M. Z. Win et al., “Network Localization and Navigation via Cooperation,” IEEE Communications Magazine, vol. 49, no. 5, pp. 56–62, May 2011.
This magazine article introduces the new paradigm of network localization and navigation for communications and contextual data collection, enabling a variety of new applications that rely on position information of mobile nodes. It provides an exploration of cooperative network localization and navigation from a theoretical foundation to applications, covering technologies and spatiotemporal cooperative algorithms.

K. Pahlavan, X. Li, and J.-P. Makela, “Indoor Geolocation Science and Technology,” IEEE Communications Magazine, vol. 40, no. 2, pp. 112–118, February 2002.
This magazine article presents an overview of the technical aspects of the existing technologies for wireless indoor location systems. It provides a fundamental understanding of the issues related to indoor geolocation science that are needed for design and performance evaluation of indoor geolocation systems.

N. Patwari, J. N. Ash, S. Kyperountas, A. O. Hero, R. L. Moses, and N. S. Correal, “Locating the Nodes: Cooperative Localization in Wireless Sensor Networks,” IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 54–69, 1 July 2005.
This magazine article presents cooperative localization and provides measurement-based statistical models of TOA, AOA, and RSS. Design aspects are discussed and performance bounds are used to help choose among measurement methods, select neighbor- hood size, set minimum reference node densities, and compare localization algorithms.

S. Gezici, Z. Tian, G. B. Giannakis, H. Kobayashi, A. F. Molisch, H. V. Poor, and Z. Sahinoglu, “Localization via Ultra-Wideband Radios: A Look at Positioning Aspects for Future Sensor Networks,” IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 70–84, July 2005.
This magazine article discusses how ultra-wideband (UWB) signaling is suitable in this context because it allows centimeter accuracy in ranging. This has also been recognized by the IEEE, which has set up a new standardization group 802.15.4a for the creation of a new physical layer for low data rate communications combined with positioning capabilities.

K. Witrisal et al., “High-Accuracy Localization for Assisted Living: 5G Systems Will Turn Multipath Channels from Foe to Friend,” IEEE Signal Processing Magazine, vol. 33, no. 2, pp. 59–70, March 2016.
This magazine article discusses the potential of future high-accuracy localization systems as a key component of assisted living applications. It focuses on discussing radio localization methods that reduce the required infrastructure by exploiting information from reflected multipath components, and showing that knowledge about the propagation environment enables localization with high accuracy and robustness.

M. Z. Win, Y. Shen, and W. Dai, “A Theoretical Foundation of Network Localization and Navigation,” Proceedings of the IEEE, vol. 106, no. 7, pp. 1136–1165, July 2018.
This article presents a theoretical foundation of network localization and navigation, including a mathematical formulation, an introduction of equivalent Fisher information analysis, and determination of the fundamental limits of localization accuracy. It includes key ingredients such as spatiotemporal cooperation, array signal processing, and map exploitation, highlighting the connection between the theoretical foundation and algorithm development.

S. Safavi, U. A. Khan, S. Kar, and J. M. F. Moura, “Distributed Localization: A Linear Theory,” Proceedings of the IEEE, vol. 106, no. 7, pp. 1204–1223, July 2018.
This article discusses a linear distributed localization solution for a 5G-enabled IoT environment where many low power devices, users, or agents cooperate to locate themselves without a direct access to anchors. The linearization is obtained by reparametrization of the agent location through barycentric coordinates, and the convergence and robustness of the proposed algorithm are also quantified for both stationary and dynamic conditions.

M. Z. Win, W. Dai, Y. Shen, G. Chrisikos, and V. H. Poor, “Network Operation Strategies for Efficient Localization and Navigation,” Proceedings of the IEEE, vol. 106, no. 7, pp. 1224–1254, July 2018.
This article explores various network operation strategies for network localization and navigation, including several functionalities such as node prioritization, node activation, and node deployment. It not only demonstrates the performance gain of the proposed strategies, but also introduces several important concepts such as cooperative operation, robustness guarantee, and distributed design in the development of the network operation strategies.

M. F. Keskin, A. D. Sezer, and S. Gezici, “Localization via Visible Light Systems,” Proceedings of the IEEE, vol. 106, no. 6, pp. 1063–1088, June 2018.
This article proposes a cooperative visible light positioning system architecture, in which visible light communication receiver units are able to communicate with each other for the purpose of cooperation. A low-complexity, iterative localization algorithm is proposed with demonstrated benefits of cooperation. It also investigates optimal strategies for power allocation among LED transmitters to maximize the localization accuracy subject to power and illumination constraints.

M. Z. Win, F. Meyer, Z. Liu, W. Dai, S. Bartoletti, and A. Conti, “Efficient Multi-Sensor Localization or the Internet-of-Things,” IEEE Signal Processing Magazine, vol. 35, no. 5, pp. 153–167, September 2018.
This magazine article presents a framework for designing network localization and navigation for the Internet of Things (IoT). The proposed multi-sensor localization and operation algorithms are suitable for arbitrary, large network sizes, and only rely on an information exchange among neighboring devices. An evaluation in a large-scale IoT network with 500 agents shows its attractive localization performance and low communication overhead and energy consumption.

A. Conti, S. Mazuelas, S. Bartoletti, W. C. Lindsey, and M. Z. Win, “Soft Information for Localization-of-Things,” Proceedings of the IEEE, vol. 107, no. 11, pp. 2240–2264, November 2019.
This article first reviews classical localization techniques based on single-value metrics, such as range and angle estimates, or fixed measurement models, such as Gaussian distributions. Then, it presents a new localization approach based on soft information (SI) extracted from intra- and inter-node measurements, as well as from contextual data. In particular, efficient techniques for learning and fusing different kinds of SI are described. Case studies are presented for two scenarios in which sensing measurements are based on: 1) noisy features and non-line-of-sight detector outputs and 2) IEEE 802.15.4a standard. Results show that SI-based localization is highly efficient, can significantly outperform classical techniques, and provides robustness to harsh propagation conditions.

“Advanced Signal Processing for GNSS and Robust Navigation,” IEEE Journal of Selected Topics in Signal Processing, vol. 3, no. 5, October 2009.

“Indoor Tracking: Theory, Methods, and Technologies,” IEEE Transactions on Vehicular Technology, vol. 64, no. 4, April 2015.

“Location-Awareness for Radios and Networks, Part I,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 7, July 2015.

“Location-Awareness for Radios and Networks, Part II,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 11, November 2015.

“Foundations and Trends in Localization Technologies—Part I,” Proceedings of the IEEE, vol. 106, no. 6, June 2018.

“Foundations and Trends in Localization Technologies—Part II,” Proceedings of the IEEE, vol. 106, no. 7, July 2018.

Y. Shen and M. Z. Win, “Fundamental Limits of Wideband Localization – Part I: A General Framework,” IEEE Transactions on Information Theory, vol. 56, no. 10, pp. 4956–4980, October 2010.
This article develops a general framework to characterize the localization accuracy in non-cooperative location-aware networks. It introduces the notion of equivalent Fisher information to derive the performance and to provide insights into the essence of the localization problem. The analysis begins with the received waveforms themselves rather than utilizing only the signal metrics extracted from these waveforms, and the resulting bound exploits all the information inherent in the received waveforms and serves as a fundamental limit of localization accuracy.

Y. Shen, H. Wymeersch, and M. Z. Win, “Fundamental Limits of Wideband Localization – Part II: Cooperative Networks,” IEEE Transactions on Information Theory, vol. 56, no. 10, pp. 4981–5000, October 2010.
This article extends the Part I above to cooperative location-aware networks. The analysis is based on the waveforms received at the nodes, in conjunction with Fisher information inequality. A geometrical interpretation of localization information is proposed for cooperative networks. This approach allows to succinctly derive fundamental performance limits and their scaling behaviors, and to treat anchors and agents in a unified way from the perspective of localization accuracy.

Y. Qi, H. Kobayashi, and H. Suda, “Analysis of Wireless Geolocation in a Non-Line-Of-Sight Environment,” IEEE Transactions on Wireless Communications, vol. 5, no. 3, pp. 672–681, March 2006.
This article presents an analysis of the time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA) and signal strength (SS) based positioning methods in a non-line-of sight (NLOS) environment. Both line-of-sight (LOS) and NLOS propagation are studied, and the geolocation accuracy is evaluated in terms of the Cramer-Rao Lower Bound (CRLB).

