Data Association For Multi-target Tracking Research Articles

Multi-Sensor Fusion in Automated Driving: A Survey

With the significant development of practicability in deep learning and the ultra-high-speed information transmission rate of 5G communication technology will overcome the barrier of data transmission on the Internet of Vehicles, automated driving is becoming a pivotal technology affecting the future industry. Sensors are the key to the perception of the outside world in the automated driving system and whose cooperation performance directly determines the safety of automated driving vehicles. In this survey, we mainly discuss the different strategies of multi-sensor fusion in automated driving in recent years. The performance of conventional sensors and the necessity of multi-sensor fusion are analyzed, including radar, LiDAR, camera, ultrasonic, GPS, IMU, and V2X. According to the differences in the latest studies, we divide the fusion strategies into four categories and point out some shortcomings. Sensor fusion is mainly applied for multi-target tracking and environment reconstruction. We discuss the method of establishing a motion model and data association in multi-target tracking. At the end of the paper, we analyzed the deficiencies in the current studies and put forward some suggestions for further improvement in the future. Through this investigation, we hope to analyze the current situation of multi-sensor fusion in the automated driving process and provide more efficient and reliable fusion strategies.

Multi-target tracking method based on improved firefly algorithm optimized particle filter

Joint probabilistic data association-particle filter (JPDA-PF) algorithm is often used to solve the problem of data association in multi-target tracking, it has the advantages of particle filter in dealing with nonlinear and non-Gaussian systems, but at the same time, the resampling process of particle filter will lead to the problem of particle impoverishment, which will affect the tracking accuracy. In this article, a new multi-target tracking method based on particle filter optimized by improved firefly algorithm is proposed. To solve the problem of weight degradation and avoid the problem of particle impoverishment, the improved firefly algorithm replaces the traditional resampling method and optimizes the distribution of particles in particle filter. According to the simulation results, the improved algorithm has better tracking accuracy than JPDA-PF as the number of particles is equal.

Tensor Decomposition Approach to Data Association for Multitarget Tracking

Bayesian methods employed in classical observation-to-track data-association problems in dense environments suffer from exponential growth in complexity with an increase in the number of targets. This paper employs tensor decomposition, which is a technique commonly used in high-dimensional applications to address the curse of dimensionality in the context of such data-association problems. The joint probabilistic data-association (JPDA) filter is developed in the framework of incremental tensor decomposition to curtail the computational burden caused by exponential growth in the number of feasible association events. A dynamic tensor analysis is employed to reduce each scan of measurements to a so-called core tensor, effectively reducing the number of feasible association events to be considered in the JPDA filter. Object tracks are obtained by reconstructing the updated decomposed tensor measurements at the end of the association algorithm. It is shown through numerical examples that employing the reduced “core measurements” instead of the full set can lead to an order of magnitude reduction in computational time for data association. Two case studies are presented to demonstrate the reduction in computational burden afforded by the new tensor JPDA: a benchmark image-processing problem involving pedestrian tracking, and a space debris tracking problem as a part of space situational awareness.

A Study on Markov Chains Based Data Association for Multi-Target Tracking in the Cluttered Environments

A Study on Markov Chains Based Data Association for Multi-Target Tracking in the Cluttered Environments

Iterative joint integrated probabilistic data association for multitarget tracking

In situations with a significant number of targets in mutual proximity (close to each other), optimal multitarget data association approach suffers from the numerical explosion. This severely limits the applicability, i.e., the number of close targets that may be reliably tracked.We propose an iterative implementation of the joint integrated probabilistic data association (JIPDA) which allows a performance/computation resources tradeoff. This approach can also be incorporated into joint integrated track splitting (JITS). The iterations start with the single target integrated probabilistic data association (IPDA) and each subsequent iteration improves the approximation towards JIPDA, reaching the optimal multitarget solution within a finite number of iterations.

Sensor Fusion and Target Tracking Using Evidential Data Association

The problem of multitarget tracking in clutter has been shown to be very challenging for both the measurement of track association and the estimation of the targets' state. Several approaches have been suggested to solve this problem. In this paper, we present a new method for data association in multitarget tracking in the framework of evidence theory. The representation and the fusion of the information in our method are based on the use of belief function in the sense of Dempster-Shafer theory of evidence. The proposal introduces a method of computing mass assignment using a normalized Mahalanobis distance. While the decision making process is based on the extension of the frame of hypotheses, the method has been tested for a nearly constant velocity target and compared with both the nearest neighbor filter and the joint probabilistic data associations filter in highly ambiguous cases using Monte Carlo simulations. The results demonstrate the feasibility of the proposal, and show improved performance compared to the aforementioned alternