19 research outputs found
Efficient Transmit Beamspace Design for Search-free Based DOA Estimation in MIMO Radar
In this paper, we address the problem of transmit beamspace design for
multiple-input multiple-output (MIMO) radar with colocated antennas in
application to direction-of-arrival (DOA) estimation. A new method for
designing the transmit beamspace matrix that enables the use of search-free DOA
estimation techniques at the receiver is introduced. The essence of the
proposed method is to design the transmit beamspace matrix based on minimizing
the difference between a desired transmit beampattern and the actual one under
the constraint of uniform power distribution across the transmit array
elements. The desired transmit beampattern can be of arbitrary shape and is
allowed to consist of one or more spatial sectors. The number of transmit
waveforms is even but otherwise arbitrary. To allow for simple search-free DOA
estimation algorithms at the receive array, the rotational invariance property
is established at the transmit array by imposing a specific structure on the
beamspace matrix. Semi-definite relaxation is used to transform the proposed
formulation into a convex problem that can be solved efficiently. We also
propose a spatial-division based design (SDD) by dividing the spatial domain
into several subsectors and assigning a subset of the transmit beams to each
subsector. The transmit beams associated with each subsector are designed
separately. Simulation results demonstrate the improvement in the DOA
estimation performance offered by using the proposed joint and SDD transmit
beamspace design methods as compared to the traditional MIMO radar technique.Comment: 32 pages, 10 figures, submitted to the IEEE Trans. Signal Processing
in May 201
Moving Target Parameters Estimation in Non-Coherent MIMO Radar Systems
The problem of estimating the parameters of a moving target in multiple-input
multiple-output (MIMO) radar is considered and a new approach for estimating
the moving target parameters by making use of the phase information associated
with each transmit-receive path is introduced. It is required for this
technique that different receive antennas have the same time reference, but no
synchronization of initial phases of the receive antennas is needed and,
therefore, the estimation process is non-coherent. We model the target motion
within a certain processing interval as a polynomial of general order. The
first three coefficients of such a polynomial correspond to the initial
location, velocity, and acceleration of the target, respectively. A new maximum
likelihood (ML) technique for estimating the target motion coefficients is
developed. It is shown that the considered ML problem can be interpreted as the
classic "overdetermined" nonlinear least-squares problem. The proposed ML
estimator requires multi-dimensional search over the unknown polynomial
coefficients. The Cram\'er-Rao Bound (CRB) for the proposed parameter
estimation problem is derived. The performance of the proposed estimator is
validated by simulation results and is shown to achieve the CRB.Comment: 17 pages, 4 figures, Submitted to the IEEE Trans. Signal Processing
in Aug. 201
Guest Editorial Special Issue on Integrated Sensing and Communication-Part I
Driving a gradual integration of the physical and digital worlds is perceived to become a reality in the 6G era, from vehicles to drones, from surveillance facilities in cities to agricultural tools in the countryside. Jointly motivated by recent advances in communication and signal processing, radio sensing functionality can be integrated into a 6G radio access network (RAN) in a low-cost and fast manner. That is, future networks have the ability to “see” the physical world through imaging and measuring the surrounding environment, which enables advanced location-aware services, ranging from the physical to application layers. In essence, a radio emission could simultaneously convey communication data from the transmitter to the receiver and deliver environmental information from the scattered echoes. Therefore, sensing and communication (S&C) functionalities are possible to be co-designed to utilize resources efficiently and to assist each other for mutual benefits. This type of research is typically referred to as integrated sensing and communication (ISAC)