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Resilient distributed state estimation with mobile agents: overcoming Byzantine adversaries, communication losses, and intermittent measurements

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Abstract

Applications in environmental monitoring, surveillance and patrolling typically require a network of mobile agents to collectively gain information regarding the state of a static or dynamical process evolving over a region. However, these networks of mobile agents also introduce various challenges, including intermittent observations of the dynamical process, loss of communication links due to mobility and packet drops, and the potential for malicious or faulty behavior by some of the agents. The main contribution of this paper is the development of resilient, fully-distributed, and provably correct state estimation algorithms that simultaneously account for each of the above considerations, and in turn, offer a general framework for reasoning about state estimation problems in dynamic, failure-prone and adversarial environments. Specifically, we develop a simple switched linear observer for dealing with the issue of time-varying measurement models, and resilient filtering techniques for dealing with worst-case adversarial behavior subject to time-varying communication patterns among the agents. Our approach considers both communication patterns that recur in a deterministic manner, and patterns that are induced by random packet drops. For each scenario, we identify conditions on the dynamical system, the patrols, the nominal communication network topology, and the failure models that guarantee applicability of our proposed techniques. Finally, we complement our theoretical results with detailed simulations that illustrate the efficacy of our algorithms in the presence of the technical challenges described above.

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Notes

  1. In the absence of any constraints placed on the sensing capabilities or movement patterns of an agent, one can just have each mobile agent patrol all the sensing locations. However, such an assumption would in general be impractical, thereby necessitating inter-agent communication. Note that it is precisely the need for inter-agent communication that makes the issues of communication losses and adversarial attacks studied in this paper relevant.

  2. We resort to such a notation here since the superscript on the z[k] states are reserved for eigenvalues, and the subscripts are reserved for mobile agents. Thus, we introduce the notation \({\mathbf {v}}[k]\), with a superscript on \({\mathbf {v}}[k]\) pointing to a location number.

  3. The gains \({\mathbf {L}}^{(i_r)}_i\) are agent-specific, since different agents might visit the same location with different frequencies.

  4. Essentially, an odd period ensures that eigenvalues that are equal in magnitude, but opposite in sign in \({\mathbf {A}}\), remain so in \({\mathbf {A}}^{{\bar{T}}}\). Thus, if the eigenvalues of \({\mathbf {A}}\) are distinct in magnitude, then clearly no restrictions need to be imposed on the time-period \({\bar{T}}\).

  5. Since we are considering system matrices with distinct eigenvalues, an eigenvalue is detectable w.r.t. the pair \(({\mathbf {A}},{\mathbf {C}}^{({\mathcal {P}}_i)})\) if and only if it is detectable w.r.t. \(({\mathbf {A}},{\mathbf {C}}^{(i_r)})\), for some \(i_r \in {\mathcal {P}}_i.\) The ‘only if’ part of the statement may not be true for system matrices with repeated eigenvalues.

  6. This is one of the key differences of our present formulation with the resilient consensus literature. In the latter setting, there is no external state that needs to be tracked, and Sundaram and Hadjicostis (2011) and Pasqualetti et al. (2012) have shown that making the network sufficiently connected suffices to facilitate resilient consensus.

  7. Details of such an attack strategy can be found in Mitra and Sundaram (2018c). For centralized systems where f sensors are compromised, Fawzi et al. (2014) and Chong et al. (2015) have shown that for recovering the state of the system asymptotically, the system must remain detectable after the removal of any 2f sensors.

  8. For notational simplicity, while considering the eigenvalue \(\lambda _j\), we drop the superscript ‘j’ on the time-stamp \(\phi _{il}[k]\) and the delay \(\tau _{il}[k]\).

  9. If agent i receives an estimate without a time-stamp from some agent in \({\mathcal {N}}^{(j)}_i \cap {\mathcal {A}}\), it simply assigns a value of 0 to such an estimate (without loss of generality). Note that based on Assumption 2, agent i is guaranteed to receive a time-stamped estimate from every regular agent l in \({\mathcal {N}}^{(j)}_i\) at least once over every interval of the form \([k-T,k], \, \forall k \ge T\), i.e., for each \(l \in {\mathcal {N}}^{(j)}_i \cap {\mathcal {R}}\), \({\bar{z}}^{(j)}_{il}[k]\) will necessarily be of the form \( {\lambda _j}^{\tau _{il}[k]}{\hat{z}}^{(j)}_l[k-\tau _{il}[k]]\), \(\, \forall k\ge T\).

  10. In other words, due to false time-stamp information, the quantity \({\hat{z}}^{(j)}_l[k-\tau _{il}[k]]\) may not represent the true estimate of an adversarial agent l at time \((k-\tau _{il}[k])\). Thus, we resort to a slight abuse of notation here.

  11. Explicit dependence of u, v on the parameters represented by i, j, l and k is not shown to avoid cluttering of the exposition.

  12. Although we only establish asymptotic stability of the error dynamics in Proposition 1, verifying exponential stability is fairly straightforward, and hence, not explicitly proven.

  13. Unlike the SW-LFRE algorithm developed in Sect. 5, the algorithm we propose here is memoryless, i.e., at each time-step, an agent acts only on the information that it acquires (via measurements and from neighboring agents) at that time-step. We do this primarily to simplify the analysis.

  14. The choice of \(m \ge 3\) is justified later in Remark 13.

  15. To avoid cluttering the exposition, we drop the superscript ‘j’ on \({\mathcal {I}}_i[k]\) and certain other terms throughout the proof, since they can be inferred from context.

  16. The result continues to hold for the general update rule (21).

  17. The set \(\mathcal {M}^{(j)}_i[k]\) is not well-defined when \(\mathcal {I}_i[k]=1\). For such a case, l can be taken to be any node in the set \(\mathcal {N}^{(j)}_i\cap \mathcal {R}\).

  18. The need for strong \((3f+1)\)-robustness in the baseline network was provided in Remark 13, and will also be justified explicitly via simulations.

References

  • Abazeed, M., Faisal, N., Zubair, S., & Ali, A. (2013). Routing protocols for wireless multimedia sensor network: a survey. Journal of Sensors.

  • Alamdari, S., Fata, E., & Smith, S. L. (2014). Persistent monitoring in discrete environments: Minimizing the maximum weighted latency between observations. The International Journal of Robotics Research, 33(1), 138–154.

    Article  Google Scholar 

  • Artelli, M. J., & Deckro, R. F. (2008). Modeling the Lanchester laws with system dynamics. The Journal of Defense Modeling and Simulation, 5(1), 1–20.

    Article  Google Scholar 

  • Asghar, A. B., Jawaid, S. T., & Smith, S. L. (2017). A complete greedy algorithm for infinite-horizon sensor scheduling. Automatica, 81, 335–341.

    Article  MathSciNet  MATH  Google Scholar 

  • Atanasov, N., Le Ny, J., Daniilidis, K., & Pappas, G. J. (2014). Information acquisition with sensing robots: Algorithms and error bounds. In Proceedings of the 2014 IEEE international conference on robotics and automation (ICRA) (pp. 6447–6454).

  • Atanasov, N., Le Ny, J., Daniilidis, K., & Pappas, G. J. (2015). Decentralized active information acquisition: Theory and application to multi-robot SLAM. In Proceedings of the 2015 IEEE international conference on robotics and automation (ICRA) (pp. 4775–4782).

  • Chakrabarty, A., Ayoub, R., Żak, S. H., & Sundaram, S. (2017). Delayed unknown input observers for discrete-time linear systems with guaranteed performance. Systems & Control Letters, 103, 9–15.

    Article  MathSciNet  MATH  Google Scholar 

  • Chakrabarty, A., Fridman, E., Żak, S. H., & Buzzard, G. T. (2018). State and unknown input observers for nonlinear systems with delayed measurements. Automatica, 95, 246–253.

    Article  MathSciNet  MATH  Google Scholar 

  • Chen, Y., Kar, S., & Moura, J. M. F. (2018). Resilient distributed estimation through adversary detection. IEEE Transactions on Signal Processing, 66(9), 2455–2469.

    Article  MathSciNet  MATH  Google Scholar 

  • Chen, C.-T. (1998). Linear system theory and design. Oxford: Oxford University Press.

    Google Scholar 

  • Chong, M. S., Wakaiki, M., & Hespanha, J. P. (2015). Observability of linear systems under adversarial attacks. In Proceedings of the American control conference (pp. 2439–2444).

  • Chung, F. (2007). The heat kernel as the pagerank of a graph. Proceedings of the National Academy of Sciences, 104(50), 19735–19740.

