Probabilistic Data Association Method Research Articles

Improved Gaussian processes linear JPDA filter for multiple extended targets tracking in dense clutter

To track multiple extended targets in dense clutter, an improved Gaussian processes linear joint probabilistic data association (IGP-LJPDA) method is proposed. This method consists of two stages. In the first stage, measurements are associated with targets, and pseudo-measurements are constructed. By integrating linear JPDA with Gaussian processes, probabilities for each measurement's assignment to each target are determined. Following this, a novel pseudo-measurement model is devised by combining the marginal probability of each target with measurements belonging to their respective basic point neighborhood, enabling more efficient state updates. In the second stage, pseudo-measurements are associated with the contour, and states of extended targets are updated accordingly. By associating pseudo-measurements with Gaussian processes basic points of each target, the algorithm achieves globally optimal association. The proposed algorithm demonstrates a notable improvement in Intersection over Union (IOU) metrics in environments with significantly higher clutter rates compared to existing methods.

Impact of moving target on underwater positioning by using state measurement

The localization of moving targets in an underwater acoustic wireless sensor network (UAWSN) is inaccurate due to the various underwater forces (viscous, hydrodynamic forces, perturbation of underwater). The false measurements in the sensor network cause position errors and velocity errors which disrupt the localization of the moving target. A randomly fluctuated spillover effect is introduced in the present paper. The absorption losses generated due to the spillover effect cause false measurements of the moving target. Theorem 1 describes the genesis of these absorption losses and their consequences in UAWSN. The measurements from each moving target in the presence of absorption losses are formulated in the elliptical region. A joint probabilistic data association (JPDA) method is proposed to quantify the false measurements in the elliptical region. A moving target state estimation (MTSE) algorithm is proposed to eliminate the false measurements from the moving targets and to measure the localization of moving targets with the help of the propagation speed of targets. The theoretical measurements of position RMSE and velocity RMSE are verified with standard methods. The proposed MTSE method improves the localization performance of the moving targets by 29.42 % and reduces 32.16 % of position errors and 36.23 % of velocity errors up to 550 ​m. The proposed algorithm will be useful for the sub-aquatic Internet of underwater things (IoUT).

Target‐tracking algorithm based on improved probabilistic data association

AbstractWhen tracking a single manoeuvring target in clutter environment, when the number of effective measurements within the detection threshold is small, it usually has a greater and more obvious impact on target‐tracking results. If the observation data error is large at this time, the tracking position and speed error will be larger. To solve this problem, a target‐tracking algorithm based on improved probabilistic data association is proposed in this paper. By dynamically adjusting the detection threshold, the effective quantity within the detection threshold of each frame is basically stable. Simulation results show that the improved algorithm is more accurate in location and speed than the traditional probabilistic data association method and Kalman filter, and the availability and effectiveness of the algorithm are verified.

Enabling Plug-and-Play and Crowdsourcing SLAM in Wireless Communication Systems

Simultaneous localization and mapping (SLAM) during communication is emerging. This technology promises to provide information on propagation environments and transceivers’ location, thus creating several new services and applications for the Internet of Things and environment-aware communication. Using crowdsourcing data collected by multiple agents appears to be much potential for enhancing SLAM performance. However, the measurement uncertainties in practice and biased estimations from multiple agents may result in serious errors. This study develops a robust SLAM method with measurement plug-and-play and crowdsourcing mechanisms to address the above problems. First, we divide measurements into different categories according to their unknown biases and realize a measurement plug-and-play mechanism by extending the classic belief propagation (BP)-based SLAM method. The proposed mechanism can obtain the time-varying agent location, radio features, and corresponding measurement biases (such as clock bias, orientation bias, and received signal strength model parameters), with high accuracy and robustness in challenging scenarios without any prior information on anchors and agents. Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in which multiple agents cooperate to construct and refine the radio map in a decentralized manner. Our study presents the first BP-based crowdsourcing that resolves the “double count” and “data reliability” problems through the flexible application of probabilistic data association methods. Numerical results reveal that the crowdsourcing mechanism can further improve the accuracy of the mapping result, which, in turn, ensures the decimeter-level localization accuracy of each agent in a challenging propagation environment.

