Emerging communication network applications including fifth-generation (5G) cellular and the Internet-of-Things (IoT) will almost certainly require location information at as many network nodes as possible. Given the energy requirements and lack of indoor coverage of Global Positioning System (GPS), collaborative localization appears to be a powerful tool for such networks. In this paper, we survey the state of the art in collaborative localization with an eye toward 5G cellular and IoT applications. In particular, we discuss theoretical limits, algorithms, and practical challenges associated with collaborative localization based on range-based as well as range-angle-based techniques.

The accuracy of localization is highly degraded in indoor and harsh environments where source nodes either do not have connections with a sufficient number of anchor nodes due to strong attenuation or have very poor range estimates due to NLOS propagation. Cooperative localization is a technique in which the source nodes communicate not only with the anchor nodes, but also with each other. Hence, the source nodes can collect several additional measurements which significantly improve the localization performance. Although many studies have examined NLOS-degraded localization of a static node in noncooperative networks, and many others have examined the impact of cooperation for static localization, there is no work which considers cooperative tracking of mobile nodes. To address this open problem, in this work, we examine cooperative tracking, particularly in NLOS environments. More specifically, we develop a novel sensor tracking algorithm based on semidefinite programming (SDP) which has the ability to mitigate NLOS propagation. Our simulations show that the new SDP-based tracking algorithm outperforms the classic extended Kalman filter as well as the other recently proposed algorithms for noncooperative tracking in NLOS environments. We also show that the algorithm can be extended to cooperative networks, and that a substantial performance benefit is realized by cooperation.

It is well-known that non-line-of-sight (NLOS) propagation degrades the accuracy of localization in indoor and harsh environments. In such environments, source nodes either do not have connections with a sufficient number of anchor nodes due to strong attenuation or have very poor range estimates due to NLOS propagation. Cooperative localization, in which a source node communicates with both anchor nodes and other source nodes, can benefit such scenarios by providing additional measurements that can significantly improve the localization performance. While most of the earlier works have studied static node localization in noncooperative networks, cooperative tracking of mobile nodes in NLOS environments is still an open problem. To address this open problem, in this work we develop an technique for cooperative tracking applicable to NLOS environments. More specifically, we develop a novel node tracking algorithm based on semidefinite programming (SDP) which has the ability to mitigate NLOS propagation. Our simulations show that the proposed SDP estimator outperforms the classic extended Kalman filter for cooperative networks in NLOS environments.

In this paper, joint sensor synchronization and localization using time-of-arrival measurements is studied. In wireless sensor networks, the accuracy of the clock synchronization among nodes has a great impact on the performance of the localization using time-based ranging methods. The clocks of the anchor nodes are typically synchronized with each other, while those of the source nodes must be synchronized with the anchor nodes. Each source node has its own clock characterized by clock offset and clock skew. Synchronization is the process of determining these clock parameters for the source node, while localization is the process of estimating its location. Generally, the estimation problem is broken down into two subproblems, where the synchronization is first performed and then the source node is localized. However, in this paper, a joint synchronization and localization framework is considered and examined, as it is expected to provide better accuracy, especially in dynamic networks. The system model for joint synchronization and localization is first introduced. The maximum likelihood (ML) estimator is then derived which is shown to be highly nonlinear and nonconvex. The ML estimator does not have a closed-form solution and must be solved by computationally complex and iterative algorithms. A novel linear estimator is derived which has a closed-form solution with significantly lower complexity. The performance of the proposed linear estimator is evaluated through computer simulations. Results show that the proposed linear estimator outperforms the previously considered estimators, especially in low signal-to-noise ratios.

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.

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.

In this paper, cooperative positioning for Long Term Evolution (LTE) cellular systems using Radio Frequency Pattern Matching (RFPM) is studied. Having the locations of users in a cellular system supports many applications such as location-based services and E911. Although Global Positioning System (GPS) can be found in every smartphone, its poor performance in indoor and dense environments encourages the development of cellular network-based solutions. Cellular localization has emerged in which the user locations can be determined by measurements obtained within the network without the aid of any external sources (e.g., GPS). Several positioning techniques have been considered in Release 9 of the 3rd Generation Partnership Project (3GPP) document such as Observed Time Difference Of Arrival (OTDOA) and Enhanced Cell ID (E-CID). In OTDOA, the User Equipment (UE) measures the time difference of signals from multiple eNodeBs and uses a trilateration algorithm to find its location. However, OTDOA performance can be highly degraded in rich multipath and non-line-of-sight environments particularly when there is strong interference. Hence, many companies and researchers are also evaluating other techniques such as RFPM in which the user location is estimated by comparing UE measurements with a series of reference data. RFPM is independent of line-of-sight conditions and has good performance in dense urban and indoor environments. In the current LTE networks, the UE can only communicate with eNodeBs. However, there are scenarios in which the UE is not able to communicate with a sufficient number of eNodeBs and cannot find its location without ambiguity. In this paper, a cooperative localization technique for LTE systems is proposed in which leveraging the Device-to-Device (D2D) communications protocol the UE communicates not only with eNodeBs but also with other UEs. It will be shown through computer simulations that the proposed positioning algorithm can significantly improve the positioning performance in LTE networks.

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.

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.

Received signal strength (RSS)-based single source localization when there is not a prior knowledge about the transmit power of the source is investigated. Because of nonconvex behavior of maximum likelihood (ML) estimator, convoluted computations are required to achieve its global minimum. Therefore, we propose a novel semidefinite programming (SDP) approach by approximating ML problem to a convex optimization problem which can be solved very efficiently. Computer simulations show that our proposed SDP has a remarkable performance very close to ML estimator. Linearizing RSS model, we also derive the partly novel least squares (LS) and weighted total least squares (WTLS) algorithms for this problem. Simulations illustrate that WTLS improves the performance of LS considerably.

A novel multilayer neural network model, called artificial synaptic network, was designed and implemented for single sensor localization with time-of-arrival (TOA) measurements. In the TOA localization problem, the location of a source sensor is estimated based on its distance from a number of anchor sensors. The measured distance values are noisy and the estimator should be able to handle different amounts of noise. Three neural network models: the proposed artificial synaptic network, a multi-layer perceptron network, and a generalized radial basis functions network were applied to the TOA localization problem. The performance of the models was compared with one another. The efficiency of the models was calculated based on the memory cost. The study result shows that the proposed artificial synaptic network has the lowest RMS error and highest efficiency. The robustness of the artificial synaptic network was compared with that of the least square (LS) method and the weighted least square (WLS) method. The Cramer-Rao lower bound (CRLB) of TOA localization was used as a benchmark. The model's robustness in high noise is better than the WLS method and remarkably close to the CRLB.