    Article  Google Scholar 

  • Cressie, N. (1990). The origins of kriging. Mathematical Geology, 22(3), 239–252.

    Article  MathSciNet  MATH  Google Scholar 

  • Deghat, M., Ugrinovskii, V., Shames, I., & Langbort, C. (2016). Detection of biasing attacks on distributed estimation networks. In Proceedings of the IEEE conference on decision and control (pp. 2134–2139).

  • del Nozal, A. R., Orihuela, L., & Milláan, P. (2017). Distributed consensus-based Kalman filtering considering subspace decomposition. IFAC-PapersOnLine, 50(1), 2494–2499.

    Article  Google Scholar 

  • Dibaji, S. M., & Ishii, H. (2017). Resilient consensus of second-order agent networks: Asynchronous update rules with delays. Automatica, 81, 123–132.

    Article  MathSciNet  MATH  Google Scholar 

  • Dolev, D., Lynch, N. A., Pinter, S. S., Stark, E. W., & Weihl, W. E. (1986). Reaching approximate agreement in the presence of faults. Journal of the ACM (JACM), 33(3), 499–516.

    Article  MathSciNet  MATH  Google Scholar 

  • Doostmohammadian, M., & Khan, U. A. (2013). On the genericity properties in distributed estimation: Topology design and sensor placement. IEEE Journal of Selected Topics in Signal Processing, 7(2), 195–204.

    Article  Google Scholar 

  • Dunbabin, M., Roberts, J. M., Usher, K., & Corke, P. (2004). A new robot for environmental monitoring on the Great Barrier Reef. In Proceedings of the 2004 Australasian conference on robotics & automation. Australian Robotics & Automation Association

  • Elia, N. (2005). Remote stabilization over fading channels. Systems & Control Letters, 54(3), 237–249.

    Article  MathSciNet  MATH  Google Scholar 

  • Fawzi, H., Tabuada, P., & Diggavi, S. (2014). Secure estimation and control for cyber-physical systems under adversarial attacks. IEEE Transactions on Automatic Control, 59(6), 1454–1467.

    Article  MathSciNet  MATH  Google Scholar 

  • Gandin, L. S. (1963). Objective analysis of meteorological fields. Israel Program for Scientific Translations, 242.

  • Goodin, D. (2016). There is a new way to take down drones, and it doesn’t involve shotguns. arsTechnica, October 2016.

  • Graham, R., & Cortés, J. (2012). Adaptive information collection by robotic sensor networks for spatial estimation. IEEE Transactions on Automatic Control, 57(6), 1404–1419.

    Article  MathSciNet  MATH  Google Scholar 

  • Guerrero-Bonilla, L., Prorok, A., & Kumar, V. (2017). Formations for resilient robot teams. IEEE Robotics and Automation Letters, 2(2), 841–848.

    Article  Google Scholar 

  • Gupta, V., Chung, T. H., Hassibi, B., & Murray, R. M. (2006). On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage. Automatica, 42(2), 251–260.

    Article  MathSciNet  MATH  Google Scholar 

  • Han, W., Trentelman, H. L., Wang, Z., & Shen, Y. (2018). A simple approach to distributed observer design for linear systems. IEEE Transactions on Automatic Control.

  • Hespanha, J. P., Naghshtabrizi, P., & Yonggang, X. (2007). A survey of recent results in networked control systems. Proceedings of the IEEE, 95(1), 138–162.

    Article  Google Scholar 

  • Higdon, D. (1998). A process-convolution approach to modelling temperatures in the North Atlantic Ocean. Environmental and Ecological Statistics, 5(2), 173–190.

    Article  Google Scholar 

  • Jawaid, S. T., & Smith, S. L. (2015). Submodularity and greedy algorithms in sensor scheduling for linear dynamical systems. Automatica, 61, 282–288.

    Article  MathSciNet  MATH  Google Scholar 

  • Kaur, T., & Kumar, D. (2015). Wireless multifunctional robot for military applications. In Proceedings of the 2015 2nd IEEE international conference on recent advances in engineering & computational sciences (RAECS) (pp. 1–5).

  • Khan, U., & Stankovic, A. M. (2013). Secure distributed estimation in cyber-physical systems. In Proceedings of the IEEE international conference on acoustics, speech and signal processing (pp. 5209–5213).

