System Optimization for Temporal Correlated Cognitive Radar with EBPSK-Based MCPC Signal

Chen, Peng; Wu, Lenan

doi:10.1155/2015/302083

Cited by 6 publications

(7 citation statements)

References 32 publications

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“…When the method based on maximum a posteriori probability (MAP) is adopted to estimate TIR, which is TSC [14] or target frequency response [9] in frequency domain, the estimated TSC for the m th target can be expressed as

{\hat{g}}_{m, k} = {bold-italicQ}_{m, k} {bold-italicr}_{m . k},

where

{bold-italicQ}_{m, k} ≜ {()(, Z_{k}^{H} R_{w}^{- 1} {bold-italicZ}_{k} + R_{m, g}^{- 1})}^{- 1} Z_{k}^{H} R_{w}^{- 1}

is the received filter based on MAP. For the m th target, the MSE matrix of MAP estimation is

\begin{matrix} right leftthickmathspace.5em \\ N_{m, 1} & ≜ E \{({\hat{g}}_{m, k} - g_{m, k}) {()(, {\overset{false^}{bold-italicg}}_{m, k} - {bold-italicg}_{m, k})}^{H}\} \\ = Q_{m, k} (Z_{k} R_{m, g} {bold-italicZ}_{k}^{normalH} + R_{w}) {bold-italicQ}_{m, k}^{normalH<...>} \end{matrix}

…”

Section: Tsc Estimation For Multiple Extended Targets Separated In mentioning

confidence: 99%

“…Additional, TIR is usually used to describe the extended target [7, 11], which occupies more than one resolution cells instead of a point [12, 13]. However, when TIR during pulse repetition interval (PRI) is temporally correlated, a more appropriate model: namely, wide sense stationary uncorrelated scattering (WSSUS) model, is proposed in [5, 14–16] to model the extended target, where an exponentially correlated expression is utilised to describe this type of extended target.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Waveform design for multiple extended targets in temporally correlated cognitive radar system

Chen

2016

IET Radar, Sonar & Navigation

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{\hat{g}}_{m, k} = {bold-italicQ}_{m, k} {bold-italicr}_{m . k},

where

{bold-italicQ}_{m, k} ≜ {()(, Z_{k}^{H} R_{w}^{- 1} {bold-italicZ}_{k} + R_{m, g}^{- 1})}^{- 1} Z_{k}^{H} R_{w}^{- 1}

is the received filter based on MAP. For the m th target, the MSE matrix of MAP estimation is

\begin{matrix} right leftthickmathspace.5em \\ N_{m, 1} & ≜ E \{({\hat{g}}_{m, k} - g_{m, k}) {()(, {\overset{false^}{bold-italicg}}_{m, k} - {bold-italicg}_{m, k})}^{H}\} \\ = Q_{m, k} (Z_{k} R_{m, g} {bold-italicZ}_{k}^{normalH} + R_{w}) {bold-italicQ}_{m, k}^{normalH<...>} \end{matrix}

…”

Section: Tsc Estimation For Multiple Extended Targets Separated In mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Waveform design for multiple extended targets in temporally correlated cognitive radar system

Chen

2016

IET Radar, Sonar & Navigation

Self Cite

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“…e orthogonal waveforms are transmitted by multiple-input and multiple-output (MIMO) radar systems [1][2][3][4][5][6], so the performance of target detection, estimation, and tracking can be improved by the waveform diversity. Generally, the MIMO radar system can be classified into two types: (1) the colocated MIMO radars [7][8][9], where the distance between antennas is comparable with the wavelength, and the waveform diversity is exploited to improve the target estimation performance with the large virtual aperture; (2) the distributed MIMO radar [10][11][12][13], where the spacing between antennas is large, and the different view-angles from the antennas to targets are used to improve the detection performance with the diversity of radar cross-section (RCS). Since the direction of arrival (DOA) estimation problem is addressed in this paper, the colocated MIMO radar is adopted to improve the DOA estimation performance with larger virtual aperture than that in traditional phased array [14].…”

