Search Results (14)

For modern radars with high resolutions, an extended target may generate more than one observations. The conventional point target-based tracking method can hardly be applied in such scenarios. Recently, the ET-GM-PHD approach has been presented for tracking these extended targets. The performance of such an approach has been influenced by the following disadvantages. First, it has been formulated under the linear Gaussian assumptions. When targets move with nonlinear models, the tracking performance may be rapidly decreased. Second, it neglects the time associations of the estimated states at different time steps, which makes it very challenging to manage targets for the radar systems. In this paper, we present a labeled ET-GM-PHD approach based on the square root cubature information filter (SRCIF) to solve such problems. To be more specific, we, first, utilize the SCRIF for predicting and updating the GM components of the ET-GM-PHD approach. For decreasing the computational cost, a candidate observation extracting method has been put forward in the GM component updating step. Thus, the ET-GM-PHD approach can be adopted to track extended targets with nonlinear motions. Second, a label-based trajectory constructing method has been proposed. By assigning the GM components with different labels before the GM component predicting step, we can obtain the estimated states with different labels. On this basis, the associations between the estimated states and trajectories can be modeled based on these labels. Thus, we can obtain the states and trajectories of multi extended targets simultaneously. The simulation results prove the effectiveness of our approach. Full article

In the context of 3D geometric reconstruction from sequential inverse synthetic aperture radar (ISAR) images, the accurate scatterer trajectory association is a critical step. Aiming at the above problem, an enhanced scatterer trajectory association method is proposed by designing a modified label Gaussian mixture probability hypothesis density (ML-GM-PHD) filtering algorithm. The algorithm commences by constructing a general motion model for scatterers across sequential ISAR images, followed by an in-depth analysis of their motion characteristics. Subsequently, the actual projected positions and measurements of the scattering centers of the observed target are treated as random finite sets, which allows us to reformulate the scatterer trajectory association into a maximum a posteriori (MAP) estimation problem. After that, a ML-GM-PHD filtering algorithm is proposed to realize the scatterer trajectory association. Furthermore, the proposed method is applied to ISAR images in both the forward and reverse directions, enabling the fusion of trajectories from opposing directions to bolster the completeness of the scatterer trajectories. Finally, the factorization method is performed on the scatterer trajectory matrix to implement the 3D geometry reconstruction of the scattering centers in the observed target. Experimental results based on random points and electromagnetic data verify the effectiveness and performance priority of the proposed algorithm. Full article

Different from the standard probability hypothesis density (PHD) and cardinality probability hypothesis density (CPHD) filters, the trajectory PHD (TPHD) and trajectory CPHD (TCPHD) filters employ the sets of trajectories rather than the sets of the targets as the variables for multi-target filtering. The TPHD and TCPHD filters exploit the inherent potential of the standard PHD and CPHD filters to generate the target trajectory estimates from first principles. In this paper, we develop the TPHD and TCPHD filters for pulse Doppler radars (PD-TPHD and PD-TCPHD filters) to improve the multi-target tracking performance in the scenario with clutter. The Doppler radar can obtain the Doppler measurements of targets in addition to the position measurements of targets, and both measurements are integrated into the recursive filtering of PD-TPHD and PD-TCPHD. PD-TPHD and PD-TCPHD can propagate the best augmented Poisson and independent identically distributed multi-trajectory density approximation, respectively, through the Kullback–Leibler divergence minimization operation. Considering the low computational complexity of sequential filtering, Doppler measurements are sequentially applied to the Gaussian mixture implementation. Moreover, we perform the $L$-scan implementations of PD-TPHD and PD-TCPHD. Simulation results demonstrate the effectiveness and robustness of the proposed algorithms in the scenario with clutter. Full article

Under space-based observation conditions, targets are subject to a large number of stars, clutter, false alarms, and other interferences, which can significantly impact the traditional Gaussian mixture probability hypothesis density (GM-PHD) filtering method, leading to tracking biases. To enhance the capability of the traditional GM-PHD method for multi-target tracking in space-based platform observation scenarios, in this article, we propose a GM-PHD algorithm based on spatio-temporal pipeline filtering and enhance the conventional spatio-temporal pipeline filtering method. The proposed algorithm incorporates two key enhancements: firstly, by adaptively adjusting the pipeline’s central position through target state prediction, it ensures continuous target tracking while eliminating noise; secondly, by computing trajectory similarity to distinguish stars from targets, it effectively mitigates stellar interference in target tracking. The proposed algorithm realizes a more accurate estimation of the target by constructing a target state pipeline using the time series and correlating multiple frames of data to achieve a smaller optimal sub-pattern assignment (OSPA) distance and a higher tracking accuracy compared with the traditional algorithm. Through simulations and real-world data validation, the algorithm showcased its capability for multi-target tracking in a space-based context, outperforming traditional methods and effectively addressing the challenge of stellar interference in space-based multi-target tracking. Full article

