Sparse Antenna and Pulse Placement for Colocated MIMO Radar

Abstract

Multiple input multiple output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. Although higher angular resolution, improved parameter identifiability, and better target detection are achieved, the hardware costs (due to multiple transmitters and multiple receivers) and high energy consumption (multiple pulses) limit the usage of MIMO radars in large scale networks. On one hand, higher angle and velocity estimation accuracy is required, but on the other hand, a lower number of antennas/pulses is desirable. To achieve such a compromise, in this work, the Cramér-Rao lower bound (CRLB) for the angle and velocity estimator is employed as a performance metric to design the antenna and pulse placement. It is shown that the CRLB derived for two targets is a more appropriate criterion in comparison with the single-target CRLB since the two-target CRLB takes into account both the mainlobe width and sidelobe level of the ambiguity function. In this paper, several algorithms for antenna and pulse selection based on convex and submodular optimization are proposed. Numerical experiments are provided to illustrate the developed theory.

Sparse Signal Bands Selection for Precise Time-based Ranging in Terrestrial Positioning

“…in which F m (θ) denotes the FIM from the m-th band in a twopath channel (see (7)). Eventually, the CRLB of the estimators, with among them the unknown propagation delay of the LoS path, can be derived from (2).…”

“…If the uncertainty in one of the estimators is numerically much larger than one of the others, the optimization will be dominated by the estimators with the larger uncertainty. Thus, we also introduce a user specified compensation weight vector γ [7] to balance the estimators,…”

“…Because of the presence of multiple unknown parameters, the CRLB is no longer a scalar. In order to employ the CRLB in an objective function or constraint in an optimization problem, the minimum eigenvalue constraint (i.e., E-optimality) [6], [7] is chosen here. The errors of the estimators in θ are assumed to be constrained in an origin-centered sphere of radius r e , with a probability larger than p e , which can be written by…”

Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems

“…Then, we employ CVX to solve the convex optimization problems (any off-the-shelf solvers can also be employed). Since the obtained A is continuous, we need a rounding algorithm to obtain a Boolean solution [15] (projecting to the feasible domain). Here, we used the projection technique presented in [6].…”

“…2(q → ∞ stands for an unquantized setting). All the proposed algorithms are quantized in such a way to make the total number of communicated bits equal (based on(15) and (16)).…”