Data Association Problem Research Articles

Posterior Cramér–Rao lower bounds for extended target tracking with PMBM conjugate recursion

AbstractThis letter considers the posterior Cramér–Rao lower bounds (PCRLB) problem for extended target tracking from a stack of measurement data that are modelled as random variables in the random finite sets framework. The scalars in the traditional PCRLB are converted into vectors based on random finite sets to derive a theoretical lower bound. In this way, the proposed method can be applied to the multi‐target tracking problem and accommodates scenarios with targets of varying. Moreover, solving the data association problem from four parts caused by the conjugate update of the Poisson multi‐Bernoulli mixture filter is considered. Simulation results are presented to verify the effectiveness of the derived PCRLB.

Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm.

Simultaneous localization and mapping (SLAM) is a hot research area that is widely required in many robotics applications. In SLAM technology, it is essential to explore an accurate and efficient map model to represent the environment and develop the corresponding data association methods needed to achieve reliable matching from measurements to maps. These two key elements impact the working stability of the SLAM system, especially in complex scenarios. However, previous literature has not fully addressed the problems of efficient mapping and accurate data association. In this article, we propose a novel hash multi-scale (H-MS) map to ensure query efficiency with accurate modeling. In the proposed map, the inserted map point will simultaneously participate in modeling voxels of different scales in a voxel group, enabling the map to represent objects of different scales in the environment effectively. Meanwhile, the root node of the voxel group is saved to a hash table for efficient access. Secondly, considering the one-to-many (1 ×103 order of magnitude) high computational data association problem caused by maintaining multi-scale voxel landmarks simultaneously in the H-MS map, we further propose a bidirectional matching algorithm (MSBM). This algorithm utilizes forward-reverse-forward projection to balance the efficiency and accuracy problem. The proposed H-MS map and MSBM algorithm are integrated into a completed LiDAR SLAM (HMS-SLAM) system. Finally, we validated the proposed map model, matching algorithm, and integrated system on the public KITTI dataset. The experimental results show that, compared with the ikd tree map, the H-MS map model has higher insertion and deletion efficiency, both having O(1) time complexity. The computational efficiency and accuracy of the MSBM algorithm are better than that of the small-scale priority matching algorithm, and the computing speed of the MSBM achieves 49 ms/time under a single CPU thread. In addition, the HMS-SLAM system built in this article has also reached excellent performance in terms of mapping accuracy and memory usage.

Adaptive Multi-Hypothesis Marginal Bayes Filter for Tracking Multiple Targets

Tracking multiple targets in the presence of unknown number of targets, missed detection, clutter, and noise is a challenging problem. To cope with this problem, a novel approach for generating the potential birth targets was developed, a mathematical model for multiple hypotheses was established, and an adaptive multi-hypothesis marginal Bayes filter is herein proposed in terms of the established mathematical model for multiple hypotheses and the novel birth approach. This filter delivers the existence probabilities of targets and their probability density functions. It uses multiple hypotheses to solve the data association problem to form the existence probabilities of targets and their probability density functions. To obviate the requirement for prior birth models, this filter uses the observations from two consecutive time steps to establish the birth models adaptively. Its tracking performance was tested by comparing it with other adaptive filters, showing that the proposed filter is robust, and it can obtain higher tracking accuracy than other filters.

A group target track‐before‐detect approach using two‐stage strategy with maximum‐likelihood probabilistic data association

AbstractIn tracking scenarios involving groups with dense targets, achieving effective data association is challenging due to mutual occlusion and interference among targets. The complexity of the tracking problem is further exacerbated in low‐observable environments by the increase in false alarm rates. The track‐before‐detect (TBD) is an advanced technology for detecting and tracking low‐observable targets, effectively mitigating data association problems by integrating multi‐frame echo data. However, the existing multi‐target TBD algorithms typically assume that the targets are spatially separated and are not suitable for scenarios involving group targets. A group target maximum‐likelihood probabilistic data association (GT‐ML‐PDA) algorithm, based on the concept of TBD, is proposed to track group targets effectively in low‐observable environments. The proposed algorithm divides group target tracking into two stages: group centre trajectory estimation and individual target trajectory estimation. To enhance the performance of the proposed algorithm, two strategies are suggested: modifying the equivalent measurements and extracting independent measurement sets for individual targets. Simulation results demonstrate that the proposed algorithm is capable of effectively tracking numerous individual targets within a group, even in the presence of heavy clutter.

SCGTracker: Spatio-temporal correlation and graph neural networks for multiple object tracking

Multiple Object Tracking is an important vitally important fundamental task in computer vision. Visual tracking becomes challenging when objects move in groups and are obscured from each other. There are two mainstream solution strategies for these group models. One is to transform the data association problem into a graph matching problem for solving, while the other is to apply the social power model as an advanced constraint for group tracking. In the former case, the solving difficulty geometric growth as the number of tracked objects increases, and the computing efficiency for real-time tracking demand cannot be met. The latter strategy tends to set up fixed-size groups or offline training rules, resulting in a lack of flexibility that limits their scenario generalization. According to the shortcomings of existing methods, this paper proposes a novel multiple object tracking method with spatio-temporal correlation and graph neural networks. Firstly, the relational features of the historical trajectories are extracted through the spatio-temporal relationship learning module, which models the spatio-temporal correlations of the objects and dynamically constructs the group structure online. Then, the graph neural network is combined with appearance and motion information, and the similarity between each detection and tracklet is used as a weight in node feature aggregation to make powerful distinctions between node features. Meanwhile, the spatio-temporal correlation method is also used to solve target loss issues caused by occlusion. Even collocated with linearly assigned data association method, good tracking results are still achieved, with a low computational complexity. Experiments on three challenging public datasets, namely MOT16, MOT17, and MOT20, validated the accuracy and efficiency of the proposed tracking method.

Mixed Correlation Coefficient Between Probability Hesitation Fuzzy Sets and Applications

Probability hesitation fuzzy sets(PHFSs) have been paid increasing attention in the fuzzy domain nowadays, and correlation coefficient is becoming an important research content in fuzzy data analysis, many scholars have been conducted relevant researches on it. However, the existing correlation coefficients between PHFSs have some limitations, such as not considering the number of membership degrees and having counter-intuitive phenomenon to some extent. Therefore, we propose the mixed correlation coefficient between PHFSs. To begin with, we give the concepts of the average, variance, and length ratio of PHFE to illustrate the integrity, distribution, and length. Then, we define the average, variance, and length three correlation coefficients respectively on the above basic concepts. Furthermore, we construct the mixed correlation coefficient through combining these basic correlation coefficients, and extend to weighted form in addition, which addresses the issues of the available correlation coefficients. Finally, we utilize proposed correlation coefficient to analysis problems of data association and decision making. The effectiveness and rationality of the proposed method is illustrated at length.

Data-Driven Clustering and Bernoulli Merging for the Poisson Multi-Bernoulli Mixture Filter

This paper proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets. We define a measurement-driven clustering algorithm to reduce the data association problem into several subproblems, and we provide the derivation of the resulting clustered PMBM posterior density via Kullback-Leibler divergence minimisation. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and inter-track swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than one thousand targets.

IDT: An Integration of Detection and Tracking Toward Low-Observable Multipedestrian for Urban Autonomous Driving

Robust pedestrian trajectory-tracking is an essential prerequisite to traffic accident prevention. However, it is a challenging task in urban autonomous driving, since the weak backscattered signals from pedestrians with small radar cross-section may be submerged in strong background clutters, especially under adverse weather conditions. On this account, this article presents an integration of detection and tracking (iDT) toward multipedestrian with a low signal-to-noise ratio (SNR). In particular, in contrast to conventional methods, in which the detection and tracking are treated as two separate processes, we address them jointly to ensure the accuracy of continuous detection and tracking in low SNR conditions. Another distinguishing element is that to accommodate the time-varying number of targets, the Bayesian framework is tailored by augmenting the state vector with a multipedestrian evolutional indicator. The advantage is that all targets can be tracked simultaneously by searching the global likelihood ratio of a spectrum once, rather than assigning an individual tracker to each target in conventional methods. Furthermore, through the proposed integrated framework, the data association problem is circumvented because there is no explicit measurement-target assignment process in our approach. In addition, a commercial automotive multiple-input-multiple-output millimeter-wave radar sensor is employed to validate the proposed method. Consequently, numerous simulation and experiment results turn out that iDT shows unique advantages in low-observable multipedestrian tracking compared with traditional methods.

Probabilistic Semantic Data Association for Collaborative Human-Robot Sensing

Humans cannot always be treated as oracles for collaborative sensing. Robots thus need to maintain beliefs over unknown world states when receiving semantic data from humans, as well as account for possible discrepancies between human-provided data and these beliefs. To this end, this paper introduces the problem of semantic data association (SDA) in relation to conventional data association problems for sensor fusion. It then develops a novel probabilistic semantic data association (PSDA) algorithm to rigorously address SDA in general settings, unlike previous work on semantic data fusion which developed heuristic techniques for specific settings. PSDA is further incorporated into a recursive hybrid Bayesian data fusion scheme which uses Gaussian mixture priors for object states and softmax functions for semantic human sensor data likelihoods. Simulations of a multi-object search task show that PSDA enables robust collaborative state estimation under a wide range of conditions where semantic human sensor data can be erroneous or contain significant reference ambiguities.

A data association algorithm for the robust confidence ellipsoid filter

In this paper, we propose an algorithm to deal with the data association problem of the robust confidence ellipsoid filter. First, at every time instant, for each target, we need to get a validation gate based on the confidence ellipsoid of the previous time instant. Herein, the validation gate is the minimum volume ellipsoid which guarantees that the true measurement of the target at the current time instant must be contained within it. To obtain the validation gate, an optimization problem is derived from the uncertain dynamic system and it is further converted into a semidefinite programming problem. Then, we make use of the measurements in the validation gate (candidate measurements) and the confidence ellipsoid of the previous time instant to gain the confidence ellipsoid of the current time instant. Similar to the process of obtaining the validation gate, the confidence ellipsoid is obtained by solving a semidefinite programming problem. The ellipsoid has the minimum volume and simultaneously, ensures that the true state vector is contained in it. Next, we propose a strategy to deal with the case where there is no measurement in the validation gate (missing measurement). Last, the numerical examples show the verifications of the proposed algorithm.

Multiple target data-association algorithm based on Takagi–Sugeno intuitionistic fuzzy model

To solve the problem of data association in multiple target tracking in a densely cluttered environment, a multiple target data association algorithm based on Takagi–Sugeno (T-S) intuitionistic fuzzy model is proposed. In the proposed algorithm, the new multiple target T-S intuitionistic fuzzy model (MTTS-IFM) is constructed by incorporating intuitionistic fuzzy sets and intuitionistic fuzzy numbers. To identify the premise parameters for MTTS-IFM, a new intuitionistic index is defined and a modified form of the intuitionistic fuzzy C-means clustering algorithm is proposed. Furthermore, to solve the multiple target data association for the MTTS-IFM approach, the input variables of MTTS-IFM are defined using the maximum intuitionistic fuzzy entropy, and a data association approach is proposed to deal with the uncertainty between targets and measurements. Finally, simulation results show that the performance of the proposed algorithm is better than that of algorithms designed for multiple target tracking in either a clutter-free or cluttered environment. Moreover, the real-time performance was clearly better than that of the joint probabilistic data association filter (JPDAF) and slightly better than that of maximum entropy fuzzy (MEF-JPDAF), Fitzgerald-JPDAF, and a T-S intuitionistic fuzzy model (TS-FM).

