A very useful paradigm to design robust detectors is the maxmin approach, which maximizes the worst case detection performance.
Figure 11 shows one of the worst case detection results for (a) torus, (b) ball, (c) plasticine figures, (d) elephant, (e) dinosaur and (f) dog. Figure 11a,c,d shows that in some cases local descriptors may not be descriptive enough to distinguish between similar objects.
As performance criterion we consider the worst-case detection time of static intruders.
Third and finally, we develop three distributed algorithms, for the cameras to partition the roadmap, and, consequently, synchronize along a trajectory with minimum worst-case detection time.
One can observe that the proposed method places a peak around the target location, that is, the worst-case detection performance in the convex uncertainty may be improved.
A novel DL-based method has been proposed to maximize the worst-case output SINR of MIMO STAP such that the worst-case detection performance can be maximized.
In this article, we have investigated the problem of robust waveform optimization for improving the worst-case detection performance of MIMO STAP by explicitly incorporating the parameter estimate uncertainty into the optimization model.
In this article, we address the problem of robust waveform optimization for improving the worst-case detection performance of multi-input multi-output (MIMO) space time adaptive processing (STAP) in the presence of colored Gaussian disturbance.
In this article, we consider the problem of robust waveform design in the presence of colored Gaussian disturbance (including clutter, jamming, and thermal noise), which maximizes the worst-case detection performance of MIMO STAP.
A novel diagonal loading-based method is proposed to optimize the waveform covariance matrix for maximizing the worst-case output signal-interference-noise ratio (SINR) over the convex uncertainty set such that the worst-case detection performance of MIMO STAP can be maximized.
Because maximization of the output SINR is tantamount to maximization of the detection performance in the case of Gaussian noise (see e.g., [6, 7] and the references therein for more details), here the waveform covariance matrix (WCM) is optimized to maximize the worst-case output SINR of MIMO STAP over the convex uncertainty set such that the worst-case detection performance can be maximized.