  • Khan, U., Kar, S., Jadbabaie, A., & Moura, J. M. F. (2010). On connectivity, observability, and stability in distributed estimation. In Proceedings of the 49th IEEE conference on decision and control (pp. 6639–6644).

  • Khan, U. A., & Jadbabaie, A. (2014). Collaborative scalar-gain estimators for potentially unstable social dynamics with limited communication. Automatica, 50(7), 1909–1914.

    Article  MathSciNet  MATH  Google Scholar 

  • Khan, U., & Moura, J. M. F. (2008). Distributing the Kalman filter for large-scale systems. IEEE Transactions on Signal Processing, 56(10), 4919–4935.

    Article  MathSciNet  MATH  Google Scholar 

  • Kube, C. (2018). Russia has figured out how to jam U.S. drones in Syria, officials say. NBC News, Apr. 2018.

  • LeBlanc, H. J., Zhang, H., Koutsoukos, X., & Sundaram, S. (2013). Resilient asymptotic consensus in robust networks. IEEE Journal on Selected Areas in Communications, 31(4), 766–781.

    Article  Google Scholar 

  • Liu, X., Cheng, S., Liu, H., Sha, H., Zhang, D., & Ning, H. (2012). A survey on gas sensing technology. Sensors, 12(7), 9635–9665.

    Article  Google Scholar 

  • Lynch, K. M., Schwartz, I. B., Yang, P., & Freeman, R. A. (2008). Decentralized environmental modeling by mobile sensor networks. IEEE Transactions on Robotics, 24(3), 710–724.

    Article  Google Scholar 

  • Martínez, S. (2010). Distributed interpolation schemes for field estimation by mobile sensor networks. IEEE Transactions on Control Systems Technology, 18(2), 491–500.

    Article  Google Scholar 

  • Matei, I., Baras, J. S., & Srinivasan, V. (2012). Trust-based multi-agent filtering for increased smart grid security. In Proceedings of the Mediterranean conference on control & automation (pp. 716–721).

  • Matei, I., & Baras, J. S. (2012). Consensus-based linear distributed filtering. Automatica, 48(8), 1776–1782.

    Article  MathSciNet  MATH  Google Scholar 

  • Millán, P., Orihuela, L., Vivas, C., & Rubio, F. R. (2012). Distributed consensus-based estimation considering network induced delays and dropouts. Automatica, 48(10), 2726–2729.

    Article  MathSciNet  MATH  Google Scholar 

  • Mitra, A., & Sundaram, S. (2016a). An approach for distributed state estimation of LTI systems. In Proceedings of the 54th annual Allerton conference on communication, control, and computing (pp. 1088–1093).

  • Mitra, A., & Sundaram, S. (2016b). Secure distributed observers for a class of linear time invariant systems in the presence of Byzantine adversaries. In Proceedings of the IEEE conference on decision and control (pp. 2709–2714).

  • Mitra, A., & Sundaram, S. (2018a). Distributed observers for LTI systems. IEEE Transactions on Automatic Control, 63(11), 3689–3704.

    Article  MathSciNet  MATH  Google Scholar 

  • Mitra, A., & Sundaram, S. (2018b). A novel switched linear observer for estimating the state of a dynamical process with a mobile agent. In Proceedings of the 57th IEEE conference on decision and control.

  • Mitra, A., & Sundaram, S. (2018c). Byzantine-resilient distributed observers for LTI systems. arXiv preprint arXiv:1802.09651.

  • Mitra, A., & Sundaram, S. (2018d). Secure distributed state estimation of an LTI system over time-varying networks and analog erasure channels. In Proceedings of the 2018 American control conference (pp. 6578–6583).

  • Mo, Y., Ambrosino, R., & Sinopoli, B. (2011). Sensor selection strategies for state estimation in energy constrained wireless sensor networks. Automatica, 47(7), 1330–1338.

    Article  MathSciNet  MATH  Google Scholar 

  • Moore, T. (1985). Robots for nuclear power plants. IAEA Bulletin, 27(3), 31–38.

    Google Scholar 

  • Ogren, P., Fiorelli, E., & Leonard, N. E. (2004). Cooperative control of mobile sensor networks: Adaptive gradient climbing in a distributed environment. IEEE Transactions on Automatic control, 49(8), 1292–1302.

