Passive Bistatic Radar Using Weather Radars and Electromagnetic Vector Sensors

There are nearly 150 identical dual-polarized Doppler weather radars in the U.S. providing weather forecasts and warnings over a wide coverage area. A passive radar network uses available signals of opportunity such as weather surveillance radar, with one or several receivers, to detect the target. Because the network uses available signals of opportunity, the need to build sophisticated infrastructure for the transmitter is avoided, thereby reducing the overall cost. In addition, the receivers are smaller, cheaper, consume less power, and can be easily deployed, making the network less vulnerable to electronic counter measures. However, the non-cooperative nature of the transmitter can lead to poor spatial/Doppler resolution of the target. Further, the presence of strong inhomogeneous clutter yields a poor estimate of the signal subspace, which leads to the degradation in the performance of the detector.

To overcome these challenges, we propose a passive bistatic radar network using weather radar as the signal of opportunity. We exploit the polarimetric information of the target and the surrounding clutter with the help of diversely polarized antennas such as electromagnetic vector sensors (EMVS). We first extract the signal subspace from the received data contaminated by signal-dependent clutter interference using a maximum likelihood approach. Thereafter, we design a statistically-based generalized likelihood ratio test (GLRT) to detect the target in the presence of strong inhomogeneous clutter. Under the asymptotic case, we show that our proposed GLRT method is a constant false alarm rate (CFAR) detector. Then, with the help of numerical simulations, we demonstrate the robustness and limitations of our proposed detector. The results of our proposed approach can also be used in multifunction phased array radar (MPAR).