Efficient multiple target tracking (MTT) is the key to achieving green, precision, and large-scale aquaculture, marine exploration, and marine farming. The traditional MTT methods based on Bayes estimation have some pending problems such as an unknown detection probability, random target newborn, complex data associations, and so on, which lead to an inefficient tracking performance. In this work, an efficient two-stage MTT method based on a YOLOv8 detector and SMC-PHD tracker, named EMTT-YOLO, is proposed to enhance the detection probability and then improve the tracking performance. Firstly, the first detection stage, the YOLOv8 model, which adopts several improved modules to improve the detection behaviors, is introduced to detect multiple targets and derive the extracted features such as the bounding box coordination, confidence, and detection probability. Secondly, the particles are built based on the previous detection results, and then the SMC-PHD filter, the second tracking stage, is proposed to track multiple targets. Thirdly, the lightweight data association Hungarian method is introduced to set up the data relevance to derive the trajectories of multiple targets. Moreover, comprehensive experiments are presented to verify the effectiveness of this two-stage tracking method of the EMTT-YOLO. Comparisons with other multiple target detection methods and tracking methods also demonstrate that the detection and tracking behaviors are improved greatly. Full article

The development of high-tech, dim, small targets, such as drones and cruise missiles, brings great challenges to radar multi-target tracking (MTT), making it necessary to extend the beam dwell time to obtain a high signal-to-noise ratio (SNR). In order to solve the problem of radar sampling time variation exacerbated by extending the beam dwell time when detecting weak targets, a sector-matching (SM) PHD filter is proposed, which combines the actual radar system with a PHD filter and quantifies the relationship between the beam dwell time, the false alarm rate and the detection probability. The proposed filter divides the scanning area into small sectors to obtain actual multi-target measurement times and rederives the prediction and update steps based on the actual sampling time. Furthermore, a state correction step is added before state extraction. Applying the SM structure to the basic Gaussian mixture PHD (GM-PHD) filter and labeled GM-PHD filter, the simulation results demonstrate that the proposed structure can improve the accuracy of multi-weak-target state estimation in the dense clutter and can continuously generate explicit trajectories. The overall real-time performance of the proposed filter is similar to that of the PHD filter. Full article

Unmanned aerial vehicles (UAVs) will become an indispensable part of future sixth-generation (6G)-based mobile networks that can provide flexible deposition, strong adaptability, and high service quality. Under the guarantee of blockchain, UAVs can provide efficient communication or computing services for ground intelligence devices and promote the development of wireless communication. However, as the number of UAVs increases, issues regarding UAV path planning, the handling of emergencies, the intrusion of illegal UAVs, etc., will need to be addressed. This paper proposes an improved Gaussian mixture probability hypothesis density (GM-PHD) filter based on machine learning for the target tracking and recognition of non-cooperative UAV swarms. Simulation results demonstrate that the improved filter can effectively suppress clutter interference in complex environments and improve the performance of multi-target recognition and trajectory tracking compared with the traditional GM-PHD filter. Full article

In order to solve the problem that the measurement noise covariance may be unknown or change with time in actual multi-target tracking, this paper brings the variational Bayesian approximation method into the trajectory probability hypothesis density (TPHD) filter and proposes a variational Bayesian TPHD (VB-TPHD) filter to obtain measurement noise covariance adaptively. By modeling the unknown covariance as the random matrix that obeys the inverse gamma distribution, VB-TPHD filter minimizes the Kullback–Leibler divergence (KLD) and estimates the sequence of multi-trajectory states with noise covariance matrices simultaneously. We propose the Gaussian mixture VB-TPHD (AGM-VB-TPHD) filter under adaptive newborn intensity for linear Gaussian models and also give the extended Kalman (AEK-VB-TPHD) filter and unscented Kalman (AUK-VB-TPHD) filter in nonlinear Gaussian models. The simulation results prove the effectiveness of the idea that the VB-TPHD filter can form robust and stable trajectory filtering while learning adaptive measurement noise statistics. Compared with the tag-VB-PHD filter, the estimated error of the VB-TPHD filter is greatly reduced, and the estimation of the trajectory number is more accurate. Full article

The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can effectively track multiple targets in a single scenario. However, for GM-PHD, unknown target behavior, e.g., target birth or target intersection, produces difficulties in terms of accurate estimation. First of all, GM-PHD assumes the model parameters about the birth target are prior information, which results in the inability to detect the birth target that occurs at random in complex scenarios. Then, since the measurements generated by the intersected targets overlap each other, GM-PHD cannot distinguish these targets, resulting in a biased estimation of the state and number of targets. To solve these problems, this paper proposes an improved GM-PHD filter with a birth intensity and spawned intensity updating method based on the trajectory situation feedback. In the filtering process, the trajectory initiation feedback formed by the rule-based correlation of Gaussian components is introduced to GM-PHD to adjust the birth intensity in real time, which is used to improve the detection of birth targets. Simultaneously, the analysis of trajectory situation is designed to determine the relative motion trend between targets. On this basis, the filter improves the recognition of the intersected targets by enhancing the spawned intensity. Simulation results demonstrate that the proposed algorithm achieves better performance on the state and number of targets in complex scenarios, and shows superiority to other GM-PHD filters. Full article

