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The detection of moving targets has become an important application in the recent years.
Tracking moving targets has become an increasingly important application for sensor networks.
A particular recent series of research studies on the problem of tracking/pursuiting moving targets has followed an approach based on artificial potentials and sliding mode control.
Motion parameter estimation of ground moving targets has been studied extensively in the context of conventional radar systems (e.g., single-antenna radar [3], phased-array radar [4], and multiple-input multiple-output (MIMO) radar [5 7]).
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Besides, splitting and merging of multiple shapes depending on moving targets have not been considered by previous methods.
The sharpness difference curves of the moving targets have the same profiles as that presented in Figure 9.
After its subtraction from the raw radar data, the primary traces of moving targets have arisen in the radargrams (Figures 5 e) and 5(f)).
The radar returns corresponding to stationary objects like the clutter from natural scenes are canceled, while the returns backscattered by moving targets have a different phase in the two acquisitions and remain uncanceled.
Neurons sensitive to (and in some cases selective for) small moving targets have been described in a variety of insect species [4] [7].
At the surveillance antenna, the contribution of a moving target has the form [2] s r ( t ) = A ( t ) e jϕ ( t ) s ( t − τ ( t ) ) + w ( t ).
Computer simulations and practical experiments of a mobile robot to track a moving target have been carried out to validate the performance and robustness of the proposed system.
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