Probabilistic Data Association Research Articles

Detection and Tracking of Road Barrier Based on Radar and Vision Sensor Fusion

The detection and tracking algorithms of road barrier including tunnel and guardrail are proposed to enhance performance and reliability for driver assistance systems. Although the road barrier is one of the key features to determine a safe drivable area, it may be recognized incorrectly due to performance degradation of commercial sensors such as radar and monocular camera. Two frequent cases among many challenging problems are considered with the commercial sensors. The first case is that few tracks of radar to road barrier are detected due to material type of road barrier. The second one is inaccuracy of relative lateral position by radar, thus resulting in large variance of distance between a vehicle and road barrier. To overcome the problems, the detection and estimation algorithms of tracks corresponding to road barrier are proposed. Then, the tracking algorithm based on a probabilistic data association filter (PDAF) is used to reduce variation of lateral distance between vehicle and road barrier. Finally, the proposed algorithms are validated via field test data and their performance is compared with that of road barrier measured by lidar.

Global sensor selection for maneuvering target tracking in clutter

This paper studies the problem of sensor selection for maneuvering target tracking in the cluttered environment. By modeling the target dynamics as jump Markov linear systems, a decentralized tracking algorithm is developed by applying the extended Kalman filter and the probabilistic data association technique. A cost function that minimizes the expected filtered mean square position error is utilized and a sensor selection scheme is proposed. A numerical example is provided to illustrate the effectiveness of the proposed approach.

Tracking Crossing Targets in Passive Sonars Using NNJPDA

This paper presents an effective solution to the problems of multi-target tracking in passive sonar. Since bearing information alone is used, target crossing problems arises in passive sonar. Nearest Neighbor Joint Probabilistic Data Association (NNJPDA) is employed for the information processing. Further a prediction mechanism is used, where the track bearing depends only on the predicted value not on the measurements. This leads to the correct assignment of measurements with the track in the clutter and thereby avoids the track loss. The state estimation is then refined by Kalman filtering based on the corrected measurements from the NNJPDA technique. Thus accurate and continuous track is maintained.

IPDA filters in the sense of Gaussian mixture PHD algorithm

The Integrated Probabilistic Data Association (IPDA) type filters provide estimates of the underlying target probability of existence as well as they track state maintenance. For each scan, IPDA recursive calculates the probability of target existence in order to resolve the uncertainty. Likewise, Random Finite Set (RFS) is a method for single target and multi-target tracking. It provides a Bayesian recursion of multi-target distribution through the Finite Set calculus. Practical implementation of multi-target posterior recursion is too difficult. It was analytically proved that IPDA algorithm can be derived from the RFS based filter recursion under the linear Gaussian assumptions. Probability hypothesis density (PHD) filter is an alternative to this problem where only the first order moment of the complete multi-target posterior is propagated in time. In this article, IPDA and Gausian Mixtures PHD (GM PHD) filters in a single target tracking scenario are derived and compared. Simulations have demonstrated the superiority of IPDA filters in heavy clutters.

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.

Target tracking with a two‐scan data association algorithm extended for the hybrid target state

This study develops a two-scan data association algorithm for the hybrid target state, which consists of the target trajectory state and the target existence state. The proposed method uses the sliding window-based approach for the multi-scan data association. It updates the target existence state by considering all the feasible multi-scan target existence events in the sliding window. The proposed method also updates the target trajectory state by calculating the joint association events in the sliding window. Performance of the proposed algorithm is compared with that of the integrated track splitting algorithm and the integrated probabilistic data association algorithm for various conditions.

Doppler-aided localization of mobile nodes in an underwater distributed antenna system

In this paper, we consider a problem of localizing a moving object in the context of an underwater distributed antenna system. All the distributed nodes record the time-of-arrivals of a single message from the moving object and obtain the Doppler speed estimates used in the decoding process. Conventional methods have relied only on the time-of-arrival measurements to obtain position estimates, on top of which filtering methods can be further applied for tracking purposes. In this work, we explore the usefulness of Doppler speed information. The combination of time-of-arrival measurements and Doppler speed estimates associated with a single message enhances the accuracy of position estimation and provides an estimate of the velocity of the mobile object. A Kalman Filter (KF) and a Probabilistic Data Association Filter (PDAF) are further implemented processing the point estimates. Simulations show that the proposed Doppler-aided methods improve both the point estimation and tracking filter performance, which is verified using data from pool tests.