In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity. Full article

Intelligent transportation systems (ITSs) play an increasingly important role in traffic management and traffic safety. Smart cameras are the most widely used sensors in ITSs. However, cameras suffer from a reduction in detection and positioning accuracy due to target occlusion and external environmental interference, which has become a bottleneck restricting ITS development. This work designs a stable perception system based on a millimeter-wave radar and camera to address these problems. Radar has better ranging accuracy and weather robustness, which is a better complement to camera perception. Based on an improved Gaussian mixture probability hypothesis density (GM-PHD) filter, we also propose an optimal attribute fusion algorithm for target detection and tracking. The algorithm selects the sensors’ optimal measurement attributes to improve the localization accuracy while introducing an adaptive attenuation function and loss tags to ensure the continuity of the target trajectory. The verification experiments of the algorithm and the perception system demonstrate that our scheme can steadily output the classification and high-precision localization information of the target. The proposed framework could guide the design of safer and more efficient ITSs with low costs. Full article

The 5G cellular network is expected to provide core service platform for the expanded Internet of Things (IoT) by supporting enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low latency communications (URLLC). Unmanned aerial vehicles (UAVs), also known as drones, provide civil, commercial, and government services in various fields. Particularly in a 5G IoT scenario, UAV-aided network communications will fulfill an increasingly important role and will require the tracking of multiple UAV targets. As UAVs move quickly, maintaining the stability of the communication connection in 5G will be a challenge. Therefore, it is necessary to track the trajectory of UAVs. At present, the GM-PHD filter has a problem that the new target intensity must be known, and it cannot obtain the moving target trajectory and the influence of the clutter is likely to cause false alarm. A UAV-PHD filter is proposed in this work to improve the traditional GM-PHD filter by applying machine learning to the emergency detection and trajectory tracking of UAV targets. An out-of-sight detection algorithm for multiple UAVs is then presented to improve tracking performance. The method is assessed by simulation using MATLAB, and OSPA distance is utilized as an evaluation indicator. The simulation results illustrate that the proposed method can be applied to the tracking of multiple UAV targets in future 5G-IoT scenarios, and the performance is superior to the traditional GM-PHD filter. Full article

Under realistic environmental conditions, heuristic-based data association and map management routines often result in divergent map and trajectory estimates in robotic Simultaneous Localization And Mapping (SLAM). To address these issues, SLAM solutions have been proposed based on the Random Finite Set (RFS) framework, which models the map and measurements such that the usual requirements of external data association routines and map management heuristics can be circumvented and realistic sensor detection uncertainty can be taken into account. Rao–Blackwellized particle filter (RBPF)-based RFS SLAM solutions have been demonstrated using the Probability Hypothesis Density (PHD) filter and subsequently the Labeled Multi-Bernoulli (LMB) filter. In multi-target tracking, the LMB filter, which was introduced as an efficient approximation to the computationally expensive $\delta$ -Generalized LMB ( $\delta$ -GLMB) filter, converts its representation of an LMB distribution to $\delta$ -GLMB form during the measurement update step. This not only results in a loss of information yielding inferior results (compared to the $\delta$ -GLMB filter) but also fails to take computational advantages in parallelized implementations possible with RBPF-based SLAM algorithms. Similar to state-of-the-art random vector-valued RBPF solutions such as FastSLAM and MH-FastSLAM, the performances of all RBPF-based SLAM algorithms based on the RFS framework also diverge from ground truth over time due to random sampling approaches, which only rely on control noise variance. Further, the methods lose particle diversity and diverge over time as a result of particle degeneracy. To alleviate this problem and further improve the quality of map estimates, a SLAM solution using an optimal kernel-based particle filter combined with an efficient variant of the $\delta$ -GLMB filter ( $\delta$ -GLMB-SLAM) is presented. The performance of the proposed $\delta$ -GLMB-SLAM algorithm, referred to as $\delta$ -GLMB-SLAM2.0, was demonstrated using simulated datasets and a section of the publicly available KITTI dataset. The results suggest that even with a limited number of particles, $\delta$ -GLMB-SLAM2.0 outperforms state-of-the-art RBPF-based RFS SLAM algorithms. Full article

Hough Transform (HT), which has a low sensitivity to local faults and good ability in suppressing noise and clutters, usually applies to trajectory detection in a cluttered environment. This paper describes its application for detecting the trajectories of extended targets in three-dimensional measurements, i.e., a two-dimensional positional information and its measuring time. For taking the full merits of a multi-scan, the measuring time is regarded as a variable for the time axis. This correspondence extends the HT to 3-dimensional data. Meanwhile, a three-dimensional accumulator matrix is built for the purpose of voting. The voting process is done in an iterative way by selecting the 3D-line with the most votes and removing the corresponding measurements in each step. The three dimensional Hough Transform-based extended target track-before-detect technique (3DHT-ET-TBD), proposed here, is suitable to track the extended target and non-extended target simultaneously and few false alarm trajectories arise. Both the real data and simulated data are exploited to evaluate its performance. Compared with the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter based methods and a 4DHT-TBD algorithm, the 3DHT-ET-TBD is a more promising approach for multi-extended target tracking problems due to its high efficiency and low computation, especially in situations where the noise and false alarms are considerably high but few measurements are generated by the extended targets. Full article