Conditional and Unconditional Cramér-Rao Bounds for Near-Field Localization in Bistatic MIMO Radar Systems

Khamidullina, Liana; Podkurkov, Ivan; Haardt, Martin

doi:10.1109/tsp.2021.3082469

Cited by 17 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rank-(M, N, •) decomposition [13], also known as type-2 BTD (i.e., with Tucker-2 blocks), and coupled LL1 decomposition were used in [20] and [21] to perform target localization in multi-pulse, multi-array MIMO radar, with and without training waveforms, respectively. [21] was recently extended to near-field targets in [22]. The direction-finding (DF) problem in L-shaped electromagnetic vector sensor arrays with timevarying polarization state (i.e., partial or incomplete polarization) was also solved with the aid of type-2 BTD modeling in [23,24].…”

Section: Cpd (Sum Of Rank-1 Terms) and Tkd (Only One Term Of Low Mult...mentioning

confidence: 99%

Block-Term Tensor Decomposition in Array Signalprocessing

Kofidis

2024

Preprint

View full text Add to dashboard Cite

Section: Cpd (Sum Of Rank-1 Terms) and Tkd (Only One Term Of Low Mult...mentioning

confidence: 99%

Block-Term Tensor Decomposition in Array Signalprocessing

Kofidis

2024

Preprint

View full text Add to dashboard Cite

“…By substituting these terms into (38) and (39), the expressions in (40) and ( 41) can be obtained accordingly.…”

Section: Dt Rmentioning

confidence: 99%

“…Moreover, most existing results are derived for the source localization problem that involves only one-hop signal propagation, which cannot be applied for radar sensing scenario with double-hop signal propagation. In [40], the authors derived the conditional and unconditional CRBs for near-field bistatic MIMO radar system. However, such results were dependent on the derivative of the path difference with respect to unknown parameters, which is difficult to gain insights between the CRBs and the key system parameters or array configuration.…”

Section: Introductionmentioning

confidence: 99%

SNR Scaling Laws for Radio Sensing with Extremely Large-Scale MIMO

Wang

Zeng

2022

2022 IEEE International Conference on Communications Workshops (ICC Workshops)

View full text Add to dashboard Cite

Mobile communication networks were designed to mainly support ubiquitous wireless communications, yet they are also expected to achieve radio sensing capabilities in the near future. However, most prior studies on radio sensing usually rely on far-field assumption with uniform plane wave (UPW) models. With the ever-increasing antenna size, together with the growing demands to sense nearby targets, the conventional far-field UPW assumption may become invalid. Therefore, this paper studies near-field radio sensing with extremely large-scale (XL) antenna arrays, where the more general uniform spheric wave (USW) sensing model is considered. Closed-form expressions of the Cramér-Rao Bounds (CRBs) for both angle and range estimations are derived for near-field XL-MIMO radar mode and XL-phased array radar mode, respectively. Our results reveal that different from the conventional UPW model where the CRB for angle decreases unboundedly as the number of antennas increases, for XL-MIMO radar-based near-field sensing, the CRB decreases with diminishing return and approaches to a certain limit as the number of antennas increases. Besides, different from the farfield model where the CRB for range is infinity since it has no range estimation capability, that for the near-field case is finite. Furthermore, it is revealed that the commonly used spherical wave model based on second-order Taylor approximation is insufficient for near-field CRB analysis. Extensive simulation results are provided to validate our derived CRBs.

show abstract

“…Using the accurate spherical wavefront model, tensor-based NF source localization algorithms were proposed in [13] and [14], by extracting the angle and range parameters from the estimates of the steering matrices, with improved estimation accuracy as compared to Fresnel approximation adopted in [8,9]. In [15], the conditional and unconditional Cramer-Rao bounds (CRBs) for NF source parameter estimation are analyzed based on the exact spherical wavefront model for bistatic MIMO radar systems.…”

Section: Introductionmentioning

confidence: 99%

An Exact Near-Field Model Based Localization for Bistatic MIMO Radar With COLD Arrays

Chen

Wang

Liu

et al. 2023

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Most existing near-field (NF) source localization algorithms are developed based on the Fresnel approximation model, and assume that the spatial amplitudes of the target at the sensors are equal. Unlike these algorithms, an NF source parameter estimation algorithm is proposed, based on the exact spatial propagation geometry model, for bistatic multiple-input multiple-output (MIMO) radar deployed with a linear concentered orthogonal loop and dipole (COLD) array at both the transmitter and receiver. The proposed method first compresses the output signal of the matched filter at the receiver into a third-order parallel factor (PARAFAC) data model, on which a trilinear decomposition is performed, and subsequently three factor matrices can be obtained. Then, multiple parameters of interest, including direction-of-departure (DOD), direction-ofarrival (DOA), range from transmitter to target (RFTT), range from target to receiver (RFTR), two-dimensional (2-D) transmit polarization angle (TPA) and 2-D receive polarization angle (R-PA), are estimated from the spatial amplitude ratio exploiting the rotation invariant property and the Khatri-Rao product. Finally, the phase uncertainties of transmit and receive arrays can be extracted from additional phase items. The proposed algorithm avoids spectrum peak search, and the estimated parameters in closed forms can be automatically matched unambiguously. In addition, it is suitable for non-uniform linear arrays (NLA) with arbitrary array element spacing and phase uncertainty. Advantages of the proposed method are demonstrated by simulation results.

show abstract

Conditional and Unconditional Cramér-Rao Bounds for Near-Field Localization in Bistatic MIMO Radar Systems

Cited by 17 publications

References 34 publications

Block-Term Tensor Decomposition in Array Signalprocessing

Block-Term Tensor Decomposition in Array Signalprocessing

SNR Scaling Laws for Radio Sensing with Extremely Large-Scale MIMO

An Exact Near-Field Model Based Localization for Bistatic MIMO Radar With COLD Arrays

Contact Info

Product

Resources

About