Search Results (41)

Accurate and consistent self-localization is essential for multi-UAV aerial missions in complex dynamic environments. However, communication constraints and heterogeneous sensor reliability variations often lead to cumulative localization errors and degraded robustness in conventional fusion frameworks. To address these challenges, this paper proposes a distributed cooperative localization framework integrating deep temporal feature learning, heterogeneous multi-sensor fusion, and consistency-aware distributed state estimation. First, an LSTM-based staged fusion strategy is designed to integrate VIO, GPS, and UWB measurements for accurate single-UAV localization. Second, a Squeeze-and-Excitation LSTM Self-Attention (SE-LSTM-SA) network is developed to adaptively recalibrate heterogeneous sensor channels and enhance temporal feature extraction under dynamic sensing conditions. Finally, a consistency-aware distributed fusion mechanism based on the Labeled Multi-Bernoulli (LMB) framework is introduced to improve inter-UAV state consistency through iterative local-neighbor information exchange. Experiments conducted on the XTDrone platform demonstrate that the proposed framework achieves superior localization accuracy compared with traditional EKF and conventional LSTM-based methods. Specifically, the proposed method achieves lower RMSE, MAE, and Maximum Prediction Error (MaxPE), while significantly improving global consistency performance. Experimental results demonstrate that the proposed framework provides accurate and consistent localization performance for multi-UAV systems in complex dynamic environments. Full article

In complex detection environments, unknown detection probability and clutter rate hinder accurate tracking of cluster targets. To address this issue, this paper proposes a novel multi-sensor adaptive generalized labeled multi-Bernoulli (MS-AGLMB) filter. Specifically, we consider interactions among cluster members and adopt a virtual leader–follower model to describe cluster kinematics. Given unknown environmental parameters, we employ an adaptive cardinalized probability hypothesis density (CPHD) filter to estimate the detection probability and clutter rate in real time. Furthermore, we use Gibbs sampling to efficiently truncate GLMB association hypotheses, obtaining the posterior density and solving the multi-sensor measurement partitioning problem. A joint prediction and update strategy enables simultaneous estimation of target trajectories, detection probability, clutter rate, and cluster structure. Simulation results demonstrate that the proposed algorithm achieves greater robustness in scenarios with time-varying detection probability and clutter rate, outperforming comparison filters in cluster target tracking. Full article

Active sonar tracking of multiple underwater targets is frequently challenged by intense reverberation, which leads to sonar returns that are both non-stationary and non-Gaussian. In such scenarios, the generalized labeled multi-Bernoulli (GLMB) filter, which relies on a Gaussian assumption, often experiences a rise in an Optimal Subpattern Assignment (OSPA) distance, along with recurrent label switching. To mitigate this problem, a robust delta-generalized labeled multi-Bernoulli technique (ST-δ-GLMB) is introduced; it characterizes noise using a Student’s t-distribution and employs variational Bayes to estimate the corresponding parameters. More precisely, the Student’s t-distribution is utilized to represent measurement non-stationarity, and an online variational Bayesian estimation of the noise parameters is conducted within a multi-target framework based on the Student’s t-model. Moreover, without altering the GLMB data-association and label-management machinery, we derive closed-form updates and propagation for the Student’s t-parameters, thereby keeping the recursive computational burden and practical implementability under control. Finally, Monte Carlo simulations and lake-trial data demonstrate that, under non-stationary and heavy-clutter conditions, ST-δ-GLMB maintains stable track continuity and accurate target-number (cardinality) estimates in the presence of non-stationary measurements. Full article

Compared with single-radar systems, multi-radar systems generally achieve superior detection performance due to their spatial and frequency diversity. To further enhance multi-target tracking, this paper proposes a multi-radar distributed fusion algorithm aided by multi-feature information. Each radar computes its measurement-updated Labeled Multi-Bernoulli (LMB) posterior, and track association is performed using multi-feature information extracted from radar echoes, including Doppler frequency and signal-to-noise ratio (SNR), improving robustness in complex scenarios. Distributed fusion is then carried out via the Generalized Covariance Intersection (GCI) algorithm. Simulation results show that, compared with other fusion methods, the proposed approach achieves superior multi-target tracking accuracy while maintaining lower computational cost. Full article

