This chapter focuses on active sensing using sparse arrays. In active sensing applications, such as radar, sonar, wireless communications, and medical ultrasound, a collection of sensors probes the environment by emitting self-generated energy. A key benefit of such active multi-sensor arrays is their ability to focus and steer energy in desired directions by beamforming on transmit. Sparse sensor arrays offer several advantages over conventional uniform arrays, including improved resolution using fewer physical sensors and the capability to identify more scatterers than sensors. This is facilitated by the effective transmit-receive virtual array known as the sum co-array, which can have many more virtual sensors than the number of physical transmit or receive sensors. Herein, we focus on the design of low-redundancy sparse array configurations and on employing transmit-receive (Tx-Rx) beamforming using sparse arrays. We discuss the optimal, but computationally intractable Minimum-redundancy array, and a scalable symmetric array framework, which extends many well-known passive sparse array geometries to the active case. We also examine mitigating side lobes arising from spatial undersampling by a synthetic beamforming method known as image addition. We briefly present approaches for finding the physical beamforming weights synthesizing a desired Tx-Rx beampattern, and consider related spatio-temporal trade-offs. We conclude by discussing selected applications of sparse arrays in active sensing.