Difference-coarray processing enables sparse arrays to synthesize virtual uniform linear arrays with enlarged apertures and higher degrees of freedom. For fully augmentable arrays (FAAs), whose difference coarrays generate a contiguous symmetric virtual support and admit an exact composite Toeplitz parameterization, coarray mapping and redundancy averaging produce heteroscedastic and correlated unique-lag statistics. This makes unknown-source-number DOA estimation difficult to formulate within a unified covariance-aware framework. This paper proposes a Toeplitz quasi-likelihood screening-refit (TQSR) estimator for FAA virtual-domain processing. TQSR builds a strictly real asymptotic Gaussian quasi-likelihood from the realified sample covariance vector and the redundancy-averaged unique-lag statistics. Stage I constructs a trace-regularized screened signal Toeplitz covariance under a positive-semidefinite constraint, from which a spectral readout extracts the working model order and a feasible initializer. Stage II performs an unpenalized heteroscedastic local refit in the physical-parameter space to reduce Stage-I shrinkage bias. Under standard local regularity conditions and on the Stage-I correct-branch event, the analysis establishes Stage-I convergence, a local error bound, threshold-based model-order recovery consistency, dominant-eigengap consistency under vanishing Stage-I tuning and an additional population dominant-gap condition, and Stage-II attainment of the virtual-domain first-order asymptotic information bound under correct model-order recovery. It also clarifies the relationships among the exact physical-domain Cramér–Rao bound, the virtual-domain asymptotic bound, and the Gaussian-approximate virtual-domain bound. Numerical experiments show improved source-number recovery and end-to-end DOA estimation, especially in underdetermined closely spaced regimes.

Conventional range processing is fundamentally limited by signal bandwidth and therefore cannot reliably resolve multiple targets located within the same range-resolution cell. Existing sparse-reconstruction-based range super-resolution methods typically construct dictionaries from ideal delay-induced frequency responses, which do not faithfully characterize the pulse-compressed observations used in practice, thereby making these methods susceptible to model mismatch and the associated performance degradation. To address this issue, this paper proposes a pulse-compression-response-dictionary-based sparse Bayesian learning (PCD-SBL) method for radar range super-resolution. By jointly incorporating the transmitted waveform, system bandwidth, sampling rate, pulse-compression processing, and the candidate range grid, the proposed method constructs a pulse-compression-response dictionary consistent with the actual observation mechanism and formulates a sparse measurement model in the range domain. Based on this model, a hierarchical sparse Bayesian learning framework is then developed to iteratively update the sparsity-controlling hyperparameters and noise precision, enabling the estimation of multiple closely spaced target ranges within a single range-resolution cell. Simulation and measured-data results demonstrate that the proposed method alleviates the adverse effect of model mismatch and provides reliable range super-resolution for closely spaced swarm targets.

In this paper, we propose two methods for tracking multiple extended targets or unresolved group targets with elliptical extents. These two methods are derived from the Trajectory PHD (TPHD) filter and the Trajectory Cardinalized-PHD (TCPHD) filter, respectively. They enabling stable trajectory generation for extended targets. By employing a decoupled shape parameter model, the proposed methods can explicitly estimate the target extent, including the orientation of the elliptical extension and the lengths of its two semi-axes. For the extended target TPHD filter, we provide a Gaussian mixture implementation with an explicit extent update. For the extended target TCPHD filter, we derive closed-form Bayesian recursions and further provide its corresponding Gaussian mixture implementation. The proposed methods are named the GM-ET-TPHD-E filter and the GM-ET-TCPHD-E filter, respectively. We illustrate the abilities of these methods through both simulation and real-data experiments, with the latter utilizing the SIND dataset. The experimental results demonstrate that the two proposed methods have advantages over existing filters in the accuracy of extent estimation, as well as in the completeness and correctness of target trajectory generation.

The rapid development of Uncrewed aerial vehicle (UAV) swarm technology, characterized by small size, low radar cross section, and coordinated motion dynamics, poses significant challenges to conventional radar systems in target detection and high-precision localization. Effective detection and localization of swarm targets are hindered by the limited resolution and low echo signal-to-noise ratio (SNR). To address these challenges, this article proposes a joint azimuth-elevation super-resolution estimation method based on 2-D iterative weighted atomic norm minimization (2D-IWANM). A unified processing framework is established by integrating long-time coherent integration (LTCI) with sparse reconstruction techniques, providing an effective solution for radar-based UAV swarm localization. Based on this framework, the LTCI technique enhances the echo SNR and compensates for the signal phase, establishing a reliable basis for subsequent processing. The 2D-IWANM algorithm operates directly on the original signal matrix, avoiding the computational cost of vectorization in conventional 2-D vectorized atomic norm minimization methods. Furthermore, an adaptive iterative weighting strategy is incorporated to update atomic weights in each iteration, effectively suppressing spurious peaks, overcoming resolution limitations, and enhancing the ability to resolve closely spaced targets. Simulation and comparative results show that the proposed algorithm achieves superior resolution, robustness, and computational efficiency. Its effectiveness is further validated using two sets of measured data.