    Article  MathSciNet  MATH  Google Scholar 

  • Olfati-Saber, R. (2009). Kalman-consensus filter: Optimality, stability, and performance. In Proceedings of the 48th IEEE conference on decision and control held jointly with the 28th Chinese control conference (pp. 7036–7042).

  • Park, H., & Hutchinson, S. (2018). Robust rendezvous for multi-robot system with random node failures: An optimization approach. Autonomous Robots, 1–12.

  • Park, H., & Hutchinson, S. A. (2017). Fault-tolerant rendezvous of multirobot systems. IEEE Transactions on Robotics, 33(3), 565–582.

    Article  Google Scholar 

  • Park, S., & Martins, N. C. (2017). Design of distributed LTI observers for state omniscience. IEEE Transactions on Automatic Control, 62(2), 561–576.

    Article  MathSciNet  MATH  Google Scholar 

  • Pasqualetti, F., Bicchi, A., & Bullo, F. (2012). Consensus computation in unreliable networks: A system theoretic approach. IEEE Transactions on Automatic Control, 57(1), 90–104.

    Article  MathSciNet  MATH  Google Scholar 

  • Qian, K., Song, A., Bao, J., & Zhang, H. (2012). Small teleoperated robot for nuclear radiation and chemical leak detection. International Journal of Advanced Robotic Systems, 9(3), 70.

    Article  Google Scholar 

  • Rego, F. F. C., Aguiar, A. P., Pascoal, A. M., & Jones, C. N. (2017). A design method for distributed Luenberger observers. In Proceedings of the 56th IEEE conference on decision and control (pp. 3374–3379).

  • Roy, S., & Dhal, R. (2015). Situational awareness for dynamical network processes using incidental measurements. IEEE Journal of Selected Topics in Signal Processing, 9(2), 304–316.

    Article  Google Scholar 

  • Saldana, D., Prorok, A., Sundaram, S., Campos, M. F. M., & Kumar, V. (2017). Resilient consensus for time-varying networks of dynamic agents. In Proceedings of the American control conference (pp. 252–258).

  • Saulnier, K., Saldana, D., Prorok, A., Pappas, G. J., & Kumar, V. (2017). Resilient flocking for mobile robot teams. IEEE Robotics and Automation Letters, 2(2), 1039–1046.

    Article  Google Scholar 

  • Schlotfeldt, B., Tzoumas, V., Thakur, D., & Pappas, G. J. (2018). Resilient active information gathering with mobile robots. arXiv preprint arXiv:1803.09730.

  • Smith, S. L., Schwager, M., & Rus, D. (2012). Persistent robotic tasks: Monitoring and sweeping in changing environments. IEEE Transactions on Robotics, 28(2), 410–426.

    Article  Google Scholar 

  • Smith, R. N., Schwager, M., Smith, S. L., Jones, B. H., Rus, D., & Sukhatme, G. S. (2011). Persistent ocean monitoring with underwater gliders: Adapting sampling resolution. Journal of Field Robotics, 28(5), 714–741.

    Article  Google Scholar 

  • Speranzon, A., Fischione, C., & Johansson, K. H. (2006). Distributed and collaborative estimation over wireless sensor networks. In Proceedings of the 45th IEEE conference on decision and control (pp. 1025–1030).

  • Srinivasan, S., Latchman, H., Shea, J., Wong, T., & McNair, J. (2004). Airborne traffic surveillance systems: Video surveillance of highway traffic. In Proceedings of the ACM 2nd international workshop on video surveillance & sensor networks (pp. 131–135). ACM.

  • Su, L., & Vaidya, N. H. (2016). Fault-tolerant multi-agent optimization: Optimal iterative distributed algorithms. In Proceedings of the 2016 ACM symposium on principles of distributed computing (pp. 425–434). ACM.

  • Su, L., & Vaidya, N. H. (2016). Non-Bayesian learning in the presence of Byzantine agents. In International symposium on distributed computing (pp. 414–427). Springer.

  • Sundaram, S., & Gharesifard, B. (2015). Consensus-based distributed optimization with malicious nodes. In Proceedings of the annual Allerton conference on communication, control and computing (pp. 244–249).

  • Sundaram, S., & Hadjicostis, C. N. (2011). Distributed function calculation via linear iterative strategies in the presence of malicious agents. IEEE Transactions on Automatic Control, 56(7), 1495–1508.

