Multiple input multiple output (MIMO) radars are composed of several transmitters
and receivers, where each transmit element radiates a different waveform. Collocated
MIMO radar systems exploit the waveform diversity, based on mutual orthogonality
of the transmitted signals. MIMO radar exhibits several advantages with respect
to traditional radar array systems in terms of flexibility and performance. However,
MIMO radar poses new challenges for both hardware design and digital processing.
In particular, MIMO suffers from range-azimuth resolution trade-off between large
bandwidth desired for high range resolution and small bandwidth for high azimuth
resolution. In addition, achieving high azimuth resolution requires a large number of
transmit and receive antennas. Moreover, the digital processing is performed on samples of the received signal, from each transmitter to each receiver, at its Nyquist rate, which can be prohibitively large when high resolution is needed.
In this work, we adopt a frequency division multiple access (FDMA) approach to
avoid the range-azimuth resolution conflict and show that, using FDMA the narrowband assumption may be relaxed to the individual bandwidth. We can then combine large overall total bandwidth for the sake of high range resolution and narrow individual bandwidth to allow for a larger aperture and high azimuth resolution. In addition, we process all channels jointly to overcome the FDMA range resolution limitation to a single bandwidth, and address range-azimuth coupling using a random array configuration.

Finally, overcoming the rate bottleneck, sub-Nyquist sampling methods have been
proposed that break the link between radar signal bandwidth and sampling rate. In
this work, we extend these methods to MIMO configurations and propose a sub-Nyquist MIMO radar (SUMMeR) system that performs both time and spatial compression. We present a range-azimuth-Doppler recovery algorithm from sub-Nyquist samples obtained from a reduced number of transmitters and receivers, that exploits the sparsity of the recovered targets’ parameters. This allows us to achieve reduction in the number of deployed antennas and the number of samples per receiver, without degrading the time and spatial resolutions.
Our enhanced range-azimuth resolution capabilities illustrated in simulations and
numerical experiments show that our FDMA approach based on joint processing outperforms the classic code division multiple access (CDMA). In addition, simulations illustrate the detection performance of SUMMeR for different compression levels and show that both time and spatial resolution are preserved, with respect to classic Nyquist MIMO configurations. We also examine the impact of design parameters, such as antennas’ locations and carrier frequencies, on the detection performance, and provide guidelines for their choice. Finally, we present a hardware prototype of the SUMMeR system with capabilities of cognitive transmission. The prototype allows both spatial and time compressive sampling.