Many multi-target tracking applications (e.g., tracking multiple targets with LiDAR or millimeter-wave radar) are challenged by closely spaced targets. In this work, we propose a method for the tracking of multiple extended targets or unresolvable group targets in such scenarios. The approach builds on the cardinality probability hypothesis density (CPHD) filtering framework for computational efficiency, models the target’s extent with the multiplicative error model (MEM), and uses variational Gaussian mixture model (VGMM)-derived responsibilities to drive probabilistic data association (PDA) measurement updates. This effectively mitigates state fusion between closely spaced targets and yields more accurate state estimation. In experiments on diverse simulated and real datasets, the proposed method consistently outperforms existing approaches, achieving the lowest localization, shape estimation, and cardinality estimation errors while maintaining an acceptable runtime and scalability.

In this paper, we propose two methods for tracking multiple extended targets or unresolved group targets with elliptical extent shape. These two methods are deduced from the famous Probability Hypothesis Density (PHD) filter and the Cardinality-PHD (CPHD) filter, respectively. In these two methods, Trajectory Set Theory (TST) is combined to establish the target trajectory estimates. Moreover, by employing a decoupled shape estimation model, the proposed methods can explicitly provide the shape estimation of the target, such as the orientation of the ellipse extension and the length of its two axes. We derived the closed Bayesian recursive of these two methods with stable trajectory generation and accurate extent estimation, resulting in the TPHD-E filter and the TCPHD-E filter. In addition, Gaussian mixture implementations of our methods are provided, which are further referred to as the GM-TPHD-E filter and the GM-TCPHD-E filters. We illustrate the ability of these methods through simulations and experiments with real data. These experiments demonstrate that the two proposed algorithms have advantages over existing algorithms in target shape estimation, as well as in the completeness and accuracy of target trajectory generation.

Uncrewed aerial vehicle (UAV) swarms have the characteristics of small size, high density, and agile maneuverability. These attributes have given rise to substantial difficulties for radar in achieving precise detection and resolution of UAV swarms. Meanwhile, their potential malicious use poses a significant threat to national security, making accurate identification of UAV swarms of utmost importance. This article presents a super-resolution method for UAV swarms that integrate the coherent long-time integration technique with the gridless sparse recovery method based on iterative weighted atomic norm minimization (IW-ANM). In the method, a framework for UAV swarm detection and super-resolution processing is first established. Then, based on the framework, the IW-ANM algorithm is proposed. This algorithm encodes prior information into the Toeplitz constraint matrix, adopts a well-designed weight function, and finally super-resolves UAV swarms in the spatial dimension through iterative weighting. Numerical simulations demonstrate that, compared with the reweighted atomic norm minimization, ANM, multiple signal classification, and so on, the proposed IW-ANM algorithm is more practical and robust, and has a better super-resolution performance in conditions of low signal-to-noise ratio, high-density swarms, and small angle intervals. Furthermore, a real experiment is conducted to validate the effectiveness of the proposed IW-ANM.

In this work, we propose a method for tracking multiple extended targets or unresolvable group targets in a clutter environment. First, based on the random matrix model (RMM), each target's joint kinematic-extent state is modelled as a gamma Gaussian inverse Wishart (GGIW) distribution. Considering the uncertainty of measurement origin caused by the clutters, we adopt the idea of probabilistic data association and describe the joint association event as an unknown parameter in the joint prior distribution. Then, variational Bayesian inference (VBI) is used to approximate the intractable posterior distribution. To improve practicality, we propose two lightweight schemes to reduce computational complexity. The first is clustering-based and effectively prunes joint association events. The second simplifies the variational posterior by using marginal association probabilities. Finally, we demonstrate effectiveness on simulations and real-data experiments and show that the method outperforms state-of-the-art baselines in accuracy and adaptability.

