Multitarget Tracking with Lidar and Stereo Vision Based on Probabilistic Data Association FIR Filter

Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? the KITTI vision benchmark suite, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361 Bagloee SA, Tavana M, Asadi M, Oliver T (2016) Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J Modern Transp 24(4):284–303 Minderhoud MM, Bovy PH (2001) Extended time-to-collision measures for road traffic safety assessment. Accid Anal Prevent 33(1):89–97 Blackman SS (1986) Multiple-target tracking with radar applications. MA, Artech House Inc, Dedham You H, Jianjuan X, Xin G (2016) Radar data processing with applications. Wiley, Hoboken Okuma K, Taleghani A, De Freitas N, Little JJ, Lowe DG (2004) “A boosted particle filter: Multitarget detection and tracking,” in European Conference on Computer Vision, pp. 28–39 Bar-Shalom Y, Daum F, Huang J (2009) The probabilistic data association filter. IEEE Control Syst 29(6):82–100 Attari M, Habibi S, Gadsden S (2017) Target tracking formulation of the SVSF with data association techniques. IEEE Trans Aerosp Electr Syst 53(1):12–25 Xu L, Jin S, Yin G (2013) Track fusion via track filter in dense sensor environments. Measurement 46(10):3871–3875 Wang C-C, Thorpe C, Thrun S, Hebert M, Durrant-Whyte H (2007) Simultaneous localization, mapping and moving object tracking. Int J Robot Res 26(9):889–916 Zheng L, Wang X (2017) “Improved NN-JPDAF for joint multiple target tracking and feature extraction,” arXiv preprint arXiv:1703.08254, Colegrove S, Davey S (2003) PDAF with multiple clutter regions and target models. IEEE Trans Aerosp Electr Syst 39(1):110–124 Wilthil EF, Flåten AL, Brekke EF (2017) A target tracking system for ASV collision avoidance based on the PDAF in sensing and control for autonomous vehicles. Springer, Berlin, pp 269–288 Bar-Shalom Y, Willett PK, Tian X (2011) Tracking and data fusion, vol 11. Storrs, CT, USA, YBS publishing Singer R, Sea R, Housewright K (1974) Derivation and evaluation of improved tracking filter for use in dense multitarget environments. IEEE Trans Inform Theory 20(4):423–432 Bar-Shalom Y, Tse E (1975) Tracking in a cluttered environment with probabilistic data association. Automatica 11(5):451–460 Wang Y, Karimi HR, Lam H-K, Yan H (2019) Fuzzy output tracking control and filtering for nonlinear discrete-time descriptor systems under unreliable communication links. IEEE Trans Cybern 50(6):2369–2379 Wang Y, Ahn CK, Yan H, Xie S (2020) Fuzzy control and filtering for nonlinear singularly perturbed Markov jump systems. IEEE Trans Cybern 51(1):297–308 Li D-J, Li J, Li S (2016) Adaptive control of nonlinear systems with full state constraints using integral barrier lyapunov functionals. Neurocomputing 186:90–96 Liu L, Liu Y-J, Chen A, Tong S, Chen CP (2020) Integral barrier lyapunov function-based adaptive control for switched nonlinear systems. Sci China Inform Sci 63(3):1–14 Liu L, Li X, Liu Y-J, Tong S (2021) Neural network based adaptive event trigger control for a class of electromagnetic suspension systems. Control Eng Pract 106:104675 Bar-Shalom Y, Fortmann TE, Cable PG (1990) Tracking and data association. J Acoust Soc Am 87(2):918–919 Blom HA, Bloem EA (2006) “Joint particle filtering of multiple maneuvering targets from unassociated measurements,” tech. rep. National Aerospace Laboratory NLR Geiger A, Lenz P, Urtasun R (2018) Augmented human state estimation using interacting multiple model particle filters with probabilistic data association. IEEE Robot Autom Lett 3(3):12–25 Pak