1,538 research outputs found
Robust Adaptive LCMV Beamformer Based On An Iterative Suboptimal Solution
The main drawback of closed-form solution of linearly constrained minimum variance (CF-LCMV) beamformer is the dilemma of acquiring long observation time for stable covariance matrix estimates and short observation time to track dynamic behavior of targets, leading to poor performance including low signal-noise-ratio (SNR), low jammer-to-noise ratios (JNRs) and small number of snapshots. Additionally, CF-LCMV suffers from heavy computational burden which mainly comes from two matrix inverse operations for computing the optimal weight vector. In this paper, we derive a low-complexity Robust Adaptive LCMV beamformer based on an Iterative Suboptimal solution (RAIS-LCMV) using conjugate gradient (CG) optimization method. The merit of our proposed method is threefold. Firstly, RAIS-LCMV beamformer can reduce the complexity of CF-LCMV remarkably. Secondly, RAIS-LCMV beamformer can adjust output adaptively based on measurement and its convergence speed is comparable. Finally, RAIS-LCMV algorithm has robust performance against low SNR, JNRs, and small number of snapshots. Simulation results demonstrate the superiority of our proposed algorithms
MU-MIMO Communications with MIMO Radar: From Co-existence to Joint Transmission
Beamforming techniques are proposed for a joint multi-input-multi-output
(MIMO) radar-communication (RadCom) system, where a single device acts both as
a radar and a communication base station (BS) by simultaneously communicating
with downlink users and detecting radar targets. Two operational options are
considered, where we first split the antennas into two groups, one for radar
and the other for communication. Under this deployment, the radar signal is
designed to fall into the null-space of the downlink channel. The communication
beamformer is optimized such that the beampattern obtained matches the radar's
beampattern while satisfying the communication performance requirements. To
reduce the optimizations' constraints, we consider a second operational option,
where all the antennas transmit a joint waveform that is shared by both radar
and communications. In this case, we formulate an appropriate probing
beampattern, while guaranteeing the performance of the downlink communications.
By incorporating the SINR constraints into objective functions as penalty
terms, we further simplify the original beamforming designs to weighted
optimizations, and solve them by efficient manifold algorithms. Numerical
results show that the shared deployment outperforms the separated case
significantly, and the proposed weighted optimizations achieve a similar
performance to the original optimizations, despite their significantly lower
computational complexity.Comment: 15 pages, 15 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
In this work, we propose a subspace-based algorithm for DOA estimation which
iteratively reduces the disturbance factors of the estimated data covariance
matrix and incorporates prior knowledge which is gradually obtained on line. An
analysis of the MSE of the reshaped data covariance matrix is carried out along
with comparisons between computational complexities of the proposed and
existing algorithms. Simulations focusing on closely-spaced sources, where they
are uncorrelated and correlated, illustrate the improvements achieved.Comment: 7 figures. arXiv admin note: text overlap with arXiv:1703.1052
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