Optimal coordination method of opportunistic array radars for multi‐target‐tracking‐based radio frequency stealth in clutter

AbstractOpportunistic array radar is a new radar system that can improve the modern radar performance effectively. In order to improve its radio frequency stealth ability, a novel coordination method of opportunistic array radars in the network for target tracking in clutter is presented. First, the database of radar cross section for targets is built, then the signal‐to‐noise ratio for netted radars is computed according to the radar cross section and range of target. Then the joint probabilistic data association algorithm of tracking is improved with consideration of emitted power of the opportunistic array radar, which has a main impact on detection probability for tracking in clutter. Finally, with the help of grey relational grade and covariance control, the opportunistic array radar with the minimum radiated power will be selected for better radio frequency stealth performance. Simulation results show that the proposed algorithm not only has excellent tracking accuracy in clutter but also saves much more radiated power comparing with other methods.

Vote selection mechanisms and probabilistic data association-based mobile node localization algorithm in mixed LOS/NLOS environments

As one of the key technologies of wireless sensor networks (WSNs), the localization of mobile nodes (MN) is one of the most significant research topics in WSNs. When a line-of-sight (LOS) channel is available, accuracy localization result can be obtained. Motivated by the fact that the non-line-of-sight (NLOS) propagation of signal is ubiquitous and decreases the accuracy of localization, we propose a MN localization algorithm in mixed LOS/NLOS environments. Considering the characteristics of NLOS error, we propose a localization algorithm based on vote selection mechanisms to filter the distance measurements and reserve the reliable measurements. Then a modified probabilistic data association algorithm is proposed to fuse the multiple measurements reserved from the vote selection. The position of the MN is finally determined by a linear least squares algorithm based on reference nodes selection. This algorithm effectively mitigates various kinds of NLOS errors and largely improves the localization accuracy of the MN in mixed LOS/NLOS environments. The simulation and experiments results show that the proposed algorithm has better robustness and higher localization accuracy than other methods.

Multi‐target tracking in clutter using a high pulse repetition frequency radar

In this study, two algorithms of single-target tracking in clutter using a high pulse repetition frequency radar are extended: the Gaussian mixture measurement likelihood-integrated track splitting (GMM-ITS) algorithm and the enhanced multiple models (MM) to multi-target tracking algorithm, that is, the GMM-joint ITS algorithm and the enhanced MM-joint probabilistic data association algorithm, respectively. Both algorithms are extended on the basis of the optimal Bayes approach that creates track clusters for determining the nearby tracks that share measurements by enumerating and evaluating all the feasible joint measurement allocations. In all cases, the track trajectory probability density function is a Gaussian mixture, and both algorithms enable false track discrimination using the probability of target existence.

基于聚类的多目标自适应互联跟踪算法

In data fusion systems, multitarget data association is an important and difficult field of research. Currently, joint probabilistic data association (JPDA) is a common approach to solve this problem; however, the confirmed matrix is generated according to all measurements available at each time step. Therefore, in real situations involving dense clutter and multiple targets, it will result in increased computational load and unacceptably poor performance. To address this problem, an adaptive algorithm for multitarget tracking (adaptive-JPDA) is presented based on clustering. First, a clustering analysis step is applied to divide the measurements into different clusters. Second, the association approaches are selected adaptively according to the measurement parameter; in this way, each measurement can be associated with the correct track or clutter. In order to track multiple targets, the joint association matrix is constructed according to the membership matrix and the relationship matrix, which results in joint association probability. Third, a theoretical analysis is presented to compare the computational complexity. The main idea is that without the pre-tracking or matrix splitting process at each time step, all measurements are handled via a clustering step before data association. Thus, the extent of data association can be reduced dramatically, which reduces the computational complexity. Without disturbance from independent measurements outside the clustering, this approach can yield significant improvements in tracking performance. Finally, simulation results demonstrate the effectiveness of this approach.