Y. Shen, S. Mazuelas, and M. Z. Win, “Network Navigation: Theory and Interpretation,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 9, pp. 1823–1834, October 2012.
This article establishes a theoretical foundation for network navigation and determine the fundamental limits of navigation accuracy using equivalent Fisher information analysis. It then introduces the notion of carry-over information in time and provides a geometrical interpretation for the evolution of navigation information. It unifies the navigation information obtained from spatial and temporal cooperation, leading to a deep understanding of information evolution and cooperation benefits in navigation networks.

D. B. Jourdan, D. Dardari, and M. Z. Win, “Position Error Bound for UWB Localization in Dense Cluttered Environments,” IEEE Transactions on Aerospace and Electronic Systems, vol. 44, no. 2, pp. 613–628, April 2008.
This article derives a fundamental limit of localization accuracy for an ultra-wideband (UWB) system operating in harsh propagation environments. It shows that the relative importance of information coming from different beacons varies depending on the propagation conditions, such as whether the beacon is line-of-sight or non-line-of-sight (NLOS). In particular, any prior information knowledge on NLOS beacons can significantly improve the localization accuracy, especially in dense cluttered environments.

T. Ihler, J. W. Fisher III, R. L. Moses, and A. S. Willsky, “Non-Parametric Belief Propagation for Self-Localization of Sensor Networks,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 4, pp. 809–819, April 2005.
This article demonstrates that the information used for sensor localization is local with regard to the network topology and uses this observation to reformulate the problem within a graphical model framework. It also presents the utility of nonparametric belief propagation (NBP) for both estimating sensor locations and representing location uncertainties.

H. Wymeersch, J. Lien, and M. Z. Win, “Cooperative Localization in Wireless Networks,” Proceedings of the IEEE, vol. 97, no. 2, pp. 427–450, February 2009, special issue on Ultra-Wide Bandwidth (UWB) Technology & Emerging Applications.
This article gives an overview of cooperative localization approaches and applies them to ultrawide bandwidth (UWB) wireless networks. The performance of several cooperative localization algorithms is quantified using realistic UWB ranging models through an extensive measurement campaign using FCC-compliant UWB radios. A powerful fully distributed localization algorithm called SPAWN is proposed by mapping a graphical model for statistical inference onto the network topology, and by developing a suitable net-message passing schedule.

I. Dokmanic, R. Parhizkar, J. Ranieri, M. Vetterli, “Euclidean Distance Matrices: Essential Theory, Algorithms, and Applications,” IEEE Signal Processing Magazine, vol. 32, no. 6, pp. 12–30, November 2015.
This article reviews the fundamental properties of Euclidean distance matrices (EDMs), such as rank or (non)definiteness, and shows how the various EDM properties can be used to design algorithms for completing and denoising distance data. It also demonstrates applications to microphone position calibration, ultrasound tomography, room reconstruction from echoes, and phase retrieval.

R. M. Buehrer, H. Wymeersch, and R. M. Vaghefi, “Collaborative Sensor Network Localization: Algorithms and Practical Issues,” Proceedings of the IEEE, vol. 106, no. 6, pp. 1089–1114, June 2018.
This article surveys the state of the art in collaborative localization with an eye toward 5G cellular and Internet-of-Things (IoT) applications. In particular, it discusses theoretical limits, algorithms, and practical challenges associated with collaborative localization based on range-based as well as range-angle-based techniques. Specific emphasis was placed on distributed localization methods and the challenges of non-line-of-sight propagation.

U. A. Khan, S. Kar, and J. M. F. Moura, “Distributed Sensor Localization in Random Environments Using Minimal Number of Anchor Nodes,” IEEE Transactions on Signal Processing, vol. 57, no. 5, pp. 2000–2016, May 2009.
The paper introduces a distributed iterative algorithm to locate an arbitrary number of sensors with respect to a minimal number of anchors. The algorithm adopts the use of barycentric coordinates of a node with respect to its neighbors, together with a proven convergence guarantee. It also studies the random environments where non-ideal communication links and measurements occurs.