    Article  MathSciNet  MATH  Google Scholar 

  • Thanou, D., Dong, X., Kressner, D., & Frossard, P. (2017). Learning heat diffusion graphs. IEEE Transactions on Signal and Information Processing over Networks, 3(3), 484–499.

    Article  MathSciNet  Google Scholar 

  • Tseng, L., Vaidya, N., & Bhandari, V. (2015). Broadcast using certified propagation algorithm in presence of Byzantine faults. Information Processing Letters, 115(4), 512–514.

    Article  MathSciNet  MATH  Google Scholar 

  • Ugrinovskii, V. (2013). Distributed robust estimation over randomly switching networks using \({H}_{\infty }\) consensus. Automatica, 49(1), 160–168.

    Article  MathSciNet  MATH  Google Scholar 

  • Usevitch, J., & Panagou, D. (2017). \(r\)-robustness and \((r,s)\)-robustness of circulant graphs. In Proceedings of the 56th IEEE conference on decision and control (pp. 4416–4421).

  • Usevitch, J., & Panagou, D. (2018). Resilient leader-follower consensus to arbitrary reference values. In Proceedings of the 2018 American control conference (pp. 1292–1298).

  • Vaidya, N. H., Tseng, L., & Liang, G. (2012). Iterative approximate Byzantine consensus in arbitrary directed graphs. In Proceedings of the ACM symposium on principles of distributed computing (pp. 365–374).

  • Vitus, M. P., Zhang, W., Abate, A., Jianghai, H., & Tomlin, C. J. (2012). On efficient sensor scheduling for linear dynamical systems. Automatica, 48(10), 2482–2493.

    Article  MathSciNet  MATH  Google Scholar 

  • Wang, L., & Morse, A. S. (2018). A distributed observer for a time-invariant linear system. IEEE Transactions on Automatic Control, 63(7), 2123–2130.

    Article  MathSciNet  MATH  Google Scholar 

  • Wang, L., Morse, A. S., Fullmer, D., & Liu, J. (2017). A hybrid observer for a distributed linear system with a changing neighbor graph. In Proceedings of the 2017 56th IEEE conference on decision and control (pp. 1024–1029).

  • Xie, L., & Zhang, X. (2013). 3D clustering-based camera wireless sensor networks for maximizing lifespan with minimum coverage rate constraint. In Proceedings of the 2013 IEEE Global Communications Conference (GLOBECOM) (pp. 298–303).

  • Yang, P., Freeman, R. A., & Lynch, K. M. (2008). Multi-agent coordination by decentralized estimation and control. IEEE Transactions on Automatic Control, 53(11), 2480–2496.

    Article  MathSciNet  MATH  Google Scholar 

  • Yazıcıoğlu, A. Y., Egerstedt, M., & Shamma, J. S. (2015). Formation of robust multi-agent networks through self-organizing random regular graphs. IEEE Transactions on Network Science and Engineering, 2(4), 139–151.

    Article  MathSciNet  Google Scholar 

  • Zakaria, A. H., Mustafah, Y. M., Abdullah, J., Khair, N., & Abdullah, T. (2017). Development of autonomous radiation mapping robot. Procedia Computer Science, 105, 81–86.

    Article  Google Scholar 

  • Zhang, H., Fata, E., & Sundaram, S. (2015). A notion of robustness in complex networks. IEEE Transactions on Control of Network Systems, 2(3), 310–320.

    Article  MathSciNet  MATH  Google Scholar 

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  1. School of Electrical and Computer Engineering at Purdue University, West Lafayette, USA

    Aritra Mitra, Saurabh Bagchi & Shreyas Sundaram

  2. Sandia National Laboratories, Albuquerque, USA

    John A. Richards

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This is one of the several papers published in Autonomous Robots comprising the Special Issue on Foundations of Resilience for Networked Robotic Systems.

This work was supported in part by NSF CAREER award 1653648, and by a grant from Sandia National Laboratories. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. The views expressed in the article do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

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Mitra, A., Richards, J.A., Bagchi, S. et al. Resilient distributed state estimation with mobile agents: overcoming Byzantine adversaries, communication losses, and intermittent measurements. Auton Robot 43, 743–768 (2019). https://doi.org/10.1007/s10514-018-9813-7

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