Multiple pulse repetition frequency (multi-PRF) radar is widely utilized due to its advantages in range-Doppler ambiguity suppression and anti-jamming capabilities. Most existing research focuses on intra-frame multi-PRF mode, which lacks flexibility in PRF scheduling and robustness against clutter and dynamic targets. In contrast, inter-frame multi-PRF mode allows PRF variation across frames, enhancing ambiguity resolution and target detection performance in complex environments. However, research on detecting and tracking ambiguous targets under the inter-frame multi-PRF mode remains limited. To address this gap, we propose a multi-frame track-before-detect (MF-TBD) and fusion disambiguation method based on inter-frame multiPRF radar systems, referred to as IF-MF-TBD. First, an efficient method for constructing a pseudo-measurement plane is proposed to mitigate the computational complexity of ambiguity resolution. Then, to estimate the target's ambiguous state across different PRFs, intra-PRF multi-frame energy accumulation is performed within the pseudo-measurement plane, generating multiple sets of ambiguous plot-sequences. Finally, ambiguity resolution is achieved through plot-sequences association and fusion method, yielding unambiguous target tracking results. Simulation results demonstrate that the proposed IF-MF-TBD algorithm effectively enhances target detection and tracking performance in interframe multi-PRF radar systems.

Millimeter-wave radar has been widely adopted in intelligent transportation systems. Modern millimeter-wave radars' high resolution makes a single target yield multiple measurements (point cloud measurements), turning target tracking into an extended target tracking (ETT) problem. The uncertainty in radar measurement sources coupled with the complex spatial distribution of measurements fundamentally challenges ETT algorithms. Conventional ETT algorithms with cluster-then-associate frameworks partition point cloud measurements predominantly through density, failing to exploit target shape information and resulting in suboptimal clustering efficacy. This leads to inaccurate associations between measurements and extended targets, ultimately degrading overall tracking accuracy. This paper proposes a novel approach to circumvent the performance limitations caused by clustering errors in traditional methods. First, we propose a clustering-free closed-loop ETT framework that incorporates the prior target's shape information as feedback. Subsequently, we develop a data association method that leverages the inherent correlation between point cloud measurements and target shape. The associated measurements are then probabilistically fused and integrated into a Kalman filter for state updating. In simulated and real-world datasets, compared with the traditional method, clustering before association, we have validated the effectiveness of the proposed method.

With the improvement of sensor resolution, extended targets (ETs), characterised by multiple measurements, have become a hotspot in the field of target tracking. The trajectory probability hypothesis density (TPHD) filter, which inherently provides trajectory estimates, stands out as an effective tracking filter. By integrating the interacting multiple model (IMM) approach, we introduce an IMM-ET-TPHD filter for tracking maneuvering ETs in this paper. Firstly, we extend the concept of the IMM to trajectory sets and derive the IMM-ETTPHD recursion. Then, we develop a Gaussian mixture (GM) implementation of the IMM-ET-TPHD recursion. Finally, the proposed IMM-ET-TPHD algorithm is validated in a challenging maneuvering ET tracking scenario, demonstrating its superior performance.

With the increasing demand for traffic situation awareness, extended traffic target (ETT) tracking is a significant yet challenging task especially in tough scenarios with dense and various ETTs. Due to the spatial proximity of ETTs and a noisy sensor, it is challenging for multi-target tracking algorithms to effectively distinguish and track ETTs. To improve the accuracy and robustness of ETT tracking in tough traffic scenarios, we analyze the interaction among ETTs and the lane constraint. Firstly, we develop an interactive motion model for collision avoidance to address trajectory confusion when ETTs are in close proximity. Additionally, we propose a lane constraint method that models lanes as pseudo measurements and constrains the motion of ETTs via pseudo update. Considering the complexity and extendibility, the extended target trajectory probability hypothesis density (ETTPHD) filter is adopted to achieve a more accurate estimation of ETT trajectories. Specifically, we realize the proposed interactive motion model and lane constraint method based on the ETTPHD (IC-ET-TPHD) filter. Performance comparisons between our proposed filter and other algorithms are conducted through both simulations and experiments.

