The accurate parametric array manifold is the theoretical basis of array signal processing technology. In this communication, a compact parametric model of the true array manifold is proposed. A virtual extended array is established by splitting each antenna into a collection of finite-length dipoles, of which the mutual coupling effect is described using a nonlinear coupling model based on Taylor expansion. The resulting composite manifold has a simple form composed of an expanded virtual isolated manifold and a broad coupling matrix, which can provide an extremely accurate representation of the true array manifold. Numerical examples verify that the accuracy of the proposed composite manifold is two or three orders of magnitude better than that of several other state-of-the-art manifolds. The proposed composite manifold can be applied to all kinds of array signal processing algorithms directly to enhance the capabilities of antenna arrays in practical applications by replacing the overly simplified and imprecise manifold.

Movable antenna arrays can enlarge the accessible array aperture using a limited number of active antenna elements. However, existing movable array models typically assume that the array elements can be repositioned between consecutive sampling intervals, which is difficult to be realized in practical systems. Therefore, we consider a practical scenario in which the element movement rate is lower than the signal sampling rate, such that multiple snapshots can be collected under each element-position layout. However, due to the limited number of active elements, each observation group can cover only a portion of the candidate element positions, resulting in incomplete data observations. To recover the missing information, we exploit the structural properties of the covariance matrix of an ideal uniform linear array (ULA) and formulate a matrix completion problem. The multiple signal classification (MUSIC) algorithm is then applied to the completed covariance matrix for direction-of-arrival (DOA) estimation. The effectiveness of the proposed method is validated through simulation results.

The orthogonal frequency division multiplexing (OFDM) waveform with cyclic prefix (CP) in the joint communication and SAR imaging (JCASAR) systems suffers from large peak-to-average power ratio (PAPR). In order to reduce PAPR on the basis of improving JCASAR system performance, we propose a two-step optimization strategy. Firstly, to weigh the communication performance against PAPR, a subcarrier assignment and power allocation problem that jointly maximizes communication data rate and minimizes the number of communication subcarriers is formulated, subject to constraints on the radar performance and subcarrier power. And a modified cyclic minimization algorithm is proposed to solve the mixed-integer nonconvex problem iteratively. Then, a PAPR reduction method based on tone reservation is established, which is solved by majorization-minimization and least squares algorithm. Finally, the numerical simulations demonstrate the effectiveness of the proposed CP-OFDM strategy in JCASAR systems.

Jamming signal recognition is a crucial aspect of radar anti-jamming technology. In practice, radar systems often encounter highly complex environments involving multiple jamming sources. This paper addresses the problem of compound jamming recognition and proposes a modified end-to-end model based on the Detection Transformer (DETR). The enhanced model incorporates multi-scale feature fusion, allowing for precise identification and localization of individual jamming sources in the time-frequency domain. Experimental results demonstrate that the proposed model consistently outperforms existing methods under various jamming-to-noise ratio (JNR) conditions.

In the domain of electronic warfare, radar signal deinterleaving emerges as the foundational and indispensable phase of electronic reconnaissance. The ever-increasing complexity of electromagnetic environments, further compounded by technological advancements, such as multifunction radars (MFRs), has led to the inadequacy of traditional deinterleaving techniques. To tackle these challenges, this article introduces the temporal convolutional attention network (TCAN) framework. This framework harmoniously combines a TCN with an advanced attention mechanism, thereby significantly enhancing the system's signal sorting proficiency. Through rigorous experimental validation, we demonstrate that TCAN consistently outperforms existing baseline methods. This superiority is particularly pronounced under conditions of signal sparsity and in nonideal environments, which are typified by pulse loss, spurious pulses, and measurement errors. Furthermore, we conduct a thorough analysis to elucidate the profound impact of various input formats and multiparameter features on deinterleaving performance. By meticulously examining these factors, we establish TCAN as a robust and versatile solution capable of effectively navigating the heightened complexity of modern radar signal environments. Our findings highlight TCAN's potential as a potent instrument for augmenting electronic reconnaissance capabilities amidst evolving electromagnetic challenges.

Recently, tensor network decomposition has attracted increasing attention due to its high efficacy in modeling multidimensional correlations in high-order tensors. Considering this advantage, this study employs tensor train decomposition (TTD) to achieve joint 2-D direction-of-departure (2D-DOD) and 2-D direction-of-arrival (2D-DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar systems with uniform planar array (UPA) configurations. First, a 5-D tensor is defined to incorporate the 2-D spatial information from transmit UPA (TUPA) and receive UPA (RUPA), which is then combined with temporal information. Next, a low-rank 4-D tensor is generated by performing an autocorrelation operation along the temporal dimension of the 5-D tensor, forming a structure that encapsulates the difference coarrays of the TUPA and RUPA. Then, a TTD framework is designed to decompose the 4-D tensor into interconnected lower order TT-cores. Furthermore, a precise bidirectional mapping is performed between the TT-cores and the original Vandermonde factor matrices. Finally, leveraging distinct combinations of TT-cores, two innovative methods are developed to recover the Vandermonde factor matrices. The proposed methods conduct automatic pairing of 2D-DOD and obtain 2D-DOA estimates by enforcing consistent permutation orders across all factor matrices. The proposed methods are verified by simulation experiments. The results confirm that the proposed methods can surpass the existing tensor-based methods in terms of estimation accuracy.

Existing digital array radars face significant challenges when dealing with simultaneous mainlobe and sidelobe jamming, particularly in large-scale phased arrays where element-level digital beamforming imposes high computational demands. To address these issues, this paper proposes an enhanced subarray adaptive processing architecture. A novel robust mainlobe maintenance technique, based on reconstructing the subarray interference and noise covariance matrix, is introduced to suppress multiple sidelobe jammers while preserving the shape and gain of the mainlobe. Subsequently, subarray sumdelta beams with maintained mainlobe are formed to mitigate mainlobe jamming. Finally, full-array adaptive sum-delta beams are employed for accurate monopulse angle estimation. Comprehensive simulation results demonstrate the proposed method's effectiveness in maintaining the mainlobe, canceling jammers, and achieving precise angle estimation, all with relatively low computational complexity.

Most existing polarization-based spectrum sensing techniques focus on static polarized signals, but their applicability is severely compromised by polarization-agile signals due to the latter’s time-varying polarization states. To tackle this, we construct an orthogonal dual-polarized antenna reception model for such signals and establish an analysis framework incorporating both linear and circular polarization basis, starting from how polarization agility affects electric field vector distributions. Analysis of the polarization distribution difference factor (PDDF) reveals that spikes in the sliding-window standard deviation sequence of the PDDF directly map to polarization-agile transition moments. Based on this, we propose a detection algorithm using pulse sequence pattern matching, enabling effective tracking and sensing of polarization-agile signals.

The goal of this paper is to solve the low-orbit pseudolite self-interference problem. The low-orbit pseudolite navigation signals will interference satellite navigation as they broadcast at the same frequency and same time. However, the low-orbit pseudolite also needs satellite navigation to get continuous and effective high-precision positioning. So, while effectively filtering the self-interference, the GNSS navigation signals need low distortion. In this paper, we propose an anti-self-interference method which ensures that GNSS navigation signals are not distorted. Specifically, the method uses the known characteristics of self-interference signals to establish an optimization problem. By solving this problem, self-interference filtering can be achieved without estimating the direction of satellite navigation signal and without the assistance of the receiver channel amplitude-frequency and phase-frequency characteristics. The simulation test results show that the algorithm can filter self-interference without additional distortion of the satellite navigation signals. The application test results show that, the algorithm can filter self-interference with high-precision carrier observation of the satellite navigation signal which statistical standard deviation is only 0.01 times of the carrier wavelength. The method could help in achieving high-precision satellite navigation and positioning of pseudolite terminals and providing high-precision position information for low-orbit pseudolites.

In the evolving landscape of modern electronic warfare, radar systems frequently encounter significant challenges due to adversarial blanket jamming, which severely undermines their operational capabilities. To counteract these threats, this paper introduces a novel approach for jammer localization and suppression for a distributed radar system. Our method integrates Angle of Arrival (AOA) and Received Signal Strength (RSS) information to achieve precise localization of jamming sources. By accurately pinpointing the source of jammer, the system can implement targeted countermeasures to mitigate its impact. Building upon this localization framework, we propose a joint array optimization strategy that optimizes the layout of the distributed radar system by holistically maximizing multi-domain localization accuracy and enhancing anti-jamming performance. This optimization is facilitated by leveraging the Multi-Objective Particle Swarm Optimization algorithm (MOPSO), which enables the system to adaptively configure its array layout for optimal performance. Extensive simulations validate the efficacy of our proposed method. The results demonstrate that our approach not only achieves superior localization accuracy but also significantly improves the overall performance of jamming suppression in a distributed radar system.

