  In this letter, we consider the varying detection environments to address the
problem of detecting small targets within sea clutter. We first extract three
simple yet practically discriminative features from the returned signals in the
time and frequency domains and then fuse them into a 3-D feature space. Based
on the constructed space, we then adopt and elegantly modify the support vector
machine (SVM) to design a learning-based detector that enfolds the false alarm
rate (FAR). Most importantly, our proposed detector can flexibly control the
FAR by simply adjusting two introduced parameters, which facilitates to
regulate detector's sensitivity to the outliers incurred by the sea spikes and
to fairly evaluate the performance of different detection algorithms.
Experimental results demonstrate that our proposed detector significantly
improves the detection probability over several existing classical detectors in
both low signal to clutter ratio (SCR) (up to 58%) and low FAR (up to 40%)
cases.