A Detachable and Expansible Multisensor Data Fusion Model for Perception in Level 3 Autonomous Driving System

Various sensors are adopted by autonomous driving systems to perceive objects and surroundings. Thus, the multi-sensor data fusion techniques become essential to combine different sensors’ advantages for better perception performance. However, the current multi-sensor data fusion techniques suffer from the high cost of computation resources, low expansibility for more diverse sensors, and insufficient systematic consideration for modeling. This paper first constructs a detachable and expansible multi-sensor data fusion model based on three main modules: front fusion, global fusion, and synthesizer, where the methods for flexible association gating and virtual targets have been designed. The model can be disassembled and configured for different trim levels of vehicles and is easily expansible for adding more heterogeneous sensors. Next, the presented multi-sensor data fusion model is compared with the cheap Joint Probabilistic Data Association (C-JPDA) method. The comparison shows the superior accuracy of the designed model on false association and effective narrowing of the variance of object detection. Finally, the presented multi-sensor data fusion model is integrated into an embedded system and experimented on urban roads and highways with the engaged Level 3 autonomous driving function. The experiment results indicate that the proposed model has excellent sensor data fusion performance and provides accurate and timely object information in the Level 3 autonomous driving system.

Reinforcement Learning-Based Data Association for Multiple Target Tracking in Clutter.

Data association is a crucial component of multiple target tracking, in which each measurement obtained by the sensor can be determined whether it belongs to the target. However, many methods reported in the literature may not be able to ensure the accuracy and low computational complexity during the association process, especially in the presence of dense clutters. In this paper, a novel data association method based on reinforcement learning (RL), i.e., the so-called RL-JPDA method, has been proposed for solving the aforementioned problem. In the presented method, the RL is leveraged to acquire available information of measurements. In addition, the motion characteristics of the targets are utilized to ensure the accuracy of the association results. Experiments are performed to compare the proposed method with the global nearest neighbor data association method, the joint probabilistic data association method, the fuzzy optimal membership data association method and the intuitionistic fuzzy joint probabilistic data association method. The results show that the proposed method yields a shorter execution time compared to other methods. Furthermore, it can obtain an effective and feasible estimation in the environment with dense clutters.

Reverberation reduction based on multi-ping association in a moving target scenario.

Conventional reverberation reduction methods are conducted with single-ping data and may fail in a low signal-to-reverberation ratio (SRR) environment. To improve the performance of reverberation reduction, multi-ping data are fully considered in this paper. The reverberation can be treated as a combination of the steady component of reverberation and reverberation fluctuations, and then an alternating direction multiplier method is proposed to reduce the steady component of the reverberation. By exploiting the evolution of the target location along multiple pings, the reverberation fluctuation is reduced by the probabilistic data association method. The proposed method was verified by the field data, and the results show that compared with the accelerated proximal gradient method, the sparse coefficient is improved by a factor of 1.23, and the signal excess is improved by an average value of 2.0 dB. In addition, the performance of the proposed method is found to be closely related to the signal-to-reverberation-fluctuation ratio rather than only the SRR.

Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors.

In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing; (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability problem in local tracking for a single BO sensor scenario, we propose a novel local integrated probabilistic data association (LIPDA) method for target measurement state tracking. The proposed approach enables eliminating most of the clutter measurement disturbance with increased target measurement accuracy. In the central tracking, the fusion center uses the proposed distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach to sequentially fuse the OOS information transmitted by each BO sensor. The track management is carried out at local sensor level and also at the fusion center by using the recursively calculated probability of target existence as a track quality measure. The efficiency of the proposed methodology was validated by intensive numerical experiments.