Aiming to improve the accuracy of multi-target tracking in multi-sensor scenarios, this paper proposes a centralized multi-sensor (MS) generalized labeled multi-Bernoulli (GLMB) smoother, abbreviated as MS-GLMB-S. The developed smoother is built on the multi-target forward–backward Bayesian smoothing framework, which uses an MS-GLMB filter for forward recursion and is subsequently followed by backward propagation via the multi-sensor backward corrector to obtain the GLMB smoothing density. In the backward smoothing process, expressions for the multi-sensor backward corrector and the multi-target smoothing density are detailed. By deriving the time-decoupled form of the smoothing weight, a suboptimal Gibbs sampling method is introduced to achieve efficient implementation of the proposed smoother, enabling independent sampling across each sensor at different time steps within the lag interval during the backward smoothing process. Additionally, a Gaussian mixture implementation of MS-GLMB-S is formulated. Simulations conducted in both linear and nonlinear scenarios demonstrate the effectiveness and real-time performance of MS-GLMB-S. Full article

This paper addresses multisensor multitarget tracking where the sensor network can potentially be compromised by false data injection (FDI) attacks. The existence of the targets is not known and time-varying. A tracking algorithm is proposed that can detect the possible FDI attacks over the networks. First, a local estimate is generated from the measurements of each sensor based on the labeled multi-Bernoulli (LMB) filter. Then, a detection method for FDI attacks is derived based on the Kullback–Leibler divergence (KLD) between LMB random finite set (RFS) densities. The statistical characteristics of the KLD are analyzed when the measurements are secure or compromised by FDI attacks, from which the value of the threshold is selected. Finally, the global estimate is obtained by minimizing the weighted sum of the information gains from all secure local estimates to itself. A set of suitable weight parameters is selected for the information fusion of LMB densities. An efficient Gaussian implementation of the proposed algorithm is also presented for the linear Gaussian state evolution and measurement model. Experimental results illustrate that the proposed algorithm can provide reliable tracking performance against the FDI attacks. Full article

The $\delta$-GLMB filter is an analytic solution to the multi-target Bayes recursion used in multi-target tracking. It extends the Generalised Labelled Multi-Bernoulli (GLMB) framework by providing an efficient and scalable implementation while preserving track identities, making it a widely used approach in the field. Theoretically, the $\delta$-GLMB filter handles uncertainties in measurements in its filtering procedure. However, in practice, degeneration of the measurement quality affects the performance of this filter. In this paper, we discuss the effects of increasing measurement uncertainty on the $\delta$-GLMB filter and also propose two heuristic methods to improve the performance of the filter in such conditions. The base idea of the proposed methods is to utilise the information stored in the history of the filtering procedure, which can be used to decrease the measurement uncertainty effects on the filter. Since GLMB filters have shown good results in the field of multi-target tracking, an uncertainty-immune $\delta$-GLMB can serve as a strong tool in this area. In this study, the results indicate that the proposed heuristic ideas can improve the performance of filtering in the presence of uncertain observations. Experimental evaluations demonstrate that the proposed methods enhance track continuity and robustness, particularly in scenarios with low detection rates and high clutter, while maintaining computational feasibility. Full article

In this research, a multi-channel target speech enhancement scheme is proposed that is based on deep learning (DL) architecture and assisted by multi-source tracking using a labeled random finite set (RFS) framework. A neural network based on minimum variance distortionless response (MVDR) beamformer is considered as the beamformer of choice, where a residual dense convolutional graph-U-Net is applied in a generative adversarial network (GAN) setting to model the beamformer for target speech enhancement under reverberant conditions involving multiple moving speech sources. The input dataset for this neural architecture is constructed by applying multi-source tracking using multi-sensor generalized labeled multi-Bernoulli (MS-GLMB) filtering, which belongs to the labeled RFS framework, to obtain estimations of the sources’ positions and the associated labels (corresponding to each source) at each time frame with high accuracy under the effect of undesirable factors like reverberation and background noise. The tracked sources’ positions and associated labels help to correctly discriminate the target source from the interferers across all time frames and generate time–frequency (T-F) masks corresponding to the target source from the output of a time-varying, minimum variance distortionless response (MVDR) beamformer. These T-F masks constitute the target label set used to train the proposed deep neural architecture to perform target speech enhancement. The exploitation of MS-GLMB filtering and a time-varying MVDR beamformer help in providing the spatial information of the sources, in addition to the spectral information, within the neural speech enhancement framework during the training phase. Moreover, the application of the GAN framework takes advantage of adversarial optimization as an alternative to maximum likelihood (ML)-based frameworks, which further boosts the performance of target speech enhancement under reverberant conditions. The computer simulations demonstrate that the proposed approach leads to better target speech enhancement performance compared with existing state-of-the-art DL-based methodologies which do not incorporate the labeled RFS-based approach, something which is evident from the 75% ESTOI and PESQ of 2.70 achieved by the proposed approach as compared with the 46.74% ESTOI and PESQ of 1.84 achieved by Mask-MVDR with self-attention mechanism at a reverberation time (RT60) of 550 ms. Full article

