Performance Analysis of Time-Reversal MUSIC

Ciuonzo, Domenico; Romano, Gianmarco; Solimene, Raffaele

doi:10.1109/tsp.2015.2417507

Cited by 123 publications

(77 citation statements)

References 42 publications

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“…The mean-square error (MSE) of the maximum likelihood estimator (MLE) is close to the CRLB when the high signal-to-noise ratio (SNR) is satisfied. It is also worth mentioning that the performance of multiple signal classification (MUSIC) in computational time-reversal (TR) applications is studied in [13,14], where the closed-form MSE matrix of TR-MUSIC is calculated for the single-frequency case in multistatic co-located and non co-located scenarios. Simulation results show that TR-MUSIC can predict a more accurate MSE than CRLB, while it is a sub-optimal estimator since it does not asymptotically achieve the CRLB as the MLE.…”

Section: Introductionmentioning

confidence: 99%

“…Simulation results show that TR-MUSIC can predict a more accurate MSE than CRLB, while it is a sub-optimal estimator since it does not asymptotically achieve the CRLB as the MLE. In the last couple of years, there is a growing interest on the CRLB studies for the target estimation performance of distributed radar networks [11,12,13,14,15,16,17,18,19]. The authors in [11] derive the analytical expressions of CRLB for both noncoherent mode and coherent mode in MIMO radar systems, which shows that the CRLB is inversely proportional to the carrier frequency and signals averaged effective bandwidth.…”

Section: Introductionmentioning

confidence: 99%

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Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment

Shi

Salous

Wang

et al. 2016

Sensors

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This paper investigates the joint target parameter (delay and Doppler) estimation performance of linear frequency modulation (LFM)-based radar networks in a Rice fading environment. The active radar networks are composed of multiple radar transmitters and multichannel receivers placed on moving platforms. First, the log-likelihood function of the received signal for a Rician target is derived, where the received signal scattered off the target comprises of dominant scatterer (DS) component and weak isotropic scatterers (WIS) components. Then, the analytically closed-form expressions of the Cramer-Rao lower bounds (CRLBs) on the Cartesian coordinates of target position and velocity are calculated, which can be adopted as a performance metric to access the target parameter estimation accuracy for LFM-based radar network systems in a Rice fading environment. It is found that the cumulative Fisher information matrix (FIM) is a linear combination of both DS component and WIS components, and it also demonstrates that the joint CRLB is a function of signal-to-noise ratio (SNR), target’s radar cross section (RCS) and transmitted waveform parameters, as well as the relative geometry between the target and the radar network architectures. Finally, numerical results are provided to indicate that the joint target parameter estimation performance of active radar networks can be significantly improved with the exploitation of DS component.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment

Shi

Salous

Wang

et al. 2016

Sensors

View full text Add to dashboard Cite

show abstract

“…Notice that the estimation of EDB is essentially equivalent to the determination of the aliasing number. Afterwards, we calculate the equivalent parameter F p by means of spatial spectrum estimation methods which have been widely used in generic imaging problems [27][28][29]. If there exist redundant channels, the idea of subspace-based methods can be utilized [30,31]; otherwise, we use the Capon estimator [14,21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive reconstruction for azimuth signal of multichannel HRWS SAR imaging system

Zhang¹,

Gao²,

Liu³

2018

EURASIP J. Adv. Signal Process.

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The reconstruction of azimuth signal in multichannel synthetic aperture radar (SAR) for high-resolution and wide-swath (HRWS) imaging requires exact steering vectors. The information of ambiguities and system parameters are used to create the steering vectors. The knowledge of ambiguities involves ambiguity number and index; the system parameters include the pulse repetition frequency (PRF), platform velocity, and channel spacing. However, in some cases, the knowledge of ambiguities is Doppler-variant and there exist errors in the system parameters, which may degrade the reconstruction performance. In this work, an adaptive reconstruction method is developed. Firstly, we discuss the azimuth sampling of multichannel SAR and derive the equation of ambiguity index. We then utilize the azimuth cross-correlation to determine the aliasing number. Afterwards, based on the spatial spectrum estimation methods, an equivalent system parameter is adaptively calculated. With the aliasing number and the equivalent parameter, we can obtain the ambiguity information of each Doppler bin. Therefore, the steering vectors are constructed and the azimuth ambiguities can be suppressed. Compared with state-of-the-art reconstruction methods, our method achieves excellent performance even in highly nonuniform sampling case. Without relying on any system parameters, the proposed method has better practicality and applicability. We conduct extensive experiments including simulations and real data processing to verify the effectiveness and evaluate the performance of the proposed method.

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“…However, theoretical analysis and simulation results show that subspace methods usually exhibit a certain performance loss in estimating locations of highly correlated signals [6]. Results in [7,8] demonstrate that subspace methods may also suffer from performance loss in active localization scenarios. Using the structure of uniform linear array (ULA) array manifold, the authors of [9,10] have proposed an iterative quadratic maximum likelihood (IQML) method to solve the ML problem.…”

Section: Introductionmentioning

confidence: 99%

A Sum-of-Squares and Semidefinite Programming Approach for Maximum Likelihood DOA Estimation

Cai

Zhou

Zhu

2016

Sensors

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Direction of arrival (DOA) estimation using a uniform linear array (ULA) is a classical problem in array signal processing. In this paper, we focus on DOA estimation based on the maximum likelihood (ML) criterion, transform the estimation problem into a novel formulation, named as sum-of-squares (SOS), and then solve it using semidefinite programming (SDP). We first derive the SOS and SDP method for DOA estimation in the scenario of a single source and then extend it under the framework of alternating projection for multiple DOA estimation. The simulations demonstrate that the SOS- and SDP-based algorithms can provide stable and accurate DOA estimation when the number of snapshots is small and the signal-to-noise ratio (SNR) is low. Moveover, it has a higher spatial resolution compared to existing methods based on the ML criterion.

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Performance Analysis of Time-Reversal MUSIC

Cited by 123 publications

References 42 publications

Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment

Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment

Adaptive reconstruction for azimuth signal of multichannel HRWS SAR imaging system

A Sum-of-Squares and Semidefinite Programming Approach for Maximum Likelihood DOA Estimation

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