With the improvement of radar resolution and the prolonged illumination time, the detection of range-Doppler spread targets (RDSTs) has become increasingly important. However, RDSTs exhibits severe energy dispersion across multiple isolated scattering centers (SCs) with unknown spatial distributions and Doppler frequency centers (DFCs). Conventional detectors are unable to effectively accumulate the dispersed target energy, which could lead to significant performance degradation in RDST detection. To address this challenge, we propose an adaptive detection method for RDSTs based on the joint estimation of SCs and DFCs under the generalized likelihood ratio test criterion (JESCDF-GLRT). In this approach, an augmented target signal vector is formed by stacking the signals from all target SCs, capturing information about both the SCs and DFCs while exhibiting inherent sparsity. A sparse Bayesian optimization model is then formulated to jointly estimate the unknown spatial distribution of the target SCs and the corresponding Doppler steering matrix. By fully exploiting the target energy dispersed across SCs and DFCs, the proposed JESCDF-GLRT method significantly improves RDST detection performance. Its effectiveness is demonstrated through numerical simulations across five target scattering models and benchmarked against competing methods.

Multi-sensor fusion can significantly enhance target tracking performance. When multiple sensors collaborate, it is crucial to accurately associate tracks from different sensors. However, target characteristics can lead to inconsistent biases in target observations across sensors. These target-related biases can negatively impact the performance of track-to-track association (TTTA). To address this issue, we propose an innovative TTTA algorithm for monitoring traffic conditions with a millimeter-wave radar system. The model first employs a Kalman filter with pseudo-measurements to estimate state biases. Additionally, we formulate the association probability based on the similari biases. The model incorporates a two-stage association frameworkty of target states across different sensors and the stability of the that effectively eliminates false associations. Experimental results demonstrate the effectiveness of the proposed method.

Deconvolution algorithms often rely on conventional beamforming methods to obtain beamforming vectors, which limit their resolution. To enhance parameter estimation resolution, this article introduces the Parameter Estimation by Alternating Reconstruction and Sensation (PEARS) algorithm. In the proposed algorithm, direction estimation leverages a linearly constrained quadratic programming method and weighted L1-norm to solve the objective function, achieving higher resolution in the direction spectrum under fixed weighted vector conditions. The algorithm utilizes the gradient descent method to update the weighted vector, and the relationship among the dictionary matrix, direction spectrum, and weighted vector is computed using the chain rule. This process improves direction estimation results, particularly in scenarios with low signal-to-noise ratios. By alternating between target parameter estimation and weight vector calculation, the PEARS algorithm achieves highly accurate target azimuth estimation. Simulation results validate the algorithm's ability to accurately estimate target azimuth angles. In addition, lake and sea experimental results demonstrate the algorithm's effectiveness in correctly estimating direction in complex environments.

Joint tracking and classification (JTC) of vehicles is a crucial yet challenging task in intelligent transport and automotive systems. The advent of high-resolution modern sensors necessitates treating vehicles as extended targets. Current extended target tracking (ETT) algorithms provide shape estimations for vehicles, making shape size the most intuitive and accessible feature for classification. This paper contributes two key elements to achieve the JTC of vehicles. For one thing, we introduce the rectangular constraints and customize distinguishable measurement models using modified Gaussian processes (GP). For another thing, based on the customized GP models, we strengthen the role of class in the conditional extended target probability hypothesis density (ET-PHD) filter. Subsequently, we propose a class-enhanced JTC-ET-PHD filter and its Gaussian mixture implementation, enabling simultaneous kinematic, shape, and class estimation of vehicles. Finally, numerical results validate the proposed shape estimation and JTC method, affirming their effectiveness in addressing JTC challenges.