In the tracking of unresolvable group object (URGO), the common extended Kalman filter based on the multiplicative error model (MEM-EKF*) provides an effective solution to accurately estimate the extended shape. But in multiple URGOs tracking applications, the existing methods based on MEM-EKF* and joint probabilistic data association (JPDA) are not adequate for tracking the complex behavior of URGOs such as overlapping. An ideal is presented in this article that uses fuzzy clustering technology (FCT) to complete probabilistic data association between multiple URGOs and simultaneously estimate the shape of the URGOs through MEM-EKF*. A multiple URGOs tracking method called GMMEM-MEM-EKF* is proposed; it utilizes the Gaussian mixture model-expectation maximization (GMM-EM) clustering to achieve data association and estimates the states of multiple URGOs within the MEM-EKF*, which avoided the imprecision of traditional FCT such as fuzzy C-means (FCM) clustering in data association. In addition, this article gives the solution of multiple URGOs tracking under dense clutter. The better performance and accuracy of our method in dealing with complex behaviors of URGOs is demonstrated by Monte Carlo simulations in comparison to state-of-the-art methods in literature.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is widely used in integrated radar and communication systems (IRCSs). Moreover, index modulation (IM) is a reliable OFDM transmission scheme in the field of communication, which transmits information by arranging several distinguishable constellations. In this paper, we propose a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems. Combining the advantages of MIMO and OFDM-IM technologies, the integrated MIMO-OFDM-IM signal design is realized through the reasonable allocation of bits and subcarriers, resulting in better intercarrier interference (ICI) resistance and a higher transmission efficiency. Taking advantage of the sparseness of OFDM-IM, an improved target parameter estimation method based on sparse signal reconstruction is explored to eliminate the influence of empty subcarriers on the matched filtering at the receiver side. In addition, an improved sequential Monte Carlo signal detection method is introduced to realize the efficient detection of communication signals. The simulation results show that the proposed integrated system is 5 dB lower in the peak sidelobe ratio (PSLR) and 1.5 ×105 lower in the number of complex multiplications than the latest MIMO-OFDM system and can achieve almost the same parameter estimation performance. With the same spectral efficiency, it has a lower bit error rate (BER) than existing methods.

Compared to traditional single orthogonal frequency division multiplexing (OFDM) radar and OFDM communication systems, integrated OFDM radar and communication systems have the advantages of improved sharing of scarce spectrum resources, a simple hardware structure and a reduced interference between signals. In this paper, a constraint relaxation-based robust OFDM multiplexing waveform (ROW-CR) design method for integrated radar and communication systems is proposed. Considering the influence of correlated clutter and jamming signals, transmission and reception models of different system platforms are established by allocating subcarrier complex weights of different antenna signals so as to determine radar conditional mutual information and communication channel capacity parameters. Meanwhile, a restricted closed model is introduced through the limit range of the target frequency response. Then, a robust OFDM waveform optimization problem for integrated radar and communication systems is constructed by the minimax criterion, and the closed-form solution is obtained by adopting an improved method based on trace function properties and constraint relaxation, resulting in a better radar and communication performance trade-off. In addition, a parameter hierarchical optimization-based robust OFDM waveform (ROW-PHO) design method is further explored to reduce the computational complexity, and this method can also ensure a low system performance loss. Finally, the numerical simulation results verify the effectiveness of the proposed methods.

To obtain real-time ephemeris data from the Trimble BD930 board, we created an ephemeris receiving system based on Trimble BD930 board. Since the ephemeris data obtained is in the BINEX format, it cannot be directly adapted to the RINEX ephemeris format required by most receivers, and can only be used after transcoding. Based on an in-depth analysis of the differences between the two formats, we improved the traditional RINEX encoding algorithm and implemented the conversion from BINEX to RINEX using C++programs. After multiple sets of data comparison tests, the algorithm program shows that we designed can achieve a stable conversion of BINEX to RINEX with greater accuracy and speed.

In order to overcome the large amount of noise in marine communication and further improve the quality of communication under low SNR, an intelligent noise elimination algorithm based on cluster cooperation is proposed. Firstly, a finite perception congestion model is established, and a noise control strategy based on denoising operator for consistency algorithm is proposed. It is pointed out that when epsilon(t) is a high order infinite, the consistency algorithm after denoising can control the noise, make the Agent to converge to the original convergence state, and make the center to distribute normally. By frequency modulation, the noisy signal in communication is modulated into the instantaneous frequency of the analytic signal. In view of the non-linear characteristics of the marine communication signal, the cluster collaboration algorithm is realized by using the windowed Wigner-Ville distribution. For the high noise situation, the iterative algorithm can be used until the noise is completely eliminated. Experiments show that the algorithm can effectively eliminate communication noise and has high real-time performance.