S. Mazuelas, Y. Shen, and M. Z. Win, “Spatiotemporal Information Coupling in Network Navigation,” IEEE Journal of Selected Topics in Signal Processing, vol. 64, no. 12, pp. 7759-7779, December 2018.
This paper presents a principled framework to characterize the information coupling present in network navigation. It derives the equivalent Fisher information matrix for individual agents as the sum of effective information from each neighbor and the coupled information induced by the neighbors’ interaction. The results of this paper can offer guidelines for the development of distributed techniques that adequately account for information coupling, and hence enable accurate and efficient network navigation.

R. M. Vaghefi, M. R. Gholami, R. M. Buehrer, E. G. Strom, “Cooperative Received Signal Strength-Based Sensor Localization with Unknown Transmit Powers,” IEEE Trans. on Signal Processing, vol. 61, no. 6, pp. 1389-1403, March 2013.
This article proposes a cooperative localization algorithm where the source transmit powers are considered as nuisance parameters rather than known ones. It develops a semidefinite programming (SDP) relaxation technique by converting the ML-based localization problem into a convex problem, together with complexity analyses.

O. Bialer, D. Raphaeli, and A. J. Weiss, “Maximum-Likelihood Direct Position Estimation in Dense Multipath,” IEEE Transactions on Vehicular Technology, vol. 62, no. 5, pp. 2069–2079, June 2013.
This article proposes a direct position estimation algorithm for a dense multipath environment. It demonstrates the robustness and accuracy of the proposed algorithm compared to conventional indirect methods, particularly when the SNR is low and when the multipath is dense.

C. Morelli, M. Nicoli, V. Rampa, U. Spagnolini, “Hidden Markov Models for Radio Localization in Mixed LOS/NLOS Conditions,” IEEE Transactions on Signal Processing, vol. 55, no. 4, pp. 1525–1542, April 2007.
This article investigates the problem of radio localization of moving terminals (MTs) for indoor applications with mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions. It proposes a grid-based Bayesian approach to jointly track the sequence of the positions and the sight conditions of the MT.

S. Aditya, A. F. Molisch, and H. M. Behairy, “A Survey on the Impact of Multipath on Wideband Time-of-Arrival Based Localization,” Proceedings of the IEEE, vol. 106, no. 7, pp. 1183–1203, July 2018.
This paper provides an overview of the techniques for analyzing and addressing the effects of multipath on localization accuracy. It casts the localization of one or more targets as a maximum a priori estimation problem, and shows that multipath can either be a blessing or a curse depending on the extent of prior knowledge available about the multipath statistics. Under certain conditions, the spatial diversity offered by multipath can be exploited for improving localization accuracy.

D. Dardari, A. Conti, U. J. Ferner, A. Giorgetti, and M. Z. Win, “Ranging with Ultrawide Bandwidth Signals in Multipath Environments,” Proceedings of the IEEE, vol. 97, no. 2, pp. 404–426, February 2009, special issue on Ultra-Wide Bandwidth (UWB) Technology & Emerging Applications.
This article gives an overview of UWB ranging techniques together with the primary sources of TOA error including propagation effects, clock drift, and interference. Fundamental TOA bounds, such as the Cramer–Rao bound and the tighter Ziv–Zakai bound, are described in both ideal and multipath environments, serving as useful benchmarks for the performance of TOA estimation techniques. Practical low-complexity TOA estimation techniques are explored and their performance is analyzed in the presence of multipath and interference using IEEE 802.15.4a channel models as well as experimental data measured in indoor residential environments.

A. Giorgetti, M. Chiani, “Time-of-Arrival Estimation Based on Information Theoretic Criteria,” IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 1869–1879, April 2013.
This paper proposes blind ToA estimation techniques based on model selection for UWB impulse radio systems. The new ToA algorithm, based on Information-theoretic criteria, eliminates the need to set a predefined threshold, typical of ToA estimation algorithms. In particular, the algorithm is capable to detect the noise-only bins and signal-plus-noise bins using model order selection methods, from which the ToA is immediately found. Model order selection-based algorithms exhibit excellent performance, comparable with genie-aided threshold crossing ToA estimation with perfect channel and noise power knowledge.

S. Mazuelas, A. Conti, J. C. Allen, and M. Z. Win, “Soft Range Information for Network Localization,” IEEE Transactions on Signal Processing, vol. 66, no. 12, pp. 3155–3168, June 2018.
This article develops localization techniques that rely on all probable range values rather than on a single estimate of each distance. It introduces the concept of soft range information (SRI), establishes a general framework for SRI-based localization, and develops algorithms for obtaining the SRI using machine learning techniques.

