Target Detection and Localization Using MIMO Radars and Sonars

Bekkerman, I.; Tabrikian, Joseph

doi:10.1109/tsp.2006.879267

Cited by 1,051 publications

(547 citation statements)

References 9 publications

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“…Radar and sonar have been widely used for civilian, military, and scientific purposes, and the range of applications is large (Minkoff, 1992). The applications of sonar and radar systems are separated into active (Bekkerman and Tabrikian, 2006) and passive system (Carter, 1981) problems. Active sonars and radars transmit signals that are reflected back from targets (i.e., an echo), whereas passive sonars and radars do not transmit and only receive signals transmitted by sources (Stergiopoulos, 2000).…”

Section: A Radar and Sonarmentioning

confidence: 99%

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

127

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Locating the position of fixed or mobile sources (i.e., transmitters) based on measurements obtained from sensors (i.e., receivers) is an important research area that is attracting much interest. In this paper, we review several representative localization algorithms that use time of arrivals (TOAs) and time difference of arrivals (TDOAs) to achieve high signal source position estimation accuracy when a transmitter is in the line-of-sight of a receiver. Circular (TOA) and hyperbolic (TDOA) position estimation approaches both use nonlinear equations that relate the known locations of receivers and unknown locations of transmitters. Estimation of the location of transmitters using the standard nonlinear equations may not be very accurate because of receiver location errors, receiver measurement errors, and computational efficiency challenges that result in high computational burdens. Least squares and maximum likelihood based algorithms have become the most popular computational approaches to transmitter location estimation. In this paper, we summarize the computational characteristics and position estimation accuracies of various positioning algorithms. By improving methods for estimating the time-of-arrival of transmissions at receivers and transmitter location estimation algorithms, transmitter location estimation may be applied across a range of applications

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Section: A Radar and Sonarmentioning

confidence: 99%

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

127

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“…We think this target model can be used to model the target of colocated MIMO radar [1,6]. Suppose the transmitting steering vector of MIMO radar is v t , which is a M × 1 vector, and the receiving steering vector is v r , which is a N × 1 vector, thenH s can be written asH s = γv t v H r , where γ is a scalar.…”

Section: Relative Entropy and Target Modelsmentioning

confidence: 99%

“…Multiple-input multiple-output (MIMO) radar is a new technology emerging in recent years [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. A significant difference between MIMO radar and standard phased-array radar is that phased-array radar transmitters transmit coherent waveforms, i.e., one waveform is a scaled version of another waveform, while the waveforms of MIMO radar can be diverse.…”

Section: Introductionmentioning

confidence: 99%

“…zero mean complex Gaussian random variables [7,13]. Most colocated MIMO radar systems considered a scattering matrix whose entries are determined by the transmitting steering vector and the receiving steering vector [6,10,11]. In this paper, we will propose a more general target model which is applicable to both distributed MIMO radar and colocated MIMO radar.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Target models and waveform design for detection in MIMO radar

Tang

Luo

Tang

et al. 2012

Sci. China Inf. Sci.

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In this paper, we propose a general target model for multiple-input multiple-output (MIMO) radar. Two types of targets are presented. For each type of target, we develop a corresponding optimum waveform designing method to optimize detection performance. Based on Chernoff-Stein lemma, we use relative entropy as the performance measure. We show that the optimum waveform designing method can obtain better detection performance than traditional orthogonal waveform designing method.Keywords MIMO radar, waveform designing, relative entropy CitationTang J, Luo J, Tang B, et al. Target models and waveform design for detection in MIMO radar. Sci

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“…MIMO radar is categorized into two classes: statistical MIMO radar and colocated MIMO radar, depending on their antenna placement [1][2][3][4][5][6]. The advantages of MIMO radar with collocated antennas have been studied extensively, which include improved detection performance and higher resolution [7], higher sensitivity for detecting moving targets [8,9], and increased degrees of freedom for transmission beamforming [10]. MIMO radar with widely separated antennas can capture the spatial diversity of the target's radar cross section (RCS) [11].…”

Section: Introductionmentioning

confidence: 99%

Low-Grazing Angle Target Detection and System Configuration of Mimo Radar

Ding¹,

Chen²,

Wang³

et al. 2013

PIER B

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Abstract-In this paper, we focus on target detection and system configuration optimization of Multiple-input Multiple-output (MIMO) radar in low-grazing angle, where the multipath effects are very abundant. The performance of detection can be improved via utilizing the multipath echoes. First, the reflection coefficient, considering the curved earth effect, is derived. Then, the general signal model for MIMO radar is introduced for low-grazing angle. Using the NeymanPearson sense, the detector of MIMO radar with multipath is analyzed. We use the deflection coefficient as a criterion of system configuration both for MIMO radar and phased-array radar. The simulation results show that the performance can be enhanced markedly when the multipath effects are considered, and the optimal configuration of phased-array radar is with the same number of transmitters as that of receivers, however, the optimal configuration of MIMO radar depends on the signal-to-noise ratio (SNR).

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Target Detection and Localization Using MIMO Radars and Sonars

Cited by 1,051 publications

References 9 publications

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Target models and waveform design for detection in MIMO radar

Low-Grazing Angle Target Detection and System Configuration of Mimo Radar

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