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

Abstract

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.

Electromagnetic informed data model considerations for near-field DOA and range estimates

“…However, to the best of our knowledge, most near-field localization methods do not consider critical effects that are not prominent in the near-field on the signal model. Instead, they use a so-called “ exact model ” that is actually based on the behaviour of far-field electromagnetic waves 16 , 17 , 30 , 31 . The phase of the far-field electromagnetic wave is proportional to the product of the distance and inverse of the wavelength 32 .…”

“…A limited number of works, such as 7 , 16 , 17 , 30 , have highlighted algorithms based on the exact model. However, these algorithms are only useful for particular antenna configurations, such as bistatic multiple-input multiple-output (MIMO) structure, and are unsuitable for other antenna arrays.…”

“…This way, we show the importance of considering the near-field effects and the fact that whereas the assumptions of the approximated model might be sufficient in the far-field, in the near-field, they produce highly inaccurate results. Hence, in the near-field, we investigate the accuracy of assumptions made in creating the approximated model 17 . We employ the MUSIC algorithm 8 , which is suitable for any array structure, to estimate the range and DOA using both exact and approximated models.…”

Sparse Reconstruction-Based Time-Delay Estimation Algorithm in the Exponential Correlation Domain

“…Moreover, it has emerged as a crucial area of research in related fields owing to the challenges posed by complex environments in multitarget time-delay estimation. The most traditional and effective algorithm for time-delay estimation is the correlation function-based algorithm, which utilizes a renowned matched filter (MF) [6]- [9]. This class of algorithms has the advantages of low computational complexity and high output signal-to-noise ratios (SNRs).…”