R. Niu, A. Vempaty, and P. K. Varshney, “Received Signal Strength Based Localization in Wireless Sensor Networks,” Proceedings of the IEEE, vol. 106, no. 6, pp. 1166–1182, June 2018.
This article presents an overview of recent developments in received-signal-strength (RSS)-based localization in wireless sensor networks. To save communication bandwidth and sensor energy, a maximum-likelihood estimator based on quantized data is presented along with its corresponding Cramer-Rao lower bound and optimal quantizer design schemes. Moreover, it proposes an iterative sensor selection approach for energy saving, and coding-theory-based approach that is robust to malicious attacks.

S. Bartoletti, W. Dai, A. Conti, and M. Z. Win, “A Mathematical Model for Wideband Ranging,” IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 2, pp. 216–228, March 2015.
This article proposes a tractable model for the range information as a function of wireless environment, signal features, and energy detection technique. Such a model serves as a cornerstone for the design and analysis of wideband ranging systems. The article also develops practical soft-decision and hard-decision algorithms and presents a case study for ranging and localization systems operating in a wireless environment, where sample-level simulations validate the theoretical results.

M. Chiani, A. Giorgetti, and E. Paolini, “Sensor Radar for Object Tracking,” Proceedings of the IEEE, vol. 106, no. 6, pp. 1022–1041, June 2018.
This article presents a sensor network for radio imaging (sensor radar) along with all of the signal processing steps necessary to achieve high accuracy objects tracking in harsh propagation environments. The proposed sensor radar is based on the impulse radio ultra-wideband (UWB) technology, with experimental results in indoor environments that confirm the sensor radar's potential in IoT applications.

S. Bartoletti, A. Giorgetti, M. Z. Win, and A. Conti, “Blind Selection of Representative Observations for Sensor Radar Networks,” IEEE Transactions on Vehicular Technology, vol. 64, no. 4, pp. 1388–1400, April 2015.
This article introduces blind techniques for the selection of representative observations gathered by sensor radars operating in harsh environments. A methodology for the design and analysis of sensor radar networks is developed, taking into account the propagation impairments and observation selection. This work shows how observation selection improves the localization accuracy for non-coherent ultra-wideband sensor radars in a typical indoor environment.

S. Mazuelas, Y. Shen, and M. Z. Win, “Belief Condensation Filtering,” IEEE Transactions on Signal Processing, vol. 61, no. 18, pp. 4403–4415, September 2013.
This article proposes a new filtering methodology called belief condensation (BC) filtering for nonlinear and/or non-Gaussian scenarios. A general framework for filtering techniques together with an optimality criterion is established, which leads to BC filtering. Efficient BC algorithms are proposed to represent the complex distributions arising in the filtering process, and their performance advantage in term of accuracy-complexity tradeoff is validated through two representative nonlinear/non-Gaussian problems.

J. Prieto, S. Mazuelas, and M. Z. Win, “Context-Aided Inertial Navigation via Belief Condensation,” IEEE Transactions on Signal Processing, vol. 64, no. 12, pp. 3250–3261, June 2016.
This article presents a framework for context-aided inertial navigation and develops efficient algorithms for its implementation based on the inference technique called belief condensation (BC). The performance of the proposed techniques is evaluated against the state of the art through an experimental case study, which demonstrates that the proposed techniques can remarkably improve the navigation accuracy with moderate complexities.

Y. Shen, W. Dai, and M. Z. Win, “Power Optimization for Network Localization,” IEEE/ACM Transactions on Networking, vol. 22, no. 4, pp. 1337–1350, August 2014.
This article establishes a unifying optimization framework for power allocation in both active and passive localization networks. Amenable functional properties of the localization accuracy metric are revealed, which allow to transform the power allocation problems into second-order cone programs (SOCPs). The article also proposes the robust counterparts of the problems in the presence of parameter uncertainty and develops asymptotically optimal and efficient near-optimal SOCP-based algorithms.

T. Wang, G. Leus, and L. Huang, “Ranging Energy Optimization for Robust Sensor Positioning Based on Semidefinite Programming,” IEEE Transactions on Signal Processing, vol. 57, no. 12, pp. 4777–4787, December 2009.
This article investigates ranging energy optimization problems for an unsynchronized positioning system, which features robust sensor positioning in the sense that a specific accuracy requirement is fulfilled within a prescribed service area. It presents a practical algorithm based on semidefinite programming for ranging energy optimization problems.