Simultaneous tracking of a maneuvering ship and its wake using Gaussian processes

Maneuvering ship tracking has applications in maritime surveillance and the performance of tracking algorithms is affected by background clutter and ship-generated wake. Previous works consider the wake as a form of clutter with some specific spatial distribution. In this paper, the Kelvin wake, which assumes that the ship-generated wake is V-shaped, is modelled as an extended target with an irregular and time-varying shape. Thus, the problem of maneuvering ship tracking in the presence of wake becomes a special multiple target tracking problem, where the ship is a point target and the wake is an extended target. A Gaussian Processes based Multiple Model Joint Probabilistic Data Association (GP-MM-JPDA) method is proposed to estimate the ship state and the wake state simultaneously. Two versions of the GP-MM-JPDA are implemented. The first one can be applied to most multiple target tracking problems and the second one is designed especially for the ship tracking problem. Both versions can improve the ship tracking performance. In addition, the proposed methods can also infer the ship length using the estimated wake angle, which facilitates ship classification. Simulation results show the effectiveness of the proposed methods.

Low Altitude Georeferencing for Imaging Sensors in Maritime Tracking

This paper presents a method for georeferencing low-altitude camera sensors, both infrared and electro-optical, in a maritime context. Accurate georeferencing require very high precision for the object pixel coordinates due to sensor resolution. To achieve this we refine the bounding boxes provided by an SSD object detector using the Sobel operator and the Hough transform. Using real world data this method is applied in a maritime tracking system based on the Joint Integrated Probabilistic Data Association method and compared to radar tracking. The georeferenced cameras surpassed radar performance in several of the benchmarks and maintained tracks with greater reliability at the cost of reduced position accuracy.

Scalable Data Association for Extended Object Tracking

Tracking extended objects based on measurements provided by light detection and ranging (LIDAR) and millimeter wave radio detection and ranging (RADAR) sensors is a key task to obtain situational awareness in important applications including autonomous driving and indoor robotics. In this paper, we propose probabilistic data association methods for localizing and tracking of extended objects that originate an unknown number of measurements. Our approach is based on factor graphs and the sum-product algorithm (SPA). In particular, we reduce computational complexity in a principled manner by means of “stretching” factors in the graph. After stretching, new variable and factor nodes have lower dimensions than the original nodes. This leads to a reduced computational complexity of the resulting SPA. One of the introduced methods is based on an overcomplete description of data association uncertainty and has a computational complexity that only scales quadratically in the number of objects and linearly in the number of measurements. Without relying on suboptimal preprocessing steps such as a clustering of measurements, it can localize and track multiple objects that potentially generate a large number of measurements. Simulation results confirm that despite their lower computational complexity, the proposed methods can outperform reference methods based on clustering.

Doppler Data Association Scheme for Multi-Target Tracking in an Active Sonar System.

In many wireless sensors, the target kinematic states include location and Doppler information that can be observed from a time series of range and velocity measurements. In this work, we present a tracking strategy for comprising target velocity components as part of the measurement supplement procedure and evaluate the advantages of the proposed scheme. Data association capability can be considered as the key performance for multi-target tracking in an active sonar system. Then, we proposed an enhanced Doppler data association (DDA) scheme which exploits target range and target velocity components for linear multi-target tracking. If the target velocity measurements are not incorporated into target kinematic state tracking, the linear filter bank for the combination of target velocity components can be implemented. Finally, a significant enhancement in the multi-target tracking capability provided by the proposed DDA scheme with the linear multi-target combined probabilistic data association method is demonstrated in a sonar underwater scenario.

Improved Joint Probabilistic Data Association (JPDA) Filter Using Motion Feature for Multiple Maneuvering Targets in Uncertain Tracking Situations

To track multiple maneuvering targets in cluttered environments with uncertain measurement noises and uncertain target dynamic models, an improved joint probabilistic data association-fuzzy recursive least squares filter (IJPDA-FRLSF) is proposed. In the proposed filter, two uncertain models of measurements and observed angles are first established. Next, these two models are further employed to construct an additive fusion strategy, which is then utilized to calculate generalized joint association probabilities of measurements belonging to different targets. Moreover, the obtained probabilities are applied to replace the joint association probabilities calculated by the standard joint probabilistic data association (JPDA) method. Considering the advantage of the fuzzy recursive least squares filter (FRLSF) on tracking a single maneuvering target, which can relax the restrictive assumption of measurement noise covariances and target dynamic models, FRLSF is still used to update the state of each target track. Thus, the proposed filter can not only provide the advantage of FRLSF but can also adjust the weights of measurements and observed angles in the generalized joint association probabilities adaptively according to their uncertainty. The performance of the proposed filter is evaluated in two experiments with simulation data and real data. It is found to be better than the performance of other three filters in terms of the tracking accuracy and the average run time.

