Initializing and maintaining a track for a low observable (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 non-informative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down non-informative scans using the Q-function in the M-step of the EM algorithm. A track validation technique is used to conﬁrm the validity of the EM-ML estimates. It is shown that target features in the form of, for
example, amplitude information, 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 F1 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 IMM-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.