High-Resolution MIMO Radar based on Undersampled Observations

Athina Petropulu
Rutgers University
Wachman 447
Friday, April 26, 2013 - 11:00
In situation awareness applications, there is interest in networked sensors that are inexpensive and have small form factors, yet they enable reliable surveillance of an area. Unfortunately, these two requirements are competing in nature. Reliable surveillance requires collection, communication and fusion of vast amounts of data from a range of sensors. This in turn results in increased operational cost and larger form factors because of increased communication cost and computational resources. The talk presents a new approach that achieves an optimal tradeoff between the aforementioned competing requirements, i.e., it achieves "super-resolution" in the angle, Doppler and range space, while limiting the amount of data measured and transmitted by each sensor through the network. In particular, we consider a colocated multiple-input multiple-output (MIMO) radar scenario, in which the receive antennas forward their measurements of target returns to a fusion center. Based on the received data, the fusion center formulates a matrix which is then used for target parameter estimation. When the receive antennas
sample the target returns at Nyquist rate the data matrix at the fusion center is low-rank. When each receive antenna sends to the fusion center only a small number of samples, along with the sample index, the receive data matrix has missing elements, corresponding to the samples that were not forwarded. Under certain conditions, matrix completion techniques can be applied to recover the full receive data matrix, which can then be used in conjunction with array processing techniques, e.g., MUSIC, to obtain target information. Numerical results indicate that good target recovery can be achieved with occupancy of the receive data matrix as low as 50%. Theoretical results are provided on the conditions under which target information can be recovered.