This chapter presents a robust training sample selection method for space-time adaptive processing (STAP) based on the similarity of clutter reconstructed using atomic norm minimization (ANM). Traditional sample selection methods struggle in heterogeneous environments due to the difficulty in acquiring independent and identically distributed (IID) training samples. The proposed method first reconstructs the clutter covariance matrix (CCM) using ANM, addressing the off-grid problem. A clutter similarity metric is then introduced, leveraging the reconstructed CCM to assess the similarity between training samples and the cell under test (CUT) without extracting the clutter subspace. Outliers are eliminated using the generalized inner product (GIP) method, and samples with clutter distributions similar to the CUT are selected. Numerical experiments show that the proposed method enhances STAP performance by effectively selecting representative training samples, demonstrating its robustness and efficiency in heterogeneous environments.

Space-time adaptive processing (STAP) is a classic anti-interference algorithm for satellite navigation. In engineering applications, the number of time-domain taps of STAP not only determines the anti-jamming performance, but also determines the FPGA logic resource required for engineering implementation. In order to reduce application costs, it is urgent to predict the minimum number of time-domain taps required to achieve specified anti-interference indicators, so as to select the lowest cost FPGA chip. Unlike traditional dimensionality reduction algorithms such as Multistage Wiener Filter (MSWF), this paper proposes a method for determining the minimum number of time-domain taps without digital sampling data. To provide such a method, this paper construct an optimization problem and a solution method that determines the minimum number of time-domain taps based on the amplitude-frequency and phase-frequency characteristics of the specific receiver channels. Moreover, this method use interference to noise ratio (INR) to estimated minimum number of time-domain taps, which ensure the minimum number meet anti-interference performance. Finally, this paper verifies the effectiveness of the method through simulation by collecting amplitude-frequency and phase-frequency characteristics of 30 sets channels, and engineering test with three 2-element space time anti-jamming receivers.

Focusing on investigating the problems of array calibration and beamforming for Coprime Location Arrays(CLA),a new beamforming algorithm,which is called CLA-SILAC-INCM algorithm is proposed for the partly calibrated CLAs,by exploiting the Simultaneous Interference Localization and Array Calibration(SILAC)technique.Theoretical analysis shows that when the CLA contains not less than 3 fully calibrated antenna elements,highly accurate and unambiguous estimation for interference direction and array gain-phase error vector can be obtained using the SILAC technique.Afterward,the Interference plus Noise Covariance Matrix(INCM)is reconstructed and the optimal beamforming weighting vector is computed.Simulation results show that the proposed CLA-SILAC-INCM algorithm exhibits better performance compared with existing algorithms,especially when the signal-to-noise ratio is close to interference-to-noise ratio.

The electromagnetic vector sensor (EMVS) embedded multiple-input–multiple-output (MIMO) radar, named EMVS-MIMO radar, offers a unique perspective on target localization, providing both 2-D angle and polarization status estimations of targets. However, the current frameworks limit the EMVS-MIMO radar's ability to achieve high-resolution 2-D direction finding while maintaining low complexity. To address this limitation, this article proposes a uniform rectangular array-based methodology with the intersensor spacing of the receiving array greater than half-wavelength, along with a high-resolution estimation strategy based on a parallel factor (PARAFAC) model. To optimize the identifiability of the PARAFAC model, the array measurement is first arranged into a third-order tensor, and the six possible PARAFAC tensor models are established. Factor matrices are then estimated via PARAFAC decomposition, and 2-D direction finding is achieved by combining the spatial rotational invariance technique with the normalized vector-cross product technique. The proposed PARAFAC algorithm can be extended to other sparse uniform geometries, such as L-shaped arrays and parallel linear arrays. The larger array aperture and inherent tensor gain of the proposed PARAFAC approaches offer superior estimation performance compared to existing frameworks. In addition, the flexibility of the PARAFAC model may make it suitable for single snapshot scenarios. Finally, the improvements of the proposed approaches are validated by numerical simulations.

MIMO radars with electromagnetic vector sensor (EMVS) antennas (also known as EMVS-MIMO radars) have been investigated intensively for target direction estimation in recent years. Many existing works on this topic are based on an unrealistic assumption that each EMVS antenna transmits six identically polarized and mutually orthogonal waveforms. Furthermore, target-fluctuation induced non-coherent backscattering in polarization has been entirely overlooked. This paper aims to develop a general transmit-receive signal model for EMVS-MIMO radar direction estimation. Specifically, by taking transmit polarization agility and/or polarization non-coherent backscattering into account, the collected pulse signals are no longer of complete polarization, but of incomplete polarization. By regarding an incomplete polarized (IP) signal as two incoherent completely polarized (CP) signals with the same azimuth-elevation directions and drawing upon the idea of ESPRIT, a new azimuth-elevation direction estimation algorithm is thus derived. The advantage of the new algorithm is that it can provide closed-form, automatically paired azimuth-elevation direction estimates and require no information on the location/displacement of the transmit-receive antennas. Finally, simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

