Tracking methods in a multitarget environment

The objective of this paper is to survey and put in perspective the existing methods of tracking in multitarget environment. In such an environment the origin of the measurements can be uncertain: they could have come from the target(s) of interest or clutter or false alarms or be due to the background. This compact and unified presentation of the state-of-art in multitarget tracking was motivated by the recent surge of interest in this problem. It is also hoped to be useful in view of the need to adapt and modify existing techniques before using them for specific problems. Particular attention is paid to the assumptions underlying each algorithm and its applicability to various situations.

[1]  A. Jaffer,et al.  Optimal sequential estimation of discrete processes with Markov interrupted observations , 1971 .

[2]  Ronald Sea An efficient suboptimal decision procedure for associating sensor data with stored tracks in real-time surveillance systems , 1971, CDC 1971.

[3]  Yaakov Bar-Shalom,et al.  Adaptive nonlinear filtering for tracking with measurements of uncertain origin , 1972, CDC 1972.

[4]  R. Singer,et al.  New results in optimizing surveillance system tracking and data correlation performance in dense multitarget environments , 1973 .

[5]  K. Fu,et al.  On state estimation in switching environments , 1968 .

[6]  R. Mehra A comparison of several nonlinear filters for reentry vehicle tracking , 1971 .

[7]  C. Morefield Application of 0-1 integer programming to multitarget tracking problems , 1977 .

[8]  Kim B. Housewright,et al.  Derivation and evaluation of improved tracking filter for use in dense multitarget environments , 1974, IEEE Trans. Inf. Theory.

[9]  Robert W. Sittler,et al.  An Optimal Data Association Problem in Surveillance Theory , 1964, IEEE Transactions on Military Electronics.

[10]  Michael Athans,et al.  A suboptimal estimation algorithm with probabilistic editing for false measurements with applications to target tracking with wake phenomena , 1977 .

[11]  John Stein,et al.  An optimal tracking filter for processing sensor data of imprecisely determined origin in surveillance systems , 1971, CDC 1971.

[12]  Nasser E. Nahi,et al.  Optimal recursive estimation with uncertain observation , 1969, IEEE Trans. Inf. Theory.

[13]  Hiromitsu Kumamoto,et al.  Random sampling approach to state estimation in switching environments , 1977, Autom..

[14]  T. Kailath,et al.  An innovations approach to least-squares estimation--Part II: Linear smoothing in additive white noise , 1968 .

[15]  Y. Bar-Shalom,et al.  Tracking in a cluttered environment with probabilistic data association , 1975, Autom..

[16]  Amin G. Jaffer,et al.  Recursive Bayesian estimation with uncertain observation (Corresp.) , 1971, IEEE Trans. Inf. Theory.

[17]  P. Smith,et al.  A branching algorithm for discriminating and tracking multiple objects , 1975 .

[18]  D. Alspach A gaussian sum approach to the multi-target identification-tracking problem , 1975, Autom..