W. Dai, Y. Shen, and M. Z. Win, “Distributed Power Allocation for Cooperative Wireless Network Localization,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 1, pp. 28–40, January 2015.
This article presents an optimization framework for robust power allocation in cooperative wireless network localization and develops distributed power allocation strategies with low computation complexity and communication overhead. The power allocation problem for cooperative network can be decomposed into infrastructure and cooperation phases, where the optimal power allocation is proven to possess a sparsity property.

S. Mazuelas, R. M. Lorenzo, A. Bahillo, P. Fernandez, J. Prieto, and E. J. Abril, “Topology Assessment Provided by Weighted Barycentric Parameters in Harsh Environment Wireless Location Systems,” IEEE Transactions on Signal Processing, vol. 58, no. 7, pp. 3842-3857, July 2010.
This paper shows that the highest accuracy bound is achieved when certain geometrical condition relating anchors and agents is satisfied. The geometric parameters obtained from the positions of anchors and target referred to as topology-assessment-weighted-barycentric-parameters (TAWBAP) characterize the performance of the localization process showing the influence of the geometric configuration in connection with the specific characteristics of each range estimate. In addition, the paper presents design guidelines based on the TAWBAP parameters and quantifies the performance improvement. The proposed methods can result in a performance improvement that outperforms topology deployments that have been considered optimal in the literature.

R. Niu and P. K. Varshney, “Target Location Estimation in Sensor Networks with Quantized Data,” IEEE Transactions on Signal Processing, vol. 54, no. 12, pp. 4519–4528, December 2006.
This article proposes a signal intensity based maximum-likelihood target location estimator that uses quantized data for wireless sensor networks (WSNs). The method is significantly more accurate than heuristic weighted average methods and can reach the CRLB even with a relatively small amount of data. In addition, the paper also presents the optimal design method for quantization thresholds.

J. Chen, W. Dai, Y. Shen, V. K.N. Lau, and M. Z. Win, “Power Management for Cooperative Localization: A Game Theoretical Approach,” IEEE Transactions on Signal Processing, vol. 64, no. 24, pp. 6517–6532, December 2016.
This article proposes a new type of power management strategies where each agent individually minimizes its square position error bound penalized by its power cost. The strategies are obtained as solutions to two power management games that are formulated under the knowledge of local information and global information, respectively. Analytical and numerical results show that the proposed strategies significantly reduce the energy consumption with only marginal performance loss in position accuracy.

W. Dai, Y. Shen, and M. Z. Win, “A Computational Geometry Framework for Efficient Network Localization,” IEEE Transactions on Information Theory, vol. 64, no. 2, pp. 1317-1339, February 2018.
This article develops a computational geometry framework for determining the optimal node prioritization strategy. The framework consists of transforming each node prioritization strategy into a point in a Euclidian space and exploiting geometric properties of these points. Under this framework, the sparsity property of the optimal node prioritization vector is proven, which significantly reduces the search space of the optimal solution. This work yields exact optimal solutions rather than epsilon-approximate solutions for efficient network localization.

H. Godrich, A. P. Petropulu, and H. V. Poor, “Power Allocation Strategies for Target Localization in Distributed Multiple-Radar Architectures,” IEEE Transactions on Signal Processing, vol. 59, no. 7, pp. 3226–3240, July 2011.
This article proposes two resource allocation schemes for distributed multiple radar systems. The schemes minimize the total transmitted energy for a predefined estimation MSE threshold. The resulting nonconvex optimization problems are solved through relaxation and domain decomposition methods, supporting both central processing at the fusion center and distributed processing.

T. Wang, Y. Shen, A. Conti, and M. Z. Win, “Network Navigation with Scheduling: Error Evolution,” IEEE Transactions on Information Theory, vol. 63, no. 11, pp. 7509–7534, November 2017.
This article introduces situation-aware scheduling that exploits network states to select measurement pairs and develops a framework to characterize the effects of scheduling strategies and of network settings on the error evolution. Both sufficient and necessary conditions for the boundedness of the error evolution are provided, and opportunistic and random situation-aware scheduling strategies are proposed, and their performance is characterized by the time-averaged network localization errors.

