Hardware Demonstration of a Scalable Cognitive Sparse Array

High-resolution direction of arrival estimation requires a large number of antenna elements which increases the computational cost, hardware complexity, and power requirements. To balance between hardware complexity and resolution, recently, we proposed a cognitive, scalable, sparse array selection technique based on a submodular-greedy algorithm. In this demo, we present a design and implementation of a hardware prototype that demonstrate the proposed sparse antenna selection strategy. Through real-time experiments, we show that the proposed sparse selection method results in a 2 - 3 dB lower error compared to a typically employed random selection method.