15 research outputs found
Overlapped-MIMO Radar Waveform Design for Coexistence With Communication Systems
This paper explores an overlapped-multiple-input multiple-output (MIMO)
antenna architecture and a spectrum sharing algorithm via null space projection
(NSP) for radar-communications coexistence. In the overlapped-MIMO
architecture, the transmit array of a collocated MIMO radar is partitioned into
a number of subarrays that are allowed to overlap. Each of the antenna elements
in these subarrays have signals orthogonal to each other and to the elements of
the other subarrays. The proposed architecture not only improves sidelobe
suppression to reduce interference to communications system, but also enjoys
the advantages of MIMO radar without sacrificing the main desirable
characteristics. The radar-centric spectrum sharing algorithm then projects the
radar signal onto the null space of the communications system's interference
channel, which helps to avoid interference from the radar. Numerical results
are presented which show the performance of the proposed waveform design
algorithm in terms of overall beampattern and sidelobe levels of the radar
waveform and finally shows a comparison of the proposed system with existing
collocated MIMO radar architectures.Comment: accepted at IEEE WCN
Measurement Matrix Design for Compressive Sensing Based MIMO Radar
In colocated multiple-input multiple-output (MIMO) radar using compressive
sensing (CS), a receive node compresses its received signal via a linear
transformation, referred to as measurement matrix. The samples are subsequently
forwarded to a fusion center, where an L1-optimization problem is formulated
and solved for target information. CS-based MIMO radar exploits the target
sparsity in the angle-Doppler-range space and thus achieves the high
localization performance of traditional MIMO radar but with many fewer
measurements. The measurement matrix is vital for CS recovery performance. This
paper considers the design of measurement matrices that achieve an optimality
criterion that depends on the coherence of the sensing matrix (CSM) and/or
signal-to-interference ratio (SIR). The first approach minimizes a performance
penalty that is a linear combination of CSM and the inverse SIR. The second one
imposes a structure on the measurement matrix and determines the parameters
involved so that the SIR is enhanced. Depending on the transmit waveforms, the
second approach can significantly improve SIR, while maintaining CSM comparable
to that of the Gaussian random measurement matrix (GRMM). Simulations indicate
that the proposed measurement matrices can improve detection accuracy as
compared to a GRMM
Multi-stage Antenna Selection for Adaptive Beamforming in MIMO Arrays
Increasing the number of transmit and receive elements in
multiple-input-multiple-output (MIMO) antenna arrays imposes a substantial
increase in hardware and computational costs. We mitigate this problem by
employing a reconfigurable MIMO array where large transmit and receive arrays
are multiplexed in a smaller set of k baseband signals. We consider four stages
for the MIMO array configuration and propose four different selection
strategies to offer dimensionality reduction in post-processing and achieve
hardware cost reduction in digital signal processing (DSP) and radio-frequency
(RF) stages. We define the problem as a determinant maximization and develop a
unified formulation to decouple the joint problem and select antennas/elements
in various stages in one integrated problem. We then analyze the performance of
the proposed selection approaches and prove that, in terms of the output SINR,
a joint transmit-receive selection method performs best followed by
matched-filter, hybrid and factored selection methods. The theoretical results
are validated numerically, demonstrating that all methods allow an excellent
trade-off between performance and cost.Comment: Submitted for publicatio
Game theoretic analysis for MIMO radars with multiple targets
This paper considers a distributed beamforming
and resource allocation technique for a radar system in the
presence of multiple targets. The primary objective of each
radar is to minimize its transmission power while attaining an
optimal beamforming strategy and satisfying a certain detection
criterion for each of the targets. Therefore, we use convex
optimization methods together with noncooperative and partially
cooperative game theoretic approaches. Initially, we consider
a strategic noncooperative game (SNG), where there is no
communication between the various radars of the system. Hence
each radar selfishly determines its optimal beamforming and
power allocation. Subsequently, we assume a more coordinated
game theoretic approach incorporating a pricing mechanism.
Introducing a price in the utility function of each radar/player,
enforces beamformers to minimize the interference induced to
other radars and to increase the social fairness of the system.
Furthermore, we formulate a Stackelberg game by adding a
surveillance radar to the system model, which will play the role of
the leader, and hence the remaining radars will be the followers.
The leader applies a pricing policy of interference charged to the followers aiming at maximizing his profit while keeping the
incoming interference under a certain threshold. We also present
a proof of the existence and uniqueness of the Nash Equilibrium
(NE) in both the partially cooperative and noncooperative games.
Finally, the simulation results confirm the convergence of the
algorithm in all three cases
Optimal Antenna Allocation in MIMO Radars with Collocated Antennas
This paper concerns with the sensor management problem in collocated
Multiple-Input Multiple-Output (MIMO) radars. After deriving the Cramer-Rao
Lower Bound (CRLB) as a performance measure, the antenna allocation problem is
formulated as a standard Semi-definite Programming (SDP) for the single-target
case. In addition, for multiple unresolved target scenarios, a sampling-based
algorithm is proposed to deal with the non-convexity of the cost function.
Simulations confirm the superiority of the localization results under the
optimal structure.Comment: Submitted to IEEE Transactions on Aerospace and Electronic System
Target localization in MIMO radar systems
MIMO (Multiple-Input Multiple-Output) radar systems employ multiple antennas to transmit multiple waveforms and engage in joint processing of the received echoes from the target. MIMO radar has been receiving increasing attention in recent years from researchers, practitioners, and funding agencies. Elements of MIMO radar have the ability to transmit diverse waveforms ranging from independent to fully correlated. MIMO radar offers a new paradigm for signal processing research. In this dissertation, target localization accuracy performance, attainable by the use of MIMO radar systems, configured with multiple transmit and receive sensors, widely distributed over an area, are studied. The Cramer-Rao lower bound (CRLB) for target localization accuracy is developed for both coherent and noncoherent processing. The CRLB is shown to be inversely proportional to the signal effective bandwidth in the noncoherent case, but is approximately inversely proportional to the carrier frequency in the coherent case. It is shown that optimization over the sensors\u27 positions lowers the CRLB by a factor equal to the product of the number of transmitting and receiving sensors. The best linear unbiased estimator (BLUE) is derived for the MIMO target localization problem. The BLUE\u27s utility is in providing a closed-form localization estimate that facilitates the analysis of the relations between sensors locations, target location, and localization accuracy. Geometric dilution of precision (GDOP) contours are used to map the relative performance accuracy for a given layout of radars over a given geographic area. Coherent processing advantage for target localization relies on time and phase synchronization between transmitting and receiving radars. An analysis of the sensitivity of the localization performance with respect to the variance of phase synchronization error is provided by deriving the hybrid CRLB. The single target case is extended to the evaluation of multiple target localization performance. Thus far, the analysis assumes a stationary target. Study of moving target tracking capabilities is offered through the use of the Bayesian CRLB for the estimation of both target location and velocity. Centralized and decentralized tracking algorithms, inherit to distributed MIMO radar architecture, are proposed and evaluated. It is shown that communication requirements and processing load may be reduced at a relatively low performance cost