Multitarget geolocation via an agricultural octorotor based on orthographic projection and data association

In recent years, unmanned aerial vehicles with onboard spectral sensors have been used in detecting diseases in the agricultural fields. Geolocation, i.e. calculating the global coordinate of identified diseased regions based on images taken, is an important step in automating such a scouting operation. In this paper, the problem of geolocating multiple diseased regions in an image is studied. Based on the assumptions of stationary, two-dimensional shallow target plants and hover flight, an orthographic projection-based measurement model is developed. A probabilistic data association method is used to analyze the measurements from different target sources and a Kalman filter is designed to estimate the suspected diseased leaves’ position. To the best of the authors’ knowledge, it is the first time a data association technique is used in for locating multiple-diseased plants in agriculture applications. Additionally, the designed Kalman filter is based on conditions pertinent to small crops and is less computationally intensive than the typically used extended Kalman filter. Both simulation and ad hoc experiments are used to validate the proposed multitarget geolocation algorithm.

A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment.

The problem of data association for target tracking in a cluttered environment is discussed. In order to improve the real-time processing and accuracy of target tracking, based on a probabilistic data association algorithm, a novel data association algorithm using distance weighting was proposed, which can enhance the association probability of measurement originated from target, and then using a Kalman filter to estimate the target state more accurately. Thus, the tracking performance of the proposed algorithm when tracking non-maneuvering targets in a densely cluttered environment has improved, and also does better when two targets are parallel to each other, or at a small-angle crossing in a densely cluttered environment. As for maneuvering target issues, usually with an interactive multi-model framework, combined with the improved probabilistic data association method, we propose an improved algorithm using a combined interactive multiple model probabilistic data association algorithm to track a maneuvering target in a densely cluttered environment. Through Monte Carlo simulation, the results show that the proposed algorithm can be more effective and reliable for different scenarios of target tracking in a densely cluttered environment.

Investigation of wind farm effects on radar multiple target tracking

The increasing demand on wind farm (WF) installations has been raising the necessity and significance of performance assessment of electronic systems in the vicinity of WFs. Using a signal model for WF scattering, the interaction between the wind turbines (WTs) and pulse-Doppler radars (PDRs), the effects of WTs on the PDRs’ detection and tracking processes are investigated in this study. The extensive examination in this study provides insightful perspectives on the tracking performance of PDRs that are employing global nearest neighbor or probabilistic data association methods for the multiple target tracking operation under WF scatterings by exhibiting statistical metrics related to the track losses, false tracks depending on several system parameters, and WF configurations. Additionally, a novel WF design approach that is focused on minimizing WF effects on radar tracking performance is proposed in this study. The signal and tracking model employed in the examination provides a baseline for the analysis for WF-radar interactions, and the proposed WF design approach is promising to minimize the deteriorating effects of WFs on radar tracking performance.

Improved Joint Probabilistic Data Association Method based on Interacting Multiple Model

Multitarget tracking with highly maneuver and crossing track under dense-clutter environment is an emphasis in target tracking field. To effectively differentiate the measurement of cluster and target, and establish the relation between target and measurement, the data relation technologies are needed to keep the consistency of path tracking. When maneuvers happen to the target, a suitable motion model should be selected for self-adaptive tracking. With consideration of these two points, we combine JPDA algorithm with IMM algorithm according to some certain way, to track multiple maneuvering targets in clutter environment. Then an improved method named OEA-JPDA (Once Echo Association) is proposed. OEA-JPDA selects the model corresponding to the biggest determinant of the covariance matrix as the target moving model of current time. Simultaneously, a related cluster matrix is created. When the amount of echoes and models is large, all the filters are performed echo associated once with added prediction consolidation and probability update. The simulation results show that this method has higher tracking accuracy for multiple maneuvering targets which are changed with time, under the dense-clutter scenario. Even if strong maneuver occurs, there is no need for bigger adjustment to the mean square error for target destinations. The improved scheme can achieve effective tracking in cluster environment with lower computation