Disambiguation of measurements for multiple acoustic source localization using deep multi-dimensional assignments

The location estimation of multiple simultaneously active sources in an acoustic sensor network is quite challenging because the correct combination of measurements received from different microphone arrays is not usually known, generally termed the data association problem. Existing techniques generally formulate the data association problem as a multidimensional assignment and solve them using classical optimal techniques. Although these approaches show effectiveness for a limited amount of active sources, the computational time and complexity of these techniques increase significantly when the microphones or acoustic sources increase. In this paper, a learning approach is proposed that solves the multidimensional assignment using a deep neural network. Initially, the features that are employed for the association are formulated for each of the detected sources in all the array nodes, and a multidimensional assignment problem is formulated. Subsequently, a deep multidimensional assignment network is devised to extract the correspondence probability of the measurements received from the microphone arrays. Specifically, a data-driven differentiable approach is presented for multidimensional assignments that is computationally efficient. The proposed methodology is validated under realistic conditions for both speech and urban signals. The methodology is compared to state-of-the-art methods for showing the performance gain in terms of accuracy of association and location estimation with a reduced computational time.

SemanticLoop: Loop Closure With 3D Semantic Graph Matching

Loop closure can effectively correct the accumulated error in robot localization, which plays a critical role in the long-term navigation of the robot. Traditional appearance-based methods rely on local features and are prone to failure in ambiguous environments. On the other hand, object recognition can infer objects' category, pose, and extent. These objects can serve as stable semantic landmarks for viewpoint-independent and non-ambiguous loop closure. However, there is a critical object-level data association problem due to the lack of efficient and robust algorithms. We introduce a novel object-level data association algorithm, which incorporates IoU, instance-level embedding, and detection uncertainty, formulated as a linear assignment problem. Then, we model the objects as TSDF volumes and represent the environment as a 3D graph with semantics and topology. Next, we propose a graph matching-based loop detection based on the reconstructed 3D semantic graphs and correct the accumulated error by aligning the matched objects. Finally, we refine the object poses and camera trajectory in an object-level pose graph optimization. Experimental results show that the proposed object-level data association method significantly outperforms the commonly used nearest neighbor method in accuracy. Our graph matching-based loop closure is more robust to environmental appearance changes than existing appearance-based methods.

A Multiple Target Data Association Method for TDOA Passive Localization

In order to solve the multiple target data association problem in underwater acoustic passive localization system, a new algorithm focus on data association in Time Difference of Arriva l(TDOA) is studied. Firstly, the zero-sum condition is used to select TDOA measurements according to the continuous measurement values within a certain period of time. Then, the selected TDOA measurements are associated with the multiple targets and the correct correlation probability is measured by the correlation degree. The algorithm is used in sea trial. Processing results show that the method proposed in this paper can effectively solve the TDOA measurement association problem, and can locate multiple targets at the same time.

Research on construction data association management of Urban Rail Transit Based on BIM

With the continuous expansion of the scale of urban rail transit construction projects, it is difficult to improve the coordination in construction and the relevance of construction data. In order to solve the problem of data association in construction, an urban rail transit construction data association management system oriented to BIM Technology is proposed. BIM is used to build a dynamic workflow collaboration architecture to manage the data in urban rail transit construction, so as to realize construction collaborative scheduling. In the test and analysis of the system, the comprehensive ability test of the system shows that the minimum response time of the system can be reduced to 1.2 ms. At the same time, the system has ideal visualization effect and obvious practical value in practical application. Therefore, the urban rail transit construction data association management system based on BIM can show ideal results in complex construction environment, which is of great significance to China's urban construction.

Object Clustering With Dirichlet Process Mixture Model for Data Association in Monocular SLAM

Semantic simultaneous localization and mapping (SLAM) with a monocular camera is particularly attractive because of the deployment simplicity and economic availability. Data association problem which assigns unique identities for objects shown in multiple frames plays a fundamental role in semantic SLAM. Previous prevalent methods which mainly focused on associating geometric KeyPoints are no longer suitable. Some naive methods that rely on object distance or 2-D/3-D Intersection over Union are also vulnerable when occlusions happen. In this article, we propose a novel data association method for cuboid landmarks based on Dirichlet process mixture model. By jointly considering object class, position, and size, our method can perform data association robustly. We evaluated our method in simulated datasets, public benchmark KITTI, and on a real robot in an office environment. Experimental results show that our method not only associates cuboids robustly but also achieves SOTA pose estimation accuracy in monocular SLAMs.

