Reducing the Number of Measurement Nodes in RF Imaging using Antenna Pattern Diversity with an Extended Rytov Approximation

RF imaging that leverages existing wireless communication infrastructure, such as radio tomographic imaging (RTI) and joint communication and sensing (JCAS) is becoming increasingly important. A challenge of RF imaging is that it requires large measurement datasets containing independent measurements. In this paper we investigate a method to reduce the number of measurement nodes in RF imaging so that it is more suitable for integration with wireless communication. The approach is to exploit antenna pattern diversity so that each node can collect multiple independent measurements from the same measurement location, thereby decreasing the number of measurement nodes required. Furthermore, we formulate pattern diversity for RF imaging using the recently developed extended Rytov Approximation (xRA) which has been demonstrated to provide remarkable RF reconstruction accuracy. The advantage of utilizing xRA is that it allows us to utilize the metric of sensing capacity to straightforwardly quantify the potential of various pattern diversity configurations. Using the sensing capacity metric we are able to identify configurations where the number of measurement nodes can be reduced by at least a factor of two. Simulation results are provided to verify the RF imaging approach with reduced measurement nodes, which demonstrates the potential of using pattern diversity.