Reza Monir Vaghefi

Asynchronous time-of-arrival-based source localization

In this paper, asynchronous wireless source localization using time-of-arrival (TOA) measurements is studied. In TOA localization, the travel time of the signal between the source node and anchor nodes is measured and used to estimate range. In synchronous networks, the anchor nodes know when the source node starts transmission. In asynchronous networks, however, the source transmit time is unknown and TOA measurements have a positive bias due to the synchronization error which could lead to a large localization error. One way to tackle this problem is to use time-difference-of-arrival (TDOA) measurements which do not depend on the source transmission time. However, in this work, applying an alternative approach, we estimate the source transmit time as a nuisance parameter jointly with the source location. The optimal maximum likelihood (ML) estimator is derived. To avoid the ML convergence problem, a novel semidefinite programming (SDP) technique is proposed by converting the noncovex ML problem into a convex one. Computer simulations showing superior performance of the proposed SDP estimator are conducted.

Improving positioning in LTE through collaboration

This paper represents a preliminary study of cooperative positioning in Long Term Evolution (LTE) systems. Many applications, such as location-based services and Enhanced 911 (E911), require that the locations of users in a cellular system are available. The global navigation satellite system (GNSS) is the most accessible positioning systems which are widely used in cellphones. However, poor operation in indoor and dense environments leads us to use cellular localization as a backup solution. In cellular localization, the locations of users are determined via measurements obtained within the network without aid of any external sources (e.g., GNSS). Observed time difference of arrival (OTDOA) is a positioning technique introduced in Release 9 of the 3GPP LTE specification. In OTDOA technique, the User Equipment (UE) measures the time difference of signals between several eNodeBs (base stations in LTE) and uses a trilateration algorithm to find its location. In the current 3GPP LTE specification, the UE can only collect measurements from eNodeBs. Therefore, in many situations, the UE is not able to communicate to a sufficient number of eNodeBs and cannot find its location without ambiguity. In this paper, we propose a cooperative localization technique for LTE systems in which the UE communicates not only with eNodeBs but also with other UEs. It will be shown that cooperative localization can significantly improve the localizability in the network, meaning that more UEs can be localized. Cooperative localization also enhances the accuracy which is highly beneficial for some applications, especially E911. A series of computer simulations are conducted to show the benefits of cooperative localization where the 3GPP simulation parameters are assumed.

Cooperative sensor localization with NLOS mitigation using semidefinite programming

Cooperative time-of-arrival-based sensor localization in a non-line-of-sight (NLOS) environment is investigated. Cooperative sensor localization plays an important role in indoor networks where GPS is limited. However, indoor networks suffer from NLOS propagation which degrades localization accuracy significantly. In this paper, we assume that the estimator is able to discriminate NLOS connections from line-of-sight (LOS) connections. We introduce a novel semidefinite programming (SDP) approach for cooperative localization which exploits NLOS connections to enhance the accuracy of localization. The performance of the proposed algorithm is compared with that of the maximum likelihood estimator and previously considered algorithms. Computer simulations show the excellent performance of the proposed SDP approach.

NLOS mitigation in TOA-based localization using semidefinite programming

In this work, time-of-arrival (TOA)-based wireless sensor localization in non-line-of-sight (NLOS) environments is investigated. In such environments, the accuracy of localization techniques is significantly degraded. While previous work often assumes some knowledge of the NLOS environment, we assume that the estimator knows neither which connections are NLOS nor the distribution of the NLOS errors. It is shown that the maximum likelihood estimator using only LOS connections provides a lower bound on the estimation accuracy. Furthermore, a novel NLOS mitigation technique based on semidefinite programming (SDP) is proposed. The proposed SDP technique estimates the source location jointly with the NLOS biases. The performance of the proposed estimator is compared with the aforementioned lower bound and with previous algorithms through computer simulations. Simulation results show that the proposed SDP estimator outperforms the other algorithms substantially, especially in severe NLOS environments.

Improving GPS-based vehicle positioning for Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) have emerged to utilize different technologies to enhance the performance and quality of transportation networks. Many applications of ITS need to have a highly accurate location information from the vehicles in a network. The Global Positioning System (GPS) is the most common and accessible technique for vehicle localization. However, conventional localization techniques which mostly rely on GPS technology are not able to provide reliable positioning accuracy in all situations. This paper presents an integrated localization algorithm that exploits all possible data from different resources including GPS, radio-frequency identification, vehicle-to-vehicle and vehicle-to-infrastructure communications, and dead reckoning. A localization algorithm is also introduced which only utilizes those resources that are most useful when several resources are available. A close-to-real-world scenario has been developed to evaluate the performance of the proposed algorithms under different situations. Simulation results show that using the proposed algorithms the vehicles can improve localization accuracy significantly in situations when GPS is weak.