A variety of filters with track-before-detect (TBD) strategies have been developed and applied to low signal-to-noise ratio (SNR) scenarios, including the probability hypothesis density (PHD) filter. Assumptions of the standard point measure-ment model based on detect-before-track (DBT) strategies are not suitable for the amplitude echo model based on TBD strategies. However, based on different models and unmatched assumptions, the measurement update formulas for DBT-PHD filter are just mechanically applied to existing TBD-PHD filters. In this paper, based on the Kullback-Leibler divergence minimization criterion, finite set statistics theory and rigorous Bayes rule, a principled closed-form solution of TBD-PHD filter is derived. Furthermore, we emphasize that PHD filter is conjugated to the Poisson prior based on TBD strategies. Next, a capping operation is devised to handle the divergence of target number estimation as SNR increases. Moreover, the sequential Monte Carlo implementations of dynamic and amplitude echo models are proposed for the radar system. Finally, Monte Carlo experiments exhibit good performance in Rayleigh noise and low SNR scenarios.

In this paper, a robust and high-accuracy decentralized fusion strategy is proposed for multi-target tracking (MTT) in netted radar systems with non-overlapping field of view (FoV). Each radar in the network runs a local Probability Hypothetical Density (PHD) filter with the decentralized consensus protocol to reduce communication bandwidth and eliminate information inconsistency among nodes. In the above process, the most critical core is an effective fusion strategy. Our proposed method adopts the geometric covariance intersection (GCI) rule to improve fusion accuracy. However, the standard GCI fusion is not suitable for the netted radar systems with non-overlapping FoV because it only focuses on the targets within the intersection of radar FoVs. Consider that, we extend the weights in GCI fusion to be a set of state-dependent weights instead of scalars to perform GCI fusion in a more robust manner. Furthermore, the radar FoVs are always unknown and time-varying in practical scenarios. Towards addressing this case, we combine a clustering algorithm based on highest posterior density to maintain a good fusion performance. The Gaussian mixture implementation of the proposed method is provided. Numerical simulations are designed to verify the effectiveness of the proposed method.

Target spawning and extended shape estimation are important problems in group target tracking. In this paper, we propose a Gaussian mixture cardinalized probability hypothesis density (GMCPHD) filter for group targets with spawning and irregular shape based on star-convex Random Hypersurface Model (RHM). In order to solve the problem of irregular group shape, we use star-convex RHM to describe the distribution of measurement sources. Besides, we use the distance division method to realize the division of measurement sets and the judgment of group splitting. On this basis, the real-time tracking of the motion state and extended shape is realized under the framework of GMCPHD. The performance of this algorithm is showcased by comparison with the elliptical RHM-based GMCPHD filter, and the results show that the proposed algorithm can improve the estimation accuracy of group shape and motion state effectively.

Weak target recognition, tracking and track management with a low signal-to-noise ratio (SNR) are always tricky problems. Probability hypothesis density (PHD) filtering propagates the first-order multi-target moment to obtain the best Poisson approximation to multi-target density. The PHD filtering does not consider explicit associations between measurements and targets, which is computationally efficient. But it cannot distinguish different targets or extract the time series of track states. Based on track-before-detect (TBD) strategies, this paper proposes labeled PHD (LPHD) filtering and derives its close-form solution, which identifies targets with a unique label. It is derived based on rigorous Bayes criteria, finite set statistics and Kullback-Leibler divergence minimization approximation. The separable TBD-based observation likelihood is conjugate to the Poisson mixture prior for LPHD filtering. Under the point-target assumption, the multi-hypothesis assignments of pixel-to-target are implemented with Murty’s K-shortest path algorithm for LPHD filtering. Additionally, sequential Monte Carlo (SMC) implementations under the nonlinear non-Gaussian assumption are devised. Finally, simulations exhibit good performance in low SNR scenarios.

This paper addresses the problem of maneuvering multi-target tracking by a network of sensors having different and limited fields of view (FoV s). Each local sensor runs the Gaussian Mixture Probability Hypothetical Density (GMPHD) filter. Due to the target maneuver, based on a single motion model, severe tracking performance degradation can be observed. We propose to use multi-model (MM) method to realize the adaptation to motion characteristic so as to overcome maneuverability of targets. Then considering FoV s of sensors in the network are different and limited, the standard weighted arithmetic average (WAA) fusion is no longer applicable and leads to an underestimation of the target number. Therefore, we use state-dependent WAA (SD-WAA) fusion rule, which performs the WAA in a more robust way by calculating a set of state-related fusion weights. Numerical experiment is designed to demonstrate the efficacy of the proposed method.