Automatic Sea-Surface Obstacle Detection and Tracking in Forward-Looking Sonar Image Sequences

Automatic sea-surface object detection and tracking for safe autonomous underwater vehicle and submarine surfacing is a critical issue in relation to the accidents reported in the last decades. Here, we propose an efficient tool to detect and track sea-surface obstacles by processing forward-looking sonar images. The proposed method can detect either still or moving objects with and without wake. For each image sequence, a sequential procedure is proposed to detect various obstacle signatures. Then, target positions and velocities are estimated in Cartesian coordinates using the debiased converted measurement Kalman filter and the joint probabilistic data association filter. Detection and tracking stages exchange information in order to reduce the number of false alarms. Promising results are obtained using real data collected at sea with various objects and scenarios.

Segmentation of the left ventricle in cardiac MRI using a probabilistic data association active shape model.

The 3D segmentation of endocardium of the left ventricle (LV) in cardiac MRI volumes is a challenging problem due to the intrinsic properties of this image modality. Typically, the object shape and position are estimated to fit the observed features collected from the images. The difficulty inherent to the LV segmentation in MRI is that the images contain outliers (i.e., observations not belonging to the LV border) due to the presence of other structures. This paper proposes a robust approach based on the Active Shape Model (ASM) that is able to circumvent the above problem. More specifically, the ASM will be guided by probabilistic data association filtering (PDAF) of strokes (i.e. line segments) computed in the neighborhood of the shape model. Thus, the proposed approach, termed herein as ASM-PDAF, will perform the following main steps: 1) edge detection (low-level features) in the vicinity of the shape model; 2) edge grouping (mid-level features) to obtain potential LV strokes; and 3) filtering using a PDAF framework (high-level features) to update the ASM. Experimental results on a public cardiac MRI database show that the proposed approach outperforms previous literature research.

Heterogeneous Multiple Sensors Joint Tracking of Maneuvering Target in Clutter.

To solve the problem of tracking maneuvering airborne targets in the presence of clutter, an improved interacting multiple model probability data association algorithm (IMMPDA-MDCM) using radar/IR sensors fusion is proposed. Under the architecture of the proposed algorithm, the radar/IR centralized fusion tracking scheme of IMMPDA-MDCM is designed to guarantee the observability of the target state. The interacting multiple model (IMM) deals with the model switching. The modified debiased converted measurement (MDCM) filter accounts for non-linearity in the dynamic system models, and reduces the effect of measurement noise on the covariance effectively. The probability data association (PDA) handles data association and measurement uncertainties in clutter. The simulation results show that the proposed algorithm can improve the tracking precision for maneuvering target in clutters, and has higher tracking precision than the traditional IMMPDA based on EKF and IMMPDA based on DCM algorithm.

Knowledge-Based Multitarget Ship Tracking for HF Surface Wave Radar Systems

These last decades spawned a great interest toward low-power high-frequency (HF) surface-wave (SW) radars for ocean remote sensing. By virtue of their over-the-horizon coverage capability and continuous-time mode of operation, these sensors are also effective long-range early warning tools in maritime situational awareness applications providing an additional source of information for target detection and tracking. Unfortunately, they also exhibit many shortcomings that need to be taken into account, and proper algorithms need to be exploited to overcome their limitations. In this paper, we develop a knowledge-based (KB) multitarget tracking methodology that takes advantage of a priori information on the ship traffic. This a priori information is given by the ship sea lanes and by their related motion models, which together constitute the basic building blocks of a variable structure interactive multiple model procedure. False alarms and missed detections are dealt with using a joint probabilistic data association rule and nonlinearities are handled by means of the unscented Kalman filter. The KB-tracking procedure is validated using real data acquired during an HF-radar experiment in the Ligurian Sea (Mediterranean Sea). Two HFSW radar systems were operated to develop and test target detection and tracking algorithms. The overall performance is defined in terms of time-on-target, false-alarm rate (FAR), track fragmentation (TF), and accuracy. A full statistical characterization is provided using one month of data. A significant improvement of the KB-tracking procedure, in terms of system performance, is demonstrated in comparison with a standard joint probabilistic data association tracker recently proposed in the literature to track HFSW radar data. The main improvement of our approach is the better capability of following targets without increasing the FAR. This increment is much more evident in the region of low FAR, where it can be over the 30% for both the HFSW radar systems. The KB-tracking exhibits on average a reduction of the TF of about the 20% and the 13% of the utilized HFSW-radar systems.