This letter aims to estimate the 2-D direction-of-arrival (DOA) using a polarized uniform rectangular array (URA) under multipath propagation. To leverage the tensorial nature, a parallel factor (PARAFAC) model is established, in which it comprises two spatial response matrices, the polarization response matrix, and the source matrix. Unfortunately, the source matrix exhibits rank-deficiency, hindering effectively PARAFAC decomposition. Our analysis reveals that the rank-deficiency can be easily resolved by taking the KhatriRao product with a full column rank factor matrix. Consequently, three rearranged PARAFAC tensors are obtained that are free of the source matrix's rank-deficiency. The estimation of 2D-DOA is then performed using the vector cross product-auxiliary rotational invariance technique (VCPARIT). The proposed algorithms are insensitive to inter-sensor distance and are suitable for a one-snapshot scenario. Furthermore, they outperform existing smoothing methods from the perspective of estimation accuracy. Theoretical advantages of the proposed algorithms are corroborated by the simulations.

It is well known that electromagnetic vector sensor (EMVS)-multiple-input multiple-output (MIMO) radar is an emerging technique that allows for 2D-direction-of-departure (DOD) and 2D-direction-of-arrival (DOA) estimation. Unfortunately, existing array geometries on the EMVS-MIMO radar can rarely reach a good compromise between the estimation accuracy and the computational burden. This article is aimed at proposing an L-shaped sparse array topology for a bistatic EMVS-MIMO radar, whose interelement distance is much larger than half-wavelength. A fast algorithm is proposed herein to estimate the 2D-DOD and 2D-DOA. First, the direction cosine estimates are obtained via the rotational invariance properties of the sparse subarrays, which are ambiguous yet exhibit high-resolution. Thereafter, the direction cosine estimates are achieved via the vector cross-product of the normalized Poynting vectors, which are unambiguous but have low-resolution. The unambiguous high-resolution direction cosine estimates are determined by combining the previous results, following which the 2D-DOD and 2D-DOA can be easily recovered. It is shown that the proposed framework can obtain a better accuracy of estimation than the other existing methods. Moreover, it is more flexible than the current sparse array methodologies. Finally, the theoretical derivations have been validated by simulation results.

In this paper, the problem of passive direction finding is addressed using an acoustic vector sensor array (AVS), which may be deployed either in free space or near a reflecting boundary. Building upon the $4 \times 1$ vector field measured by an AVS, the particle-velocity coarray augmentation (PVCA) is proposed to admit the underdetermined direction finding using the spatial difference coarray derived from the vectorization of the array covariance matrix. Unlike the widely used spatial coarray Toeplitz recovery technique, the PVCA is applicable to arbitrary array geometries and imposes no reduction of the spatial difference coarray aperture. For the array located at or near a reflecting boundary, the PVCA allows resolving up to $13$ sources, while for the array located in free space, the PVCA can identify $9$ sources at most. By applying to the systematically designed nonuniform arrays, such as coprime arrays and nested arrays, the PVCA can be coupled with the spatial smoothing technique to get the number of resolvable sources multiplied. Finally, the efficacy of the PVCA is verified by numerical simulations.

This paper investigates the problems of mutual coupling analysis and angle estimation for linear Coprime sensor Linear Arrays (CLA). Firstly, the CLA is defined. It is proved that the steering vector CLA is unambiguous. Afterwards, based on high-order cumulants, a third-order tensor model of the array output is established. Array steering vectors are subsequently estimated via tensor decomposition. Finally, unambiguous direction estimates are derived from the estimated steering vectors. The sensor spacings of the CLA can be designed as much greater than a half-wavelength, thereby significantly reducing array mutual coupling effect. Using impedance matching mutual coupling model, the mutual coupling effect and angle estimation performance are compared with the existing well-known array configurations to show the effectiveness of the CLA.

Existing sensor array direction-finding algorithms in the open literature simplify the far-field source wavefront as exactly planar in order to facilitate the algorithmic development. In fact, since the wavefront of a point emitter is necessarily spherical, this planar wavefront assumption should actually be approximate, especially for large-scale arrays. Mismatch between the actual and approximate propagation models would introduce a non-random bias on the performance of the direction-finding algorithms. This non-random bias is inevitable due to the mismatch between the algorithm’s presumptions and the data it actually processes. This work derives the mathematical expression of this non-random bias, along with several qualitative analyses. The analysis is carried out using the Taylor-series expansion. Also, the effect of the model mismatch on direction-finding algorithms is evaluated numerically.