Principles of Modern Radar - Volume 2 1891121537

696 CHAPTER 15 Multitarget, Multisensor TrackingMetrics for multitarget tracking applications are often difficult to implement in realworldsettings (as opposed to simulations), where the targets being tracked may be unresolved,hence making the true origin of each measurement somewhat ambiguous. Kamendeveloped an approach for multitarget tracking using symmetric measurement equations[21] with potential application to metrics, and Daum built on this to derive the Cramer-RaoLower Bound (CRLB) for multitarget tracking [13]. For a discussion of the difficulties ofscoring multitarget tracking algorithms, see [33, 40].In the interest of brevity, performance prediction was outside the scope of this chapter.However, it is a relevant topic for those involved in algorithm development or system acquisition.Performance prediction literature includes papers focused on the measurementto-trackassociation step [2] as well as the track filtering step [27, 28].The multitarget, multisensor tracking literature also includes sophisticated techniquesfor coping with highly-nonlinear or non-Gaussian problem sets. For example, unscentedKalman Filters [18] have been demonstrated to be superior to extended Kalman Filterswhen the propagation function and/or the measurement conversion function are highlynonlinear.Particle filters [39] have been developed to accommodate applications in whichthe underlying estimation statistics are non-Gaussian. Mahler [29] has derived the probabilistichypothesis density algorithm using finite-set statistics, to simultaneously estimatethe number of targets present and estimate their states.15.6 REFERENCES[1] Alouani, A.T., Xia, P., Rice, T.R., and Blair, W.D., “A Two-Stage Kalman Estimator forState Estimation in the Presence of Random Bias and for Tracking Maneuvering Targets,”Proceedings of the 30th IEEE Conference on Decision and Control, pp. 2059–2062, 1991.[2] Areta, J., Bar-Shalom, Y., and Rothrock, R., “Misassociation Probability in M2TA and T2TA,”Journal for Advances in Information Fusion, vol. 2, no.2, pp. 113–117, 2007.[3] Bar-Shalom, Y., “Update with Out-of-Sequence Measurements in Tracking: Exact Solution,”IEEE Transactions on Aerospace and Electronic Systems, vol. 38, pp. 769–777, 2002.[4] Bar-Shalom, Y., Blackman, S., and Fitzgerald, R., “The Dimensionless Score Function forMultiple Hypothesis Tracking,” in Signal and Data Processing of Small Targets SPIE vol.5913, 2005.[5] Bar-Shalom, Y., and Campo, L, “The Effect of the Common Process Noise on the Two-SensorFused-Track Covariance,” correspondence in IEEE Transactions on Aerospace and ElectronicSystems, vol. 22, pp. 803–805, 1986.[6] Bar-Shalom, Y., and Huimin, C., “Removal of Out-of-Sequence Measurements from Tracks,”IEEE Transactions on Aerospace and Electronic Systems, vol. 45, pp. 612–619, 2009.[7] Bar-Shalom, Y., and Li, X.R., Multitarget-Multisensor Tracking: Principles and Techniques,YBS Publishing, 1995.[8] Blackman, S., and R. Popoli, Design and Analysis of Modern Tracking Systems, Artech House,1999.[9] Blackman, S., “Multiple Hypothesis Tracking for Multiple Target Tracking,” IEEE A&ESystems Magazine, vol., 19, no. 1, 2004.[10] Blair, W.D., “Radar Tracking Algorithms,” Chapter 19 in Principles of Modern Radar: BasicPrinciples, Scitech, 2010.