A distributed network of multiple unmanned aerial vehicles (UAVs) equipped with airborne passive bistatic radar (APBR) can form a passive detection network through cooperative networking technology, a novel passive early warning detection system. Its multi-target tracking performance has a significant impact on situational awareness of the detection area. This paper proposes a passive multi-sensors joint optimization control algorithm based on task adaptive switching, with the aim of addressing the impact of limited UAV sensors’ field of view (FOV) on multi-target tracking performance in APBR networks. Firstly, for a single UAV node, the Poisson Labeled Multi-Bernoulli (PLMB) filter is selected as the local filter of each node, with the objective of obtaining the local multi-target density independently. Subsequently, the consensus arithmetic average fusion rule is employed to address the multi-sensors density fusion problem in APBR networks. This enables the acquisition of the global multi-target density and multi-target tracks of the network. The task adaptive switching mechanism of the nodes is constructed further based on the partially observable Markov decision process (POMDP), and the objective functions for the UAV to perform the search task and the tracking task are derived based on differential entropy, respectively. Ultimately, a multi-node joint optimization control algorithm is devised. The simulation experiment demonstrates that the proposed algorithm is capable of effective control of multiple nodes to solve the multi-target search and tracking problem when the node FOV is limited. This further improves the multi-target tracking and fusion capability of the distributed APBR network. Full article

Network-group targets are a set of objectives that adhere to a shared communication protocol, perform common tasks, and exhibit relatively coordinated movements. Typically, network-group targets emit radar and communication signals. However, they often employ a silent mode to evade our perception. Despite this, they continue to exchange data through their communication networks. By intercepting the communication signals of these targets, this paper proposes a method for estimating the state and network topology of network-group targets based on random finite set (RFS) theory. This method is based on the modeling of network-group targets using a labeled multi-Bernoulli (LMB) RFS. In state estimation, the usual method involves extracting the localization parameters from the signals in the first step and use these parameters for target tracking in the second step. This study focused on estimating the kinematic states of network-group targets using communication signals containing delay and Doppler information received by multiple mobile sensor platforms. The proposed method considers the coherency between frequency-hopping pulses in the signals, resulting in an improved estimation performance. In addition, considering that network-group targets require network access for information exchange, graph theory concepts were utilized to estimate the network topology of network-group targets by address measurement. The simulation results validated the effectiveness of the proposed method and demonstrated its superior performance. Full article

This paper discusses a key technique for passive localization and tracking of radiation sources, which obtains the motion trajectory of radiation sources carried by unmanned aerial vehicles (UAVs) by continuously or periodically localizing it without the active participation of the radiation sources. However, the existing methods have some limitations in complex signal environments and non-stationary wireless propagation that impact the accuracy of localization and tracking. To address these challenges, this paper extends the $\delta$-generalized labeled multi-Bernoulli (GLMB) filter to the scenario of passive localization and tracking based on the random finite-set (RFS) framework and provides the extended Kalman filter (EKF) and unscented Kalman filter (UKF) implementations of the $\delta$-GLMB filter, which fully take into account the nonlinear motion of the radiation source. By modeling the “obstacle scenario” and the influence of external factors (e.g., weather, terrain), our proposed GLMB filter can accurately track the target and capture its motion trajectory. Simulation results verify the effectiveness of the GLMB filter in target identification and state tracking. Full article