Track-before-detect (TBD) and multi-sensor fusion are two popular methods of weak target detection which can improve the performance by increasing the number of measurements. In this paper, we combine these two methods, proposing a novel multi-sensor track-before-detect (MS-TBD) method for automotive platforms. It can utilize the information from both the spatial and temporal dimensions of the target by jointly processing the measurement from different radars. In particular, the traditional TBD method is often based on an implicit assumption: the presence of targets is unchanged in the sliding window. However, this assumption may not be applicable for automotive multi-sensor systems due to the time-varying fields of view (FOV). To solve the problems mentioned above, we first present an energy accumulation strategy for automotive multi-radar systems and then propose a multiple-hypothesis detection method with the adaptive threshold (AT). It is demonstrated by simulations that the proposed methods show superior performance.

This paper intends to improve the multilane-road target tracking performance by considering heading restrictions. Most existing tracking algorithms assume that vehicles move independently in an open-field environment. However, the movements of vehicles have to be restricted by the geometry of roads, traffic rules, or preset routes. The effective utilization of prior knowledge regarding such restrictions can help to significantly enhance tracking performance. In this paper, we investigate the problem of state estimation while taking into account the heading restrictions imposed by the road direction. To describe the target longitudinal and lateral maneuvering behavior, we design the heading restrictions within a 2-D road coordinate system. The target state vector is then augmented by the y intercept of the constraint straight line, and the measurement vector is augmented by constructing two pseudo-measurements. Consequently, the heading restrictions are incorporated into unscented Kalman filter based on two augmented vectors, called HR-UKF. Furthermore, we employ the singular value decomposition method to enhance numerical stability. Finally, the effectiveness of the proposed algorithm is validated through numerical simulations and real-measured data.

Multi-frame detection has been widely researched in the scenario where the target signal-to-noise is low. However, it becomes a challenging problem under heterogeneous clutter environment. As strong clutter energy is accumulated along with the target in multiple frames, low SNR targets are still annihilated in clutter. To achieve effective clutter suppression and dim targets detection, a novel multi-frame procedure for energy accumulation under heterogeneous clutter is proposed in this paper. The presented architecture concerns a Space-Time Adaptive Processing (STAP) processor and a multi-frame detector. The STAP processor calculates the clutter covariance matrix using multi-frame training samples near the cell under test and extracts data contaminated by the target component. The multi-frame detector is developed to detect dim targets and output estimated target track sequences. Finally, simulation results are given to demonstrate the efficacy of the proposed algorithm.

Existing dynamic programming based track-before-detect (DP-TBD) strategies suffer from merit function expansion phenomenon (MFEP), which aggravated the burden of designing the detection threshold. The traditional constant false alarm rate (CFAR) detection is ineffective because the noise energy can not be exactly estimated from the area of merit function expansion. the threshold setting of existing DP-TBD strategies usually resort to the traditional Monte-Carlo counting, the extreme-value theory or its generalized version. For the nonhomogeneous clutter background and the fluctuating target, all of these constant threshold setting strategies inevitably exist the target losing or higher false alarm rate. In addition, for the multi-target scenes, in order to avoid solving high-dimensional optimization problems, existing the most effective DP-TBD methods all use the additional heuristic procedures to extract target trajectories one-by-one from the merit function expansion area by assuming target tracks are always independent. To overcome the aforementioned challenges, a novel one-step greedy optimization TBD algorithm (OSP-TBD) is proposed in this paper. By constraining the physically admissible trajectories, such that the different targets do not occupy the same resolution cell during the same stage and the trajectory with higher merit function (MF) is estimated ahead of others, OSP-TBD can eliminate the MFEP intrinsically and traditional CFAR procedure can be used to detect target adaptively. Besides, the proposed OSP-TBD algorithm can be used to process multi-target situation directly and declare all of the target trajectories corresponding to the states whose MF at the final frame exceed the given detection threshold without any additional heuristic procedure. Numerical simulations are used to assess the performance of the proposed strategies.