3,738 research outputs found
MIMO Radar Waveform Optimization With Prior Information of the Extended Target and Clutter
The concept of multiple-input multiple-output (MIMO) radar allows each transmitting antenna element to transmit an arbitrary waveform. This provides extra degrees of freedom compared to the traditional transmit beamforming approach. It has been shown in the recent literature that MIMO radar systems have many advantages. In this paper, we consider the joint optimization of waveforms and receiving filters in the MIMO radar for the case of extended target in clutter. A novel iterative algorithm is proposed to optimize the waveforms and receiving filters such that the detection performance can be maximized. The corresponding iterative algorithms are also developed for the case where only the statistics or the uncertainty set of the target impulse response is available. These algorithms guarantee that the SINR performance improves in each iteration step. Numerical results show that the proposed methods have better SINR performance than existing design methods
Robust Design of Transmit Waveform and Receive Filter For Colocated MIMO Radar
We consider the problem of angle-robust joint transmit waveform and receive
filter design for colocated Multiple-Input Multiple-Output (MIMO) radar, in the
presence of signal-dependent interferences. The design problem is cast as a
max-min optimization problem to maximize the worst-case output
signal-to-interference-plus-noise-ratio (SINR) with respect to the unknown
angle of the target of interest. Based on rank-one relaxation and semi-definite
programming (SDP) representation of a nonnegative trigonometric polynomial, a
cyclic optimization algorithm is proposed to tackle this problem. The
effectiveness of the proposed method is illustrated via numerical examples.Comment: 6 pages, 13 figures, part of this work was submitted to IEEE Signal
Processing Letters; (short introduction; typos corrected; revised statement
in section III-B and IV; revised figure labels
Adaptive Interference Removal for Un-coordinated Radar/Communication Co-existence
Most existing approaches to co-existing communication/radar systems assume
that the radar and communication systems are coordinated, i.e., they share
information, such as relative position, transmitted waveforms and channel
state. In this paper, we consider an un-coordinated scenario where a
communication receiver is to operate in the presence of a number of radars, of
which only a sub-set may be active, which poses the problem of estimating the
active waveforms and the relevant parameters thereof, so as to cancel them
prior to demodulation. Two algorithms are proposed for such a joint waveform
estimation/data demodulation problem, both exploiting sparsity of a proper
representation of the interference and of the vector containing the errors of
the data block, so as to implement an iterative joint interference removal/data
demodulation process. The former algorithm is based on classical on-grid
compressed sensing (CS), while the latter forces an atomic norm (AN)
constraint: in both cases the radar parameters and the communication
demodulation errors can be estimated by solving a convex problem. We also
propose a way to improve the efficiency of the AN-based algorithm. The
performance of these algorithms are demonstrated through extensive simulations,
taking into account a variety of conditions concerning both the interferers and
the respective channel states
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