This paper presents a novel algorithm for the localization of mixed far-field sources (FFSs) and near-field sources (NFSs) without estimating the source number. Firstly, the algorithm decouples the direction-of-arrival (DOA) estimation from the range estimation by exploiting fourth-order spatial-temporal cumulants of the observed data. Based on the joint diagonalization structure of multiple spatial-temporal cumulant matrices, a new one-dimensional (1-D) spatial spectrum function is derived to generate the DOA estimates of both FFSs and NFSs. Then, the FFSs and NFSs are identified and the range parameters of NFSs are determined via beamforming technique. Compared with traditional mixed sources localization algorithms, the proposed algorithm avoids the performance deterioration induced by erroneous source number estimation. Furthermore, it has a higher resolution capability and improves the estimation accuracy. Computer simulations are implemented to verify the effectiveness of the proposed algorithm.

Narrow-band interference (NBI) and Wide-band interference (WBI) are critical issues for synthetic aperture radar (SAR), which degrades the imaging quality severely. Since some complex signals can be modeled as linear frequency modulated (LFM) signals within a short time, LFM-WBI and NBI are mainly discussed in this paper. Due to its excellent energy concentration and useful properties (i.e., auto-terms pass through the origin of Delay-Doppler plane while cross-terms are away from it), a novel nonparametric interference suppression method using Delay-Doppler iterative decomposition algorithm is proposed. This algorithm consists of three stages. First, we present signal synthesis method (SSM) from ambiguity function (AF) and cross ambiguity function (CAF) based on the matrix rearrangement and eigenvalue decomposition. Compared with traditional SSM from Wigner distribution (WD), the proposed SSM can synthesize a signal faster and more accurately. Then, based on unique properties in Delay-Doppler domain, a mask algorithm is applied for interference identification and extraction using Radon and its inverse transformation. Finally, a signal iterative decomposition algorithm (IDA) is utilized to subtract the largest interference from the received signal one by one. After that, a well-focused SAR imagery is obtained by conventional imaging methods. The simulation and measured data results demonstrate that the proposed algorithm not only suppresses interference efficiently but also preserves the useful information as much as possible.

Compared with the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, monostatic polarimetric sensitive (PS)-FDA-MIMO radar can provide targets polarization information. While compared with PS-MIMO radar, monostatic PS-FDA-MIMO radar can distinguish targets, which is from the same range bin and spatial-polarization angle but different ambiguous range region. In practical applications, the high pulse repetition frequency will cause the range ambiguity problem. Hence, a resolving range ambiguous approach for joint spatial-polarization angle and range estimation are devised for the monostatic FDA-MIMO radar with electromagnetic vector sensor (EVS)-FDA-MIMO in this paper. In receiver, spatial-polarization angles are estimated with the ESPRIT-based algorithm. In transmitter, range dependence compensation can resolve the coupling problem between spatial angle with range due to the frequency increment. The monostatic EVS-FDA-MIMO radar is able to utilize degree-of-freedom in the spatial angle and range domains to jointly determine the spatial-polarization angle and range parameters of target by the maximum likelihood estimator method. In addition, the Cramér-Rao Bounds for spatial-polarization angle and range are derived, and the proposed approach performance is analyzed. Numerical results corroborate the effectiveness of the considered estimation strategies in some diverse simulated scenarios.

This letter presents a novel algorithm for mixed far-field (FF) and near-field (NF) source localization using a linear tripole array. Firstly, several special fourth-order cumulants are constructed, then the shift invariant properties in the cumulant domain are utilized to estimate the parameters of each source at two tripoles with spacing exceeds a half wavelength. On this basis, a matrix equation is derived to classify the sources types and estimate the parameters of mixed sources at the phase reference point. Finally, the obtained locate estimates can be used to disambiguate the cyclically ambiguous estimates to produce the high-accuracy parameters estimation. Compared to the existing methods, the proposed one doesn't need to know each sensor's position and not restrict the array configuration to be uniform linear array (ULA). Moreover, it estimates both the spatial and polarization information of mixed sources and provides improved accuracy without any spectral search, as well as realizes a more reasonable classification of the signal types. Numerical simulation results show the effectiveness of the proposed method.

Ground-based synthetic aperture radar (GB-SAR) is a highly effective technique that is widely used in landslide and bridge deformation monitoring. GB-SAR based on multiple input multiple output (MIMO) technology can achieve high accuracy and real-time detection performance. In this paper, a novel method is proposed to design transmitting and receiving array elements, which increases the minimum spacing of the antenna by sacrificing several equivalent phase centers. In MIMO arrays, the minimum antenna spacing in the azimuth direction is doubled, which increases the variety of antenna options for this design. To improve the accuracy of the system, a new method is proposed to estimate channel phase errors, amplitude errors, and position errors. The position error is decomposed into three directions with one compensated by the phase error and two estimated by the strong point. Finally, we validate the accuracy of the system and our error estimation method through simulations and experiments. The results prove that the GB-SAR system performs well in bridge deformation and vibration monitoring with the proposed method.

This paper investigates the problem of mixed far-field (FF) and near-field (NF) source localization using a linear electromagnetic-vector-sensor array with gain/phase uncertainties. Firstly, several special fourth-order cumulant matrices are constructed, such that the shift invariance structure in the cumulant domain can be derived to estimate the DOA and polarization angles of each source at two electromagnetic vector sensors (EMVSs). Then, by computing the determinant of the coefficient matrix, the sources types can be classified with the prior knowledge of the number of both the FF and NF sources. On this basis, the range of NF sources and the DOAs of mixed sources at the phase reference point are captured subsequently. Finally, these estimates can be employed to generate the unknown gain/phase errors. Compared to the existing methods, the proposed one exploits both the spatial and polarization information of sources and provides a satisfactory parameters estimation performance under unknown phase/gain responses. Moreover, it does not need to perform any spectral search and not impose restriction on EMVSs placement, as well as realizes a more reasonable classification of the signal types. Simulations are carried out to verify the effectiveness of the proposed method.