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A problem of state estimation with destination constraint is considered in this paper. An anti-radiation missile (ARM) often moves towards the target along a trajectory which is almost linear in the X-Y plane. The linear constraint for trajectory and target position are known as priori and can be used to enhance the performance of a tracking filter. In this paper, a destination constrained Kalman...
Recently, correlation filter-based tracking algorithms have attracted much attention for its high efficiency and robustness. However, achieving fast and accurate scale estimation remains a challenging problem. Most existing scale estimation approaches are inefficient and time-consuming. Besides, these existing trackers perform poorly when the object is under fast motion and partial occlusion due to...
Correlation filter based tracking method has been widely used for its high efficiency and robustness. However, reducing model drifting while achieving both high robustness and fast scale estimation is still an open problem. In this paper, we represent the target in kernel feature space and train a classifier on a scale pyramid to achieve adaptive scale estimation. We then integrate three complementary...
We present a recursive track-before-detect (TBD) algorithm based on the bin-occupancy filter for multi-target tracking from image observations by asking a different question: "Is there a target within a given pixel?" Analytic posterior probability distribution of the bin-state is derived for image measurements under the non-overlapping assumption. Coupled with the intrinsic grid characteristic...
The cardinality balanced multitarget multi-bernoulli (CBMeMBer) filter show excellent performance compare with the the probability hypothesis density (PHD) and cardinality PHD (CPHD) filters. However, the algorithm addressed only single-sensor scenarios. To resolve the problem of multisensor multitarget tracking with CBMeMBer filter, the iterated-corrector CBMeMBer filter, as a heuristic “iterated-corrector...
In this paper, we present a novel and efficient track-before-detect (TBD) algorithm based on multiple-model probability hypothesis density (MM-PHD) for tracking infrared maneuvering dim multi-target. Firstly, the standard sequential Monte Carlo probability hypothesis density (SMC-PHD) TBD-based algorithm is introduced and sequentially improved by the adaptive process noise and the importance re-sampling...
Aiming at tracking Closely-Spaced Objects (CSOs) from image observations, a new method using the Probability Hypothesis Density (PHD) filter is proposed. To circumvent the unresolved measurements problem, a detection process is used firstly to extract the connected sets of object pixels that likely correspond to the unresolved targets of interest. Then the representative measurements are constructed...
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