Performance of MIMO Radar With Angular Diversity Under Swerling Scattering Models

Aittomaki, Tuomas; Koivunen, Visa

doi:10.1109/jstsp.2009.2038971

Cited by 67 publications

(21 citation statements)

References 15 publications

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“…The advantages of multiple-input multiple-output (MIMO) radar have drawn considerable attention in the last decade [1][2][3][4][5][6][7]. MIMO radar systems employ multiple antennas on both the transmit and receive sides.…”

Section: Introductionmentioning

confidence: 99%

LFM-based waveform design for cognitive MIMO radar with constrained bandwidth

Wang

2014

EURASIP J. Adv. Signal Process.

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Waveform design is studied for a cognitive multiple-input multiple-output (MIMO) radar system faced with a combination of additive Gaussian noise and signal dependent clutter. The linear frequency modulation (LFM) signals are employed as transmitted waveforms. Based on the sensed statistics of the target and clutter-plus-noise, assuming the LFM waveforms transmitted at different transmitters can have different starting frequencies and bandwidths, these waveform parameters are designed to maximize the signal-to-clutter-plus-noise ratio at the receiver of the cognitive MIMO radar system. The constraints of the allowable range of operating frequency and total transmit energy are considered. We show that in the tested examples, the designed waveforms are nonorthogonal which leads to superior performance compared with that of the frequency spread LFM waveforms commonly used in the traditional MIMO radar systems.

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Section: Introductionmentioning

confidence: 99%

LFM-based waveform design for cognitive MIMO radar with constrained bandwidth

Wang

2014

EURASIP J. Adv. Signal Process.

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“…In particular, the calculation of diversity gain typically shows that multiple looks tend to increase the diversity gain. This seems reasonable for large enough SCNR, where each individual look should help in making a correct decision [2]. Of course, no scalar performance measure can completely describe everything about performance as we change multiple parameters (SCNR, probability of false alarm).…”

Section: Introductionmentioning

confidence: 99%

MIMO Signal Detection Using Neyman Pearson Signal Detection

Kamble¹,

Manjare²

2015

International Journal of Innovative Research in Electrical, Ele

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Abstract:In current scenario, the demand for wireless communication is increasing drastically. A wireless system has number of advantages over its wired counterpart including allowing a communication link to be set up quickly without the difficulty and expense of installing data transmission lines. The wireless communications industry has experienced an explosive growth in the last decade. One of the most promising spectrums an efficient technique is multiple-inputmultiple-output (MIMO) systems that employ multiple transmits and receives antennas. The multiple inputs multiple outputs (MIMO) radar system transmits M antennas and receives N antennas. In this proposed system first step can be initially derive the diversity gain for a signal present versus signal absent scalar hypothesis test statistic and for a vector signal present versus vector signal absent hypothesis test. The MIMO radar system, used to detect a target composed of Q random scatterers with possibly non-Gaussian reflection coefficients in the presence of possibly non-Gaussian clutter-plus-noise. Diversity gain for the MIMO radar system is dependent on the cumulative distribution function (CDF). In this maximum possible diversity gain can be achieved for non orthogonal waveforms.

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“…MIMO radar continues to receive attention [4]- [15]. The application of MIMO techniques to skywave OTH (MIMO-OTH) radar brings new opportunities and challenges to OTH radar system design.…”

Section: Introductionmentioning

confidence: 99%

Signal model and detection performance for MIMO-OTH radar with multipath ionospheric propagation and non-point targets

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Taking into account the existence of multipath ionospheric propagation (MIP), this paper develops the received signal model for a non-point target for multiple-input multipleoutput skywave over-the-horizon (MIMO-OTH) radar for the first time. The model describes the ionospheric state, the number of propagation paths between a radar antenna and the target center, as well as the statistics of the reflection coefficients. It is shown that varying system parameters, such as antenna positions and signal frequencies, can result in causing the model to change from a case with highly correlated reflection coefficients to a case with virtually uncorrelated reflection coefficients. The proposed model is used to solve a target detection problem. It is shown that it is possible to exploit the MIP to improve the detection performance of the MIMO-OTH radar.Index Terms-Detection, multipath, non-point target.1. INTRODUCTION Skywave over-the-horizon (OTH) radar employs ionospheric reflection of signals to detect targets beyond visual range. The performance of the OTH radar system relies heavily on the state of the ionosphere. The commonly used models for describing the ionospheric state usually divide the ionosphere into several layers. These models include the international reference ionosphere (IRI) model [1], the Chapman ionosphere model [2], and the multi-quasi-parabolic (MQP) ionospheric model [3], etc. In OTH radar, transmit signals can travel through different ionospheric layers to reach the target depending on their frequencies, and the signal which bounces off the target can also travel through different ionospheric layers before reaching the receivers, leading to multiple propagation paths, a phenomenon called multipath ionospheric propagation (MIP). In traditional OTH radar systems, approaches have been proposed to attempt to eliminate MIP, for example, by selectively choosing the operational frequency, employing a transmit/receive array with higher angular reso-

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Performance of MIMO Radar With Angular Diversity Under Swerling Scattering Models

Cited by 67 publications

References 15 publications

LFM-based waveform design for cognitive MIMO radar with constrained bandwidth

LFM-based waveform design for cognitive MIMO radar with constrained bandwidth

MIMO Signal Detection Using Neyman Pearson Signal Detection

Signal model and detection performance for MIMO-OTH radar with multipath ionospheric propagation and non-point targets

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