Recently, multiple-input multiple-output (MIMO) radars have received considerable attention due to their superior resolution. A MIMO radar system lends itself to a networked implementation, which is very desirable in both military and civilian applications. In networked radars, the transmit and receive antennas are placed on wireless connected nodes, such as vehicles, ships, airplanes, or even backpacks. The transmit antennas transmit probing waveforms, which impinge on targets and are reflected back. A fusion center collects the target echo measurements of all receive antennas and jointly processes the signals to extract the desired target parameters. This dissertation proposes to address the following two bottleneck issues associated with networked radars. Reliable surveillance requires collection, communication and process of vast amounts of data. This is a power and bandwidth consuming task, which can be especially taxing in scenarios in which the antennas are on battery operated devices and are connected to the fusion center via a wireless link. Sparse sensing techniques are used to substantially reduce the amount of data that need to be communicated to a fusion center, while ensuring high target detection and estimation performance. In the first part, this dissertation derives the theoretical requirements and performance guarantees for the application of compressive sensing to both MIMO radar settings, namely, the collocated MIMO radars and the distributed MIMO radars. Confirming previous simulations based observations, the theoretical results of this thesis show that exploiting the sparsity of the target vector can reduce the amount of measurements needed for successful target estimation. For compressive sensing based distributed MIMO radars, we also propose two low-complexity signal recovery approaches. With the increasing demand of radio spectrum, the operating frequency bands of communication and radar systems often overlap, causing one system to exert interference to the other. Uncoordinated interference from communication systems may significantly harm the tactical radar functionality and vice versa. In the second part, this dissertation studies spectrum sharing between a MIMO communication system and a MIMO radar system in various scenarios. First, a cooperative spectrum sharing framework is proposed for the coexistence of MIMO radars and wireless communications. Radar transmit precoding and adaptive communication transmission are adopted, and are jointly designed to maximize signal-to-interference-plus-noise ratio (SINR) at the radar receiver subject to the communication system meeting certain rate and power constraints. Compared to the non-cooperative approaches in the literature, the proposed approach has the potential to improve the spectrum utilization because it introduces more degrees of freedom. In addition, the proposed spectrum sharing framework considers several practical issues which are not addressed in literature, e.g., the radar pulsed transmit pattern, targets falling in different range bins, and radar systems operating in the presence of clutter. Second, we investigate spectrum sharing between a MIMO communication system and a recently proposed sparse sensing based radar, namely the matrix completion based MIMO radar (MIMO-MC). MIMO-MC radar receivers take sub-Nyquist rate samples, and transfer them to a fusion center where the full data matrix is completed with high accuracy. MIMO-MC radars, in addition to reducing communication bandwidth and power as compared to MIMO radars, offer a significant advantage for spectrum sharing. The advantage stems from the way the sub-sampling scheme at the radar receivers modulates the interference channel from the communication system transmitters, rendering it symbol dependent and reducing its row space. This makes it easier for the communication system to design its waveforms in an adaptive fashion so that it minimizes the interference to the radar subject to meeting rate and power constraints. Two methods are investigated to minimize the effective interference power to the radar receiver: 1) design the communication transmit covariance matrix with fixed the radar sampling scheme, and 2) jointly design the communication transmit covariance matrix and the MIMO-MC radar sampling scheme. Furthermore, we investigate joint transmit precoding for the co-existence of a MIMO-MC radar and a MIMO wireless communication system in the presence of clutter. We show that the error performance of matrix completion in MIMO-MC radars is theoretically guaranteed when precoding is employed. The radar transmit precoder, the radar sub-sampling scheme, and the communication transmit covariance matrix are jointly designed to maximize the radar SINR while meeting certain communication rate and power constraints. Efficient optimization algorithms are provided along with insight on the proposed design problem.