Passive target tracking with intermittent measurement based on random finite set

In the tracking problem for the maritime radiation source by a passive sensor, there are three main difficulties, i.e., the poor observability of the radiation source, the detection uncertainty (false and missed detections) and the uncertainty of the target appearing/disappearing in the field of view. These difficulties can make the establishment or maintenance of the radiation source target track invalid. By incorporating the elevation information of the passive sensor into the automatic bearings-only tracking (BOT) and consolidating these uncertainties under the framework of random finite set (RFS), a novel approach for tracking maritime radiation source target with intermittent measurement was proposed. Under the RFS framework, the target state was represented as a set that can take on either an empty set or a singleton; meanwhile, the measurement uncertainty was modeled as a Bernoulli random finite set. Moreover, the elevation information of the sensor platform was introduced to ensure observability of passive measurements and obtain the unique target localization. Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source and demonstrate the superiority of the proposed approach in comparison with the traditional integrated probabilistic data association (IPDA) method. The tracking performance under different conditions, particularly involving different existence probabilities and different appearance durations of the target, indicates that the method to solve our problem is robust and effective.

Effective Association of SAR and AIS Data Using Non-Rigid Point Pattern Matching

Ship surveillance using multiple remote sensing sensors becomes more and more vital presently. Among the various sensors, space-borne Synthetic Aperture Radar (SAR) is optimal for its high resolution over wide swaths and all-weather working capabilities. Meanwhile, Automatic Identification System (AIS) is efficient to provide ship navigational information. Limited to the progress of ship surveillance using SAR image only, the integration of them significantly benefits more. Data association is the fundamental issue. Many algorithms have been developed including the Nearest-Neighbour (NN) algorithm, the Joint Probabilistic Data Association (JPDA) method, and the Multiple Hypothesis Testing (MHT) approach. Ship positions derived from SAR image can be associated with the ones provided by AIS. State-of-the-art method (NN algorithm) is proved to be feasible. But it faces more challenges under adverse circumstances, such as high-density-shipping condition. We investigate the non-rigid Point Pattern Matching (PPM) method to solve this problem. To the best of our knowledge, this paper is the first to introduce non-rigid PPM to the data association of SAR and AIS. On the basis of introduction to the data association, Coherent Point Drift (CPD) algorithm is investigated. Experiments are carried out and the results illustrate that the CPD algorithm achieves higher accuracy and outperforms state-of-the-art method, especially under high-density-shipping condition.

Bernoulli particle filter with observer altitude for maritime radiation source tracking in the presence of measurement uncertainty

For maritime radiation source target tracking in particular electronic counter measures (ECM) environment, there exists two main problems which can deteriorate the tracking performance of traditional approaches. The first problem is the poor observability of the radiation source. The second one is the measurement uncertainty which includes the uncertainty of the target appearing/disappearing and the detection uncertainty (false and missed detections). A novel approach is proposed in this paper for tracking maritime radiation source in the presence of measurement uncertainty. To solve the poor observability of maritime radiation source target, using the radiation source motion restriction, the observer altitude information is incorporated into the bearings-only tracking (BOT) method to obtain the unique target localization. Then the two uncertainties in the ECM environment are modeled by the random finite set (RFS) theory and the Bernoulli filtering method with the observer altitude is adopted to solve the tracking problem of maritime radiation source in such context. Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source, and also demonstrate the superiority of the method compared with the traditional integrated probabilistic data association (IPDA) method. The tracking performance under different conditions, particularly those involving different duration of radiation source opening and switching-off, indicates that the method to solve our problem is robust and effective.