EM-ML algorithm for track initialization using possibly noninformative data

Initializing and maintaining a track for a low observable (LO) (low SNR, low target detection probability and high false alarm rate) target can be very challenging because of the low information content of measurements. In addition, in some scenarios, target-originated measurements might not be present in many consecutive scans because of mispointing, target maneuvers, or erroneous preprocessing. That is, one might have a set of noninformative scans that could result in poor track initialization and maintenance. In this paper an algorithm based on the expectation-maximization (EM) algorithm combined with maximum likelihood (ML) estimation is presented for tracking slowly maneuvering targets in heavy clutter and possibly noninformative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down noninformative scans using the Q-function in the M-step of the EM algorithm. It is shown that target features in the form of, for example, amplitude information (AI), can also be used to improve the estimates. In addition, performance bounds based on the supplemented EM (SEM) technique are also presented. The effectiveness of new algorithm is first demonstrated on a 78-frame long wave infrared (LWIR) data sequence consisting of an Fl Mirage fighter jet in heavy clutter. Previously, this scenario has been used as a benchmark for evaluating the performance of other track initialization algorithms. The new EM-ML estimator confirms the track by frame 20 while the ML-PDA (maximum likelihood estimator combined with probabilistic data association) algorithm, the IMM-MHT (interacting multiple model estimator combined with multiple hypothesis tracking) and the EVIM-PDA estimator previously required 28, 38, and 39 frames, respectively. The benefits of the new algorithm in terms of accuracy, early detection, and computational load are illustrated using simulated scenarios as well.

[1]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[2]  T. Louis.  Finding the Observed Information Matrix When Using the EM Algorithm , 1982 .

[3]  I. Meilijson.  A fast improvement to the EM algorithm on its own terms , 1989 .

[4]  S. C. Pohlig,et al.  An algorithm for detection of moving optical targets , 1989 .

[5]  Yaakov Bar-Shalom,et al.  Track formation with bearing and frequency measurements in clutter , 1990 .

[6]  Xiao-Li Meng,et al.  Using EM to Obtain Asymptotic Variance-Covariance Matrices: The SEM Algorithm , 1991 .

[7]  D. Avitzour,et al.  A maximum likelihood approach to data association , 1992 .

[8]  Roy L. Streit,et al.  Maximum likelihood method for probabilistic multihypothesis tracking , 1994, Defense, Security, and Sensing.

[9]  Aubrey B. Poore,et al.  Multidimensional assignment formulation of data association problems arising from multitarget and multisensor tracking , 1994, Comput. Optim. Appl..

[10]  Y. Bar-Shalom,et al.  Low observable target motion analysis using amplitude information , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[11]  S. C. Pohlig,et al.  Spatial-temporal detection of electro-optic moving targets , 1995 .

[12]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[13]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[14]  Darin T. Dunham,et al.  Tracking multiple targets in cluttered environments with a probabilistic multihypothesis tracker , 1997 .

[15]  A. Logothetis,et al.  An expectation-maximisation tracker for multiple observations of a single target in clutter , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[16]  Y. Bar-Shalom,et al.  A generalized S-D assignment algorithm for multisensor-multitarget state estimation , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[17]  C. Jauffret,et al.  A formulation of multitarget tracking as an incomplete data problem , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[18]  Yaakov Bar-Shalom,et al.  IR target detection and clutter reduction using the interacting multiple-model estimator , 1998, Defense, Security, and Sensing.

[19]  Stacy H. Roszkowski.  Common database for tracker comparison , 1998, Defense, Security, and Sensing.

[20]  S. Sivananthan,et al.  A radar power multiplier algorithm for acquisition of low observable ballistic missiles using an ESA radar , 1999, 1999 IEEE Aerospace Conference. Proceedings (Cat. No.99TH8403).

[21]  Samuel S. Blackman,et al.  Design and Analysis of Modern Tracking Systems , 1999 .

[22]  Taejeong Kim,et al.  Optimization of multiframe target detection schemes , 1999 .

[23]  Hong Jeong,et al.  Application of EM algorithm to adaptive filter for multiple target tracking , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[24]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[25]  Yaakov Bar-Shalom,et al.  Passive ranging of a low observable ballistic missile in a gravitational field , 2001 .

[26]  Jose M. F. Moura,et al.  Integration of Bayes detection and target tracking in real clutter image sequences , 2001, Proceedings of the 2001 IEEE Radar Conference (Cat. No.01CH37200).

[27]  S. Sivananthan,et al.  Radar power multiplier for acquisition of low observables using an ESA radar , 2001 .

[28]  G. W. Pulford,et al.  MAP estimation of target manoeuvre sequence with the expectation-maximization algorithm , 2002 .

[29]  Y. Bar-Shalom,et al.  Adaptive early-detection ML-PDA estimator for LO targets with EO sensors , 2002 .

[30]  Peter Willett,et al.  PMHT: problems and some solutions , 2002 .

[31]  William H. Press,et al.  Numerical recipes in C , 2002 .