JM, Ahn CK, Shmaliy YS, Lim MT (2015) Improving reliability of particle filter-based localization in wireless sensor networks via hybrid particle/FIR filtering. IEEE Trans Indus Inform 11(5):1089–1098 Ahn CK, Shmaliy YS (2018) New receding horizon FIR estimator for blind smart sensing of velocity via position measurements. IEEE Trans Circuits Syst II: Express Briefs 65(1):135–139 Ahn CK, Shi P, Basin MV (2016) Deadbeat dissipative FIR filtering. IEEE Trans Circuits Syst I: Regular Papers 63(8):1210–1221 Pak JM, Ahn CK, Shi P, Shmaliy YS, Lim MT (2016) Distributed hybrid particle/FIR filtering for mitigating NLOS effects in TOA-based localization using wireless sensor networks. IEEE Trans Indus Electr 64(6):5182–5191 Shmaliy YS, Zhao S, Ahn CK (2017) Unbiased finite impulse response filtering: An iterative alternative to Kalman filtering ignoring noise and initial conditions. IEEE Control Syst 37(5):70–89 Jeong HB, Ahn CK, You SH, Sohn KM (2018) Finite-memory estimation for vehicle roll and road bank angles. IEEE Trans Indus Electr 66(7):5423–5432 You SH, Ahn CK, Shmaliy YS, Zhao S (2018) Minimum weighted Frobenius norm discrete-time FIR filter with embedded unbiasedness. IEEE Trans Circuits Syst II: Express Briefs 65(9):1284–1288 Beymer D, Konolige K (1999) “Real-time tracking of multiple people using continuous detection,” in IEEE Frame Rate Workshop, pp. 1–8, Dalal N, Triggs B (2005) “Histograms of oriented gradients for human detection,” in IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893, IEEE, 2005 Colegrove S, Davis A, Ayliffe J (1986) Track initiation and nearest neighbours incorporated into probabilistic data association. J Electr Electr Eng, Aust 6(3):191–198 Chakravorty R, Challa S (2006) Augmented state integrated probabilistic data association smoothing for automatic track initiation in clutter. J Adv Inform Fusion 1(1):63–74 Särkkä S, Vehtari A, Lampinen J (2007) Rao-blackwellized particle filter for multiple target tracking. Inform Fusion 8(1):2–15 Yang R, Ng BP, Ng GW (2009) Multiple model multiple hypothesis filter with sojourn-time-dependent semi-markov switching. IEEE Signal Process Lett 16(6):529–532 Navarro-Serment LE, Mertz C, Hebert M (2010) Pedestrian detection and tracking using three-dimensional ladar data. Int J Robot Res 29(12):1516–1528 You SH, Ahn CK, Zhao S, Shmaliy YS (2021) “Frobenius norm-based unbiased FIR fusion filtering for wireless sensor networks,” IEEE Transactions on Industrial Electronics, to be published, https://doi.org/10.1109/TIE.2021.3055172. You SH, Ahn CK, Shmaliy YS, Zhao S (2019) Fusion Kalman and weighted UFIR state estimator with improved accuracy. IEEE Trans Ind Electr 67(12):10713–10722 Mahalanobis, P.C.: “On the generalised distance in statistics,” Proceedings of the National Institute of Sciences of India, vol. 2, no. 1, pp. 49–55, (1936) Singh SK, Premalatha M, Nair G (1995) “Ellipsoidal gating for an airborne track while scan radar,” in IEEE International Radar Conference, pp. 334–339, IEEE Smith RC, Cheeseman P (1986) On the representation and estimation of spatial uncertainty. Int J Robot Res 5(4):56–68 Ge B, Zhang H, Jiang L, Li Z, Butt MM (2019) Adaptive unscented kalman filter for target tracking with unknown time-varying noise covariance. Sensors 19(6):1371 Schuhmacher D, Vo B-T, Vo B-N (2008) A consistent metric for performance evaluation of multi-object filters. IEEE Trans Signal Process 56(8):3447–3457 Kang HH, Lee SW, You SH, Ahn CK (2018) Novel vehicle detection system based on stacked dog kernel and adaboost. PloS one 13(3):e0193733