Deep Attention Aware Feature Learning for Data Association in Multiple Source Localization

The letter addresses the problem of data association for multiple source localization in a distributed sensor network. To increase the efficiency of localization, we present a learning methodology to generate highly distinctive measurement features for data association. We propose an attention-based graph neural network that exploits the relationship among the phases from the Short Time Fourier transform and the estimated measurements to generate highly descriptive features for each of the detected source signals. The methodology is free from any selective threshold and processes each of the measurements to detect and enrich the similar and distinguishable aspects of the measurements. These aspects are then aggregated in feature representations for each source measurement. The generated features can then be employed with any data association method to segregate the measurements belonging to the same source. It is observed in experimental evaluation that the proposed features employed with state-of-the-art association frameworks yield lower association and localization errors.

Robust Initial Orbit Determination for Surveillance Doppler-Only Radars

An initial orbit determination algorithm for surveillance Doppler-only radars with embedded quantification capabilities is presented. The method is based on a combination of Gauss' and Lambert's solvers formulated in the differential algebra framework, which provides the Taylor expansion of the state estimate with respect to the measurement uncertainties. This feature makes the approach particularly suited for handling data association problems. The first part of the paper describes the mathematical formulation of the method, while an extensive analysis of its performance and a comparison with a reference algorithm are carried out in the second part, using both simulated and real data. The proposed approach is shown to be accurate and robust and particularly suited for short-arc observations.

A Joint Data Association Method for Laser-SLAM of Unmanned Delivery Vehicle Based on Heuristic Search Algorithm

In Laser-SLAM system of unmanned delivery vehicle, there are two kinds of association methods applied to solve the data association problem. Compared with the method of independent association for a single measurement and a single feature, the methods of batch association of measurements and features can provide more accurate association results in the state estimation stage of SLAM. In order to obtain a better association solution, a joint data association method based on heuristic search algorithm (HSA-JDA) is proposed to improve the robustness and accuracy of data association. In HSA-JDA, according to the joint maximum likelihood criterion, the data association problem is evolved into a combinatorial optimization problem of how to determine the optimal association set. A heuristic search algorithm that is an optimized artificial fish swarm algorithm by using adaptive step size and adding fish swarm jumping behavior is applied to search the optimal association solution. Experimental results show that HSA-JDA method ensures high association accuracy and then improves the robustness and accuracy of the whole state estimation results of SLAM. It can be used in the Laser-SLAM system based on Kalman filter to provide reliable association results for improving the accuracy of SLAM estimation results for unmanned delivery vehicle.

Aircraft tracking in aerial videos based on fused RetinaNet and low‐score detection classification

Aircraft tracking in aerial videos is a challenge due to the small size and appearance affinity with other targets. Recently, the tracking-by-detection trackers, such as simple online and realtime tracking (SORT), attracted the attention of many researchers due to the great evolution in deep learning-based object detectors. SORT depends on motion cues in solving the data association problem by matching high-score detection bounding boxes to tracklets. However, motion cues are insufficient to track occluded or similar aircraft efficiently. In addition, most occluded aircrafts have a low-confidence percentage. The main objective of this paper is to propose an F-SORT tracker, an enhanced SORT tracker with the following improvements. Firstly, fused RetinaNet for small aerial targets is used as a detection framework to boost the detectability of SORT. Then, aircraft appearance features are extracted from the shallowest prediction layer and utilized as additional cues to improve the data association algorithm. Finally, the extracted features distinguish aircraft online from backgrounds via a K-nearest neighbour classifier. Experiments on an airport dataset show that F-SORT can significantly improve aircraft tracking. F-SORT outperforms other state-of-the-art trackers, achieving 72.75%, 59.63%, and 82.89% on MOTA, HOTA, and IDF1. In addition, F-SORT classifying algorithm boosts other trackers when applied.