The electromagnetic wave transmitted by the millimeter-wave radar can penetrate flames, smoke, and the high-temperature field, and is the main sensor for detecting disaster victims in closed spaces. However, a moving target in the closed space will produce a considerable number of false detections in the point cloud data collected by the radar due to multipath scattering. The false detections lead to false trajectories generated by multi-target tracking filters, such as the labeled multi-Bernoulli (LMB) filter, which, therefore, leads to inaccurate estimation of the number of pedestrians. Addressing this problem, in this paper, a three-class combination of the clutter point clouds model is proposed: static clutter, non-continuous dynamic clutter (NCDC), and continuous dynamic clutter (CDC). The model is based on the spatial and temporal distribution characteristics of the CDC sequence captured by a two-dimensional (2D) millimeter-wave (MMW) radar. However, in open space, CDC appears infrequently in radar tracking applications, and thus has not been considered in multi-target tracking filters such as the LMB filter. This leads to confusion between the CDC point cloud collected by the high-resolution radar in closed spaces and the real-target point cloud. To solve this problem, the impact mechanism of the LMB filter on prediction, update, and state estimation is modeled in this paper in different stages based on the temporal and spatial distribution characteristics of CDC. Finally, a hybrid clutter model-based LMB filter (HCM-LMB) is proposed, which focuses on scenes where NCDC and CDC are mixed. The filter introduces the temporal and spatial distribution characteristics of NCDC based on the original LMB filter, and improves the prediction, update, and state estimation of the original filter by combining the impact mechanism model and the new CDC prediction, CDC estimation, and false trajectory label management algorithm. Experiments were conducted on pedestrians in building corridors using 2D MMW radar perception. The experimental results show that under the influence of CDC, the total number of pedestrians estimated by the HCM-LMB filter was reduced by 22.5% compared with that estimated by the LMB filter. Full article

This paper proposes a centralized MTT method based on a state-of-the-art multi-sensor labeled multi-Bernoulli (LMB) filter in underwater multi-static networks with autonomous underwater vehicles (AUVs). The LMB filter can accurately extract the number of targets and trajectories from measurements affected by noise, missed detections, false alarms and port–starboard ambiguity. However, its complexity increases as the number of sensors increases. In addition, due to the time-varying underwater environment, AUV detection probabilities are time-varying, and their mismatches often lead to poor MTT performance. Consequently, we detail a robust multi-sensor LMB filter that estimates detection probabilities and multi-target states simultaneously in real time. Moreover, we derive an effective approximate form of the multi-sensor LMB filter using Kullback–Leibler divergence and develop an efficient belief propagation (BP) implementation of the multi-sensor LMB filter. Our method scales linearly with the number of AUVs, providing good scalability and low computational complexity. The proposed method demonstrates superior performance in underwater multi-AUV network MTT simulations. Full article

The previous multi-frame version of the generalized labeled multi-Bernoulli model (MF-GLMB) only accounts for standard measurement models. It is not suitable for application in the detection and tracking of multiple weak targets (low signal-to-noise ratio) due to the measurement information loss. In this paper, we introduce a MF-GLMB model that formally incorporates a track-before-detect scheme for point targets using an image sensor model. Furthermore, a belief propagation algorithm is adopted to approximately calculate the marginal association probabilities of the multi-target posterior density. In this formulation, an MF-GLMB model based on the track-before-detect measurement model (MF-GLMB-TBD smoothing) enables multi-target posterior recursion for multi-target state estimation. By taking the entire history of the state estimation into account, MF-GLMB-TBD smoothing achieves superior performance in estimation precision compared with the corresponding GLMB-TBD filter. The simulation results demonstrate that the performance of the proposed algorithm is comparable to or better than that of the Gibbs sampler-based version. Full article

In this paper, we propose the specific recursion formula for the generalized labeled multi-Bernoulli filter based on the track-before-detect strategy (GLMB-TBD) using a belief propagation algorithm. The proposed method aims to track multiple weak targets with superior performance. Compared to the Murty algorithm-based and Gibbs sampling-based implementation of GLMB-TBD filter, the proposed algorithm improves the tracking accuracy of multiple weak targets without pruning operation to preserve the relevant association information. The superior performance in tracking accuracy of the algorithm is validated for simulated scenarios using OSPA⁽²⁾ metric. More importantly, the simulation results demonstrate that the proposed algorithm outputs both the Gibbs sampling-based version and Murty algorithm-based version in computational cost due to linear complex in the number of both Bernoulli components and measurements. Full article