Infrared and visible image fusion methods have shown promising results, yet existing approaches either compromise downstream detection performance through independent fusion processes or sacrifice computational efficiency and flexibility by requiring joint training of fusion and detection models. To address these challenges, we propose a detection-driven image fusion network based on diffusion models (termed as DDIF), which optimizes the fused images specifically for object detection tasks. Our method features the following three aspects: 1) we reformulate the image fusion process as an inverse problem solved by a non-differentiable optimization process wherein the fused result preserves the source modality information while conforming to the image prior provided by the diffusion model; 2) we design a Response Guide Learning Module (RGLM) to learn response maps, which determine the contribution of each modality in the fusion process according to the downstream detection task; 3) we establish explicit gradient relationships to ensure compatibility between RGLM training and the non-differentiable optimization process, enabling end-to-end training. Notably, a moderate coupling mechanism is formed in our framework as the subsequent detection model is pre-trained and frozen, enabling flexible integration with various advanced detection networks while maintaining computational efficiency. Extensive experiments indicate that our method achieves superior detection performance compared to SOTA approaches and produces high-quality image fusion results.

A sparsity-based adaptive beamforming (ABF) method is introduced to effectively process coherent signals with polarized sensor arrays (PSA). This method exploits the spatial sparsity of observed signals by transforming it into row-sparsity within a waveform-polarization composite matrix through data reorganization. This row-sparsity is subsequently cast as an$\ell _{2,1}$norm minimization problem, characterized by a gridless and compact mathematical expression with a Hermitian Toeplitz matrix. Then, a matrix factorization-based gradient descent (GD) algorithm is introduced to effectively resolve this optimization problem. The experimental evaluations demonstrate that the GD algorithm significantly outperforms the MOSEK solver in terms of computational efficiency. Further comparative analysis demonstrates that the proposed method outperforms the existing techniques, especially in contexts of low signal-to-noise ratio (SNR), with a moderate increase in computational runtime.

Pose is a fundamental attribute of 3D point cloud shape, which profoundly impacts point cloud analysis tasks. However, it is very tricky to directly solve the pose attribute since it is deeply coupled with geometry shape. To this end, the representation separation strategy has been proposed, where the global representation is modeled as a combination of the pose-related part representation and the geometry shape part representation. However, these methods still can not model the representation of the pose attribute well. As a reply, we design a new pose decoder in this paper, learning to disentangle the pose attribute by exploiting its complement,i.e. the geometry shape part representation. Specifically, a Siamese structure is introduced constituting of two shared branches, where two consistent point clouds with different pose attributes are input. The geometry shape part representation and the global representation are learned in each branch network to solving the pose-related part representation for disentangling the pose distribution. Then, we emphasize the completeness and no-redundancy of geometry shape part representation by designing two constraints. 1) We recover the learned geometry shape part representation to a point cloud and enforce it to maintain the same geometry shape as the original input point cloud to guarantee all geometry shape information is retained. 2) We develop two geometry shape part representations embedded from two branches to be the same so as to filter the pose information out. These two constraints are incorporated into the unsupervised loss function to train our pose decoder. Our pose decoder can be integrated into different point cloud shape analysis methods. We evaluate our pose decoder in point cloud classification and part segmentation tasks to handle the pose diversity problem of the input point cloud, which significantly improves the robustness. Besides, the obtained respective poses of input point clouds can be used to register them naturally, making the unsupervised method achieving superior performance.

The Angle-of-Arrival (AoA)-based approach is an appealing solution for unmanned aerial vehicle (UAV) positioning, and has received significant interest recently. In this article, we propose a novel framework for UAV three-dimensional (3-D) positioning, the core of which is to measure the two-dimensional (2-D) Angle-of-Departure (2D-AoD) and 2D-AoA via a bistatic multiple-input multiple-output (MIMO) radar. Unlike the existing positioning architectures, the MIMO radar is equipped with polarized array antennas. An estimator based on the parallel factor (PARAFAC) decomposition is developed. It first obtains the direction matrices via performing the PARAFAC decomposition of the array data. Thereafter, the rotational invariance characteristic is utilized to form a normalized polarization response vector, from which the 2D-AoD, 2D-AoA, and polarization status of the UAVs are achieved via incorporating the vector cross-product method and the least squares (LSs) technique. Finally, the 3-D positions of the UAVs are easily calculated via the location relationship between the 2D-AoD, 2D-AoA, and the coordinates of transmitting/receiving (Tx/Rx) array. The proposed framework is computationally friendly, and is capable of positioning anonymous UAV. Moreover, it is insensitive to the geometry of the Tx/Rx array, indicating that the proposed framework supports configurable Tx/Rx antennas. Simulation results are provided to verify our theoretical advantages.

A sparse reconstruction approach for adaptive beamforming (ABF) with polarization-sensitive arrays (PSA) is introduced in this letter. It first represents the spatial sparsity of incoming signals as the row sparsity of a power-scaled polarization matrix, which arises from the matrization of the redundancy-reduced covariance vector. Then the row sparsity issue is relaxed to an$\ell _{2,1}$norm minimization form and solved in a gridless way via a compact formulation, where a dimension reduction method is introduced to reduce the problem size. Compared to existing techniques, the proposed method processes the polarization information holistically and derives each signal parameter in the continuous domain. Simulation results substantiate the advantages of the proposed method over competing methods.

This paper aims to estimate the two dimensional (2D) 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.

This paper considers the joint design of the transmit waveform and receive filter for airborne multiple-input multiple-output (MIMO) radar under spectral constraint in signal-dependent clutter. The spatial-frequency spectral compatibility constraint is imposed in the joint design problem. To tackle the non-convex joint design problem, we develop an iterative algorithm based on iterative feasible point pursuit successive convex approximation (FPP-SCA). The proposed algorithm can handle the non-convex terms by the convex approximation. Simulation results demonstrate the superiority of the proposed algorithm in terms of better signal-to-interference-plus-noise ratio (SINR) and better spectral compatibility ability.

This paper revisits the problem of multiple parameters estimation for coherent targets in bistatic EMVS-MIMO radar. By extending the spatial smoothing mechanism to the EMVS-MIMO radar, three generalized spatial smoothing estimators, named the TS approach, the RS approach and the TRS approach, have been proposed. The killer idea of the proposed methodologies is to recover the rank of the covariance matrix via averaging the array measurement in spatial domain, and then estimate the parameters from the cooperation of the normalized vector cross-product technique and the LS method. Unlike the state-of-the-art polarization smoothing methods, the proposed methods would not sacrifice the polarization information, so that TS, RS and TRS are capable of providing polarization information of the coherent targets. The proposed estimators do not require any constrain on the geometries of the Tx/Rx EMVS arrays. They are computationally friendly yet retain robustness to the sensor position error. The proposed estimators are analyzed in detail, and numerical simulations are provided to verify their theoretical advantages.

This letter deals with the joint design of transmit waveform and receive filter bank for airborne multiple-input multiple-output (MIMO) radar under the target uncertainties. Assuming that the spatial angle and the Doppler frequency of the target are unknown, we formulate the maximin joint design problem by maximizing the worst-case signal-to-interference-plus-noise ratio (SINR) under the energy constraint, flexible modulus constraint, and similarity constraint on the transmit waveform. To tackle this problem, we develop a computationally efficient algorithm based on iterative feasible point pursuit successive convex approximation (FPP-SCA). Numerical results are provided to demonstrate the effectiveness and robustness of the proposed algorithm.

Recently, deep unfolding networks with interpretable parameters have been widely utilized in direction of arrival (DOA) estimation due to the faster convergence speed and better generalization ability. However, few consider the nested array for gridless DOA estimation. In this letter, we propose a deep alternating projection network to address the problem. We first convert the covariance matrix into a measurement vector in the form of atomic norm, which can reduce the matrix dimension during projection. We then train the proposed network to alternately obtain the positive semi-definite matrix and the corresponding irregular Hermitian Toeplitz matrix, where the loss function is derived by employing the trace of network output. Finally, we apply the irregular root Multiple Signal Classification (MUSIC) method to obtain gridless DOA via nested array. We demonstrate that the proposed networks can accelerate the convergence rate and reduce computational cost. Simulations verify the performance of proposed networks in comparison with the existing methods.

Owing to the large degrees of freedom and reduced mutual coupling by producing difference coarrays, nonuniform linear arrays have aroused great interest in direction of arrival (DOA) estimation. Previous works have presented some new sparse arrays, such as the thinned coprime array. In this paper, we propose a generalized thinned coprime array by introducing the flexible inter-element spacings, where the conventional one can be seen as a special case. We derive closedform expression for the range of consecutive lags, written as the functions of the antenna numbers and inter-element spacings. We show that, after optimization, the proposed array can achieve more consecutive lags than the other coprime arrays. In particular, the optimized results also provide the minimum number of antenna pairs with small separation. Simulation results demonstrate the superiority of the proposed GTCA using the subspace-based method.

Diversity smoothing has been widely developed for angle estimation with bistatic multiple input multiple output (MIMO) radar in the presence of coherent targets, the parameter identifiability of which is an important issue. In this paper, we are devoted to establishing more accurate conditions by studying the positive definiteness of smoothed target covariance matrix. The antenna numbers of transmit and receive arrays are derived as functions of the target number and target structure. We show that the new results improve upon previous ones and recover them in special cases. Simulation results are presented that corroborate our theoretical findings.

We present the convolution neural networks (CNNs) to achieve the localization of near‐field sources via the symmetric double‐nested array (SDNA). Considering that the incoherent near‐field sources can be separated in the frequency spectrum, we first calculate the phase difference matrices and consider the typical elements as the inputs of the networks. In order to guarantee the precision of the angle‐of‐arrival (AOA) estimation, we implement the autoencoders to divide the AOA subregions and construct the corresponding classification CNNs to obtain the AOAs of near‐field sources. Then, we construct a particular range vector without the estimated AOAs and utilize the regression CNN to obtain the range parameters of near‐field sources. The proposed algorithm is robust to the off‐grid parameters and suitable for the scenarios with the different number of near‐field sources. Moreover, the proposed method outperforms the existing method for near‐field source localization.

As an important branch of the Internet of Vehicles (IoV), vehicle positioning has drawn extensive attention. Traditional positioning systems based on a global positioning system incur long delays, and may fail due to obstructions. In this article, we propose an auxiliary positioning architecture, whose core is to estimate the direction of arrival (DOA) of signals from landmarks, such as wireless access points, utilizing a sensor array in the vehicle. Due to space limitations, the array may be placed in an arbitrary geometry and may suffer from unknown mutual coupling. Most algorithms are only effective for sensor arrays with special geometries, e.g., a uniform linear array or rectangular array. To tackle this problem, an improved multiple signal classification algorithm is derived, which is superior to the state-of-the-art iterative method from the perspective of computational complexity. Detailed analysis concerning identifiability, computational complexity, and Cramér-Rao bounds are given. The simulation results verify the improvement of the proposed DOA estimation algorithm. The proposed architecture can obtain robust self-localization with existing vehicular ad hoc networks, and it can collaborate with other positioning systems to provide a safe driving environment.