Section: Introductionmentioning

confidence: 99%

A Super-Resolution DOA Estimation Method for Fast-Moving Targets in MIMO Radar

Liu

Tang

Bai

et al. 2020

Mathematical Problems in Engineering

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Direction of arrival (DOA) estimation is an essential problem in the radar systems. In this paper, the problem of DOA estimation is addressed in the multiple-input and multiple-output (MIMO) radar system for the fast-moving targets. A virtual aperture is provided by orthogonal waveforms in the MIMO radar to improve the DOA estimation performance. Different from the existing methods, we consider the DOA estimation method with only one snapshot for the fast-moving targets and achieve the superresolution estimation from the snapshot. Based on a least absolute shrinkage and selection operator (LASSO), a denoise method is formulated to obtain a sparse approximation to the received signals, where the sparsity is measured by a new type of atomic norm for the MIMO radar system. However, the denoise problem cannot be solved efficiently. en, by deriving the dual norm of the new atomic norm, a semidefinite matrix is constructed from the denoise problem to formulate a semidefinite problem with the dual optimization problem. Finally, the DOA is estimated by peak-searching the spatial spectrum. Simulation results show that the proposed method achieves better performance of the DOA estimation in the MIMO radar system with only one snapshot.(DFT) [19] is used to estimate the DOA, where the received signals are sampled by the antennas in the spatial domain, and then the DOA estimation is equal to a corresponding frequency estimation in the transformed domain. erefore, the frequency (DOA) in the spatial domain is obtained by the DFT methods, but the resolution of DFT method is limited by Rayleigh criterion. e methods that can break through the Rayleigh criterion are called super-resolution methods. Multiple signal classification (MUSIC) method [20][21][22], Root-MUSIC [23], and the estimation of signal parameters via rotational invariant techniques (ESPRIT) method [24][25][26] are three most essential super-resolution methods. e noise subspace and signal subspace are obtained in the MUSIC and ESPRIT methods to estimate the DOA, respectively. A TOD-MUSIC algorithm is proposed in [27] to estimate the DOA in the scenario with low signal-to-noise ratio (SNR) with diversity bistatic MIMO radar.However, the subspaces are obtained from the estimated covariance matrix of the received signals, so the multiple measurements are needed in MUSIC and ESPRIT methods to achieve a reasonable estimation of the covariance matrix.

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“…Then, the virtual aperture is provided in the MIMO radar system and improves the radar performance in the target detection, estimation, and tracking [4,5]. The MIMO radar systems usually have two types: the colocated MIMO radar with the distance between antennas being comparable with the wavelength [6,7], and the distributed MIMO radar having larger distance between antennas [8][9][10]. Compared with the distributed MIMO radar, the colocated MIMO radar has better characteristics in terms of system synchronization and waveform diversity [11].…”

Section: Introductionmentioning

confidence: 99%

A Sparse Bayesian Learning-Based DOA Estimation Method With the Kalman Filter in MIMO Radar

et al. 2020

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The direction of arrival (DOA) estimation problem as an essential problem in the radar system is important in radar applications. In this paper, considering a multiple-input and multiple-out (MIMO) radar system, the DOA estimation problem is investigated in the scenario with fast-moving targets. The system model is first formulated, and then by exploiting both the target sparsity in the spatial domain and the temporal correlation of the moving targets, a sparse Bayesian learning (SBL)-based DOA estimation method combined with the Kalman filter (KF) is proposed. Moreover, the performances of traditional sparse-based methods are limited by the off-grid issue, and Taylor-expansion off-grid methods also have high computational complexity and limited performance. The proposed method breaks through the off-grid limit by transforming the problem in the spatial domain to that in the time domain using the movement feature. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation problem for the fast-moving targets.

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System Optimization for Temporal Correlated Cognitive Radar with EBPSK-Based MCPC Signal

Cited by 6 publications

References 32 publications

Waveform design for multiple extended targets in temporally correlated cognitive radar system

Waveform design for multiple extended targets in temporally correlated cognitive radar system

A Super-Resolution DOA Estimation Method for Fast-Moving Targets in MIMO Radar

A Sparse Bayesian Learning-Based DOA Estimation Method With the Kalman Filter in MIMO Radar

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