks (WSNs) and the internet of things (IoT). In [17] a proximity information based sensor network localization is proposed, where the main idea is to construct a local configuration of sensor nodes by using classical MDS (CMDS). The MDS based localization algorithms in [17] and [18] are centralized with higher computational complexity [7]. Semi-centralized (or clustered) MDS techniques are developed to compute local coordinates of nodes, which then are refined to find the final position of the nodes [19], [20]. In [21], [22] and [23] the authors proposed manifold learning to estimate the sensor nodes position in wireless sensor networks. In [24] the authors proposed Nystrom approximation for the proximity information matrix in MDS to reduce its size for better localization accuracy in sensor networks. Distributed MDS based localization algorithm is proposed in [25] with noisy range measurements, where the authors assume that the distances are corrupted with independent Gaussian random noise. MDS methods with different refinement schemes have also been proposed in the literature t...

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Section: Distributed Mds Based Localizationmentioning

confidence: 99%

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

Saeed

Nam

Al-Naffouri

et al. 2019

IEEE Commun. Surv. Tutorials

147

View full text Add to dashboard Cite

show abstract

“…radar, sonar, and wireless networks [1]. A series of works on TDOA localisation algorithms have been published [2][3][4][5]. Generally, the attainable mean-square errors (MSEs) of these algorithms are lower bounded by the Cramer-Rao bound (CRB).…”

Section: Introductionmentioning

confidence: 99%

Optimal and fast sensor geometry design method for TDOA localisation systemswith placement constraints

Zhang

Mao

Zhao

et al. 2019

IET signal process.

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The sensor geometry design problem of time difference of arrival (TDOA) localisation systems based on Cramer-Rao bound is studied. Sensor placement constraints are considered, which means the available placement area is limited. This makes sensor geometry design a sensor selection problem. In two-dimensional (2D) TDOA localisation or 3D TDOA localisation on the earth surface, sensor selection can be implemented through solving a fractional integer programming problem. However, traditional fractional integer programming methods are either suboptimal or too time consuming. For this reason, a new method named path-varying sphere decoding is proposed in two steps. In step one, the programming problem is relaxed into two sphere decoding (SD) subproblems. Solving these subproblems leads to the optimal solution, and the required computational complexity is much less than those of traditional optimal methods. In step two, the structure of the cost function is explored. This makes it possible to calculate the path-varying upper-bound of a quadratic function. Thus the quadratic function constraint used in one SD subproblem becomes tighter and the calculation speed is enhanced. Theory analyses and simulation results show that the proposed method is not only optimal but also much faster than traditional optimal methods when solving large-scale programming problems.

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“…6 TDOA method uses the time difference of reception of signals received by the various sensors and the network reference sensor, and this method has a significant advantage in the ease of implementation, being widely used in practice. Jiang et al 7 deals with source localization with TDOA measurements in two-dimensional (2D) scenarios. The Taylor series method 8 and Chan's method 9 are the basic TDOA localization methods.…”

Section: Introductionmentioning

confidence: 99%

A novel time difference of arrival localization algorithm using a neural network ensemble model

Zhang

Jiang

et al. 2018

International Journal of Distributed Sensor Networks

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In a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is proposed in non-line-of-sight environments. Back propagation neural network is a classic artificial neural network and may be effectively used for mathematical modeling and prediction, and an artificial neural network ensemble has better generalization ability and stability than a single network. First, the parameters, such as the weights and biases of the single neural network are optimized by the ant lion optimization method which is novel and effective. Then four types of different information from the time difference of arrival measurements are respectively used to train the individual neural network. Finally, the weighted average method is improved to combine the outputs of the different individual neural network, where weights are determined by the training errors. The estimation accuracy of the locating system is evaluated through experimental measurements. The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model.

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Multidimensional Scaling-Based TDOA Localization Scheme Using an Auxiliary Line

Cited by 57 publications

References 10 publications

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

Optimal and fast sensor geometry design method for TDOA localisation systemswith placement constraints

A novel time difference of arrival localization algorithm using a neural network ensemble model

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