Adaptive FDA Radar Transmit Power Allocation for Target Detection Enhancement in Clutter Environment

The detection of static targets in clutter environments has been an active topic in radar community, which is also a difficult problem, since there is no Doppler information that can be used to distinguish the target from clutter. Aiming at this problem, in this article, we develop an adaptive approach based on frequency diverse array (FDA) radar to enhance the detection performance. First, a parametric measurement model that incorporates the practical dependence of the clutter responses on transmit frequencies is introduced. Then, based on the generalized likelihood ratio (GLR) test, an adaptive detector that requires no training data is developed to determine the existence of a target in a specific cell under test, where the scattering features of the target and clutter are assumed unknown. In addition, the detector has the property of constant false alarm rate (CFAR) with respect to the clutter. Moreover, under the criterion of maximizing the detection probability, we propose an adaptive scheme in which the collected data are utilized to select the transmit weights for power allocation to improve the detection. Numerical simulations are conducted to illustrate the effectiveness of the proposed detector and demonstrate the performance improvement owing to the adaptive power allocation design.