Continuous 2-D assignment for multitarget tracking with rotating radars

Mechanically steered scanning radars receive measurements continuously while sweeping the surveillance region. However, most target tracking algorithms, like the multiple hypothesis tracker (MHT) and the joint probabilistic data association (JPDA) techniques, wait for the end of a scan in order to process the measurements and to estimate targets states. This is due to the fundamental assumption of one-to-one association between tracks and measurements and the 360° physical limit of a scan. Associating measurements to initialized tracks and filtering at the end of a complete scan may cause significant delays in target state update. In addition, association may become imperfect due to longer intervals between updates. This issue becomes significant when tracking high-speed targets with low scan rate sensors as in the airborne early warning (AEW) system. In this paper, we present a new dynamic sector processing algorithm using two-dimensional (2-D) assignment for scanning radars that report measurements within the duration of a scan. The full scan is dynamically and adaptively divided into sectors, which could be as small as a single detection, depending on the scanning rate, sparsity of targets, and required target state update speed. Measurement-to-track association, filtering, and target state update are done dynamically while sweeping from one region to another, i.e., continuous track update, limited only by the intermeasurement interval, becomes possible. The proposed algorithm offers low latency while maintaining estimation accuracy in track updates as well as efficient utilization of computational resources compared with standard frame-based tracking algorithms. Experimental results based on rotating radars demonstrate the advantages of the proposed technique.

Vision-Aided Inertial Navigation with Modeling of Measurement Outliers

For vision-based navigation applications, the assumption of a Gaussian distribution for measurement errors may not be valid due to outliers commonly resulting from complicated algorithmic processing of images (for example, feature extraction, feature matching, and frame-to-frame tracking). Although many algorithms have been developed to minimize the probability of outputs that are outliers, the probability is still nonzero and is not estimated with current approaches. This paper develops a vision-aided inertial navigation mechanization that explicitly accounts for the presence of outliers in vision measurements. Navigation mechanization is augmented by probabilistic data association filtering. Probabilistic data association filtering takes into account nonperfect detection of outliers by adaptively computing the probability that an outlier is undetected and weighting vision measurements accordingly. Simulation and experimental results are used to demonstrate that the probabilistic data association filtering estimator efficiently mitigates outliers and enables consistent localization performance under the presence of real-world image processing errors.

Trajectory optimisation method by using independent component analysis for MIMO‐OTHR target tracking

Traditional sky-wave over-the-horizon radar (OTHR) target tracking algorithms, such as multipath probabilistic data association, can achieve good performance if the multipath environment clutter is not strong enough. The computation complexity and tracking performance of these algorithms can be further improved by multiple-input multiple-output (MIMO) radar technology owing to its capability to utilise transmitting and receiving diversities. However, multipath propagation is still considered to degrade the performance because of the strong ionosphere and sea clutter. To deal with the interference of strong clutter in a multipath environment, a tracking trajectory optimisation method is proposed by applying the fast and robust fixed-point independent component analysis algorithm which uses the projection pursuit approach to separate clutter and a true target moving trajectory from the received signals. Numerical simulations demonstrate the feasibility of the method, and it also shows that the multipath measurements can be utilised for MIMO-OTHR target tracking.

Launch point prediction employing the smoothing IPDA algorithm in 3-D cluttered environments

The counter-battery radar system tracks the artillery projectile trajectory and finds the launch point (LP) to take swift protective actions against enemies’ succeeding shelling. The LP estimation highly depends on the accuracy of target state estimate. However, measurements from the surveillance radar systems include clutter measurements as well as target measurements, so the data association is needed for target tracking in the cluttered environments. In this paper, the smoothing integrated probabilistic data association (sIPDA) algorithm is applied for estimation of artillery projectile trajectory in the cluttered environments and backward prediction with the constant axial force (CAF) model is carried out for launch point prediction (LPP). The proposed method significantly improves the accuracy of LPP compared with the integrated probabilistic data association (IPDA) algorithm.

Multiple target tracking based on particle filtering for safety in industrial robotic cells

Human–robot collaboration is a key issue for the development of factories of the future, a space where humans and robots can work and carry out tasks together. Within this collaborative human–robot interaction paradigm, safety is one of most critical subjects. The presented paper describes a security framework for industrial robotic cells based on laser rangefinders able to detect and track obstacles around the robot, information that is used to modify robot’s behaviour and guarantee safety. The system includes a Joint Probability Data Association Particle Filter to enhance the tracking process. The obtained results, implemented and tested in a real industrial robotic cell, show the suitability of the approach.