Cooperative Joint Synchronization and Localization in Wireless Sensor Networks

In this paper, cooperative sensor localization using asynchronous time-of-arrival measurements is investigated. It is well known that localization performance in wireless networks using time-based ranging or pseudo-ranging methods is greatly affected by the accuracy of the timing synchronization between the nodes involved in the estimation. Commonly, the original estimation problem is broken down into two subproblems, the synchronization problem and the localization problem, in what is known as a two-step approach. However, in this paper, the joint synchronization and localization problem is considered and examined for use in cooperative networks. It is discussed that the cooperation between the source nodes eliminates the need for high anchor node densities and improves localization performance significantly. Furthermore, the Cramér-Rao lower bounds (CRLB) and the maximum likelihood (ML) estimator are derived. It is shown that the ML estimator is highly nonlinear and nonconvex and must, therefore, be solved by using computationally complex algorithms. In order to reduce the complexity of the estimation, a novel semidefinite programming (SDP) relaxation method is developed by relaxing the original nonconvex ML problem, in such a way as to reformulate the estimation problem as a convex problem. The performance of the proposed SDP method is shown through computer simulations to nearly equal that of the ML estimator. The approach is also applied to the noncooperative case where it is found to be superior in performance than the previously proposed suboptimal estimators. Finally, complexity analyses are included for the estimators under consideration.

Received signal strength-based sensor localization in spatially correlated shadowing

Wireless sensor localization using received signal strength (RSS) measurements is investigated in this paper. Most studies for RSS localization assume that the shadowing components are uncorrelated. However in this paper, we assume that the shadowing is spatially correlated. Under this condition, it can be shown that the localization accuracy can be improved if the correlation among links is taken into consideration. Avoiding the maximum likelihood (ML) convergence problem, we derive a novel semidefinite programming (SDP) approach by converting the corresponding noncovex ML estimator into a convex one. The performance of the proposed SDP estimator is compared with the ML estimator and previously considered estimators. Computer simulations show that the proposed SDP estimator outperforms the previously considered estimators in both uncorrelated and correlated shadowing environments.

Target Tracking in NLOS Environments Using Semidefinite Programming

In this paper, the problem of target tracking in non-line-of-sight (NLOS) environments is investigated. Target tracking has many commercial, civilian, and military applications. The accuracy of target tracking is highly affected in indoor environments where the majority of connections are NLOS. A novel tracking estimator based on semidefinite programming (SDP) with ability to mitigate the NLOS propagation is derived. Requiring no statistical information about the NLOS propagation, the proposed SDP algorithm estimates the NLOS biases jointly with the location and velocity of the target. The performance of the proposed estimator is evaluated through computer simulations where ray tracing is used to simulate the NLOS biases. It will be shown that the proposed SDP estimator outperforms the classic extended Kalman filter as well as other recently proposed estimators in NLOS environments.

Joint TOA-based sensor synchronization and localization using semidefinite programming

In this paper, asynchronous sensor localization using time-of-arrival (TOA) measurements is studied. Accurate TOA-based localization requires perfect timing synchronization between the source and anchor nodes. In asynchronous networks, the anchor nodes are assumed to be synchronized, while the clock of the source node needs be synchronized with those of the anchor nodes. Although synchronization and localization are typically performed separately, in this work a joint synchronization and localization framework is considered, as it is expected to provide significant improvement over two-step approaches. The clock parameters (clock offset and skew) of the source node are estimated jointly with its location. The corresponding Cramér-Rao lower bound (CRLB) and the maximum likelihood (ML) estimator of the system model are derived. The ML estimator is highly nonlinear and nonconvex which must be solved with computationally complex algorithms. Alternatively, a novel semidefinite programming (SDP) estimator is introduced by relaxing the original ML minimization problem into a convex problem. Computer simulations show that the proposed SDP estimator outperforms other previously proposed estimators.

GPS-free cooperative mobile tracking with the application in vehicular networks

In this paper, the problem of mobile tracking in dense environments is studied. The Global Positioning System (GPS) is the most accessible positioning technique. However, GPS does not work properly in indoor and dense areas, as the receiver typically does not have access to a sufficient number of line-of-sight satellites. Therefore, localization in these networks can be alternatively done by using measurements collected within the network and without the aid of any external resources (e.g., GPS). The mobile tracking problem includes several static reference nodes whose locations are fixed and known, and many mobile nodes whose locations are unknown and needed to be determined. The problem of mobile tracking can be solved in two forms: centralized and distributed. A centralized algorithm can result in high complexity and latency, while a distributed algorithm might lead to large estimation errors. In this paper, a novel cooperative localization technique is introduced which is able to deliver a promising localization accuracy while maintain the latency and complexity as low as possible. The performance of the proposed algorithm is compared with those of other algorithms in terms of localization accuracy, latency, and required data communication through computer simulations. The simulation results show the effectiveness of the proposed algorithm in comparison with either centralized and distributed algorithms. An important application of this work is vehicle localization in dense environments where the vehicles do not have access to GPS satellites and must be localized by the elements within the network.