Z. Ma and K. C. Ho, “A Study on the Effects of Sensor Position Error and the Placement of Calibration Emitter for Source Localization,” IEEE Transactions on Wireless Communications, vol. 13, no. 10, pp. 5440–5452, October 2014.
This article investigates the effects of position errors of randomly deployed sensors and the placement of calibration emitter for source localization. It determines the conditions under which a simpler estimator is sufficient to reach the optimum performance and proposes a criterion for calibration emitter placement.

D. Dardari, A. Conti, J. Lien, and M. Z. Win, “The Effect of Cooperation on Localization Systems Using UWB Experimental Data,” EURASIP Journal on Advances in Signal Processing, vol. 2008, pp. 1–11, Article ID 513 873, 2008, special issue on Cooperative Localization in Wireless Ad Hoc and Sensor Networks.
This article derives models for the range estimation error and the excess delay based on measured data from real ranging devices for multilateration localization algorithms. Measurements in a real indoor scenario are used to characterize how the localization accuracy is affected by the number of beacons and by the availability of priori information about the environment and network geometry. An iterative multilateration algorithm that incorporates information gathered through cooperation is then proposed with the purpose of improving the localization accuracy.

S. Maranò, W. M. Gifford, H. Wymeersch, and M. Z. Win, “NLOS Identification and Mitigation for Localization Based on UWB Experimental Data,” IEEE Journal on Selected Areas in Communications, vol. 28, no. 7, pp. 1026–1035, September 2010.
This article develops classification and regression algorithms based on machine learning techniques, which can assess whether a signal was transmitted in LOS or NLOS conditions and reduce ranging error caused by NLOS conditions. The algorithm extracts features from the channel impulse responses which are representative of the propagation conditions for learning. In contrast to common probabilistic approaches with statistical models of the features, the proposed optimization-based approach is more robust against modeling errors.

A. I. Mourikis and S. I. Roumeliotis, “Performance Analysis of Multi-Robot Cooperative Localization,” IEEE Transactions on Robotics, vol. 22, no. 4, pp. 666–681, August 2006.
This article studies the accuracy of position estimation for groups of mobile robots performing cooperative localization. It focuses on the case of teams comprised of heterogeneous robots and provides analytical expressions for the upper bound on their positioning uncertainty. The article also analyzes the maximum expected rate of uncertainty increase and the effects of topology change.

A. Conti, M. Guerra, D. Dardari, N. Decarli, and M. Z. Win, “Network Experimentation for Cooperative Localization,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 2, pp. 467–475, February 2012.
This article introduces the notion of network experimentation and proposes an experimentation methodology particularly suited for cooperative wireless networks. Extensive ultrawide bandwidth measurement campaigns are performed, based on which various cooperative localization techniques are compared under a common setting. Network experiments enable the quantification of cooperation benefits, the development of techniques for harnessing environmental information, and the characterization of network localization algorithms.

A. Conti, D. Dardari, M. Guerra, L. Mucchi, and M. Z. Win, “Experimental Characterization of Diversity Navigation,” IEEE Systems Journal, vol. 8, no. 1, pp. 115–124, March 2014.
This article characterizes the diversity of navigation systems in real environments using an extensive measurement campaign, where data from heterogeneous sensors were collected simultaneously. The performance of Bayesian navigation algorithms, relying on the particle filter implementation, is evaluated based on measured data from ultrawideband, ZigBee, and inertial sensors. This enables to quantify the benefits of data fusion as well as the effect of statistical mobility models for real-time diversity navigation.

Z. Liu, W. Dai, and M. Z. Win, “Mercury: An Infrastructure-Free System for Network Localization and Navigation,” IEEE Transactions on Mobile Computing, vol. 17, no. 5. 1119-1133, May 2018.
This article presents the Mercury system, which realizes the key ideas of network localization and navigation, including the exploitation of spatiotemporal cooperation and the use of environmental knowledge. A real-time belief propagation algorithm is developed to fuse inertial measurements and range measurements among different users with map information. The Mercury system is implemented on a network of smartphones, and the localization accuracy is evaluated through experimentation. Mercury can reduce the location uncertainty of users and is robust to imperfect initial positional knowledge.