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 ×${10}^5$ 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.

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) is not adequate for tracking the complex behavior of URGOs such as overlapping. An ideal is presented in this paper which use fuzzy clustering technique (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 GMMEMMEM-EKF* is proposed: it utilizes the Gaussian mixture modelexpectation maximization (GMM-EM) clustering to achieve data association and estimate the states of multiple URGOs within the MEM-EKF*, which avoided the imprecision of traditional FCT such as fuzzy C-means clustering (FCM) in data association. In addition, this papers 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

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.

The rapid development of unmanned 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 paper proposes a joint azimuth–elevation super-resolution estimation method based on two-dimensional iterative weighted atomic norm minimization (2D-IWANM). A unified processing framework is established by integrating long-time coherent integration (LTI) with sparse reconstruction techniques, providing an effective solution for radar-based UAV swarm localization. Based on this framework, the LTI 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 2D vectorized atomic norm minimization (2D-VANM) 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.