LAUSR

Multistatic radio frequency (RF) imaging systems utilize distributed RF sensors which transmit waveforms to illuminate a target scene and estimate the dielectric properties of the region of interest from the received echoes. This article applies the principles of dynamic data driven systems to improve the performance of multistatic RF imaging system in terms of power efficiency and image reconstruction accuracy. Target location information derived from initial image reconstruction is applied to dynamically reconfigure the imaging system. Based on the geolocation of targets relative to the distributed RF sensors, Fisher information matrices associated with the targets and multistatic sensor pairs are calculated and applied to derive optimum power allocation strategies under different constraints. Image reconstruction of multistatic RF imaging often suffers from artifacts and ghost targets caused by multipath propagation and multiorder reflections.A grouped-coordinate descent type reconstruction algorithm is developed to exploit target location information. The iterative optimization alternates between the groups of target space parameters and nontarget space parameters. The dictionary of reconstruction is dynamically updated to include the secondary reflections from target locations. The improved model fidelity leads to more accurate reconstructions without a significant increase in computational complexity. Numerical simulations demonstrate that the proposed power allocation strategies are effective in energy saving. The proposed reconstruction algorithm converges faster than sequential parameter update algorithms, and the reconstruction accuracy is superior to that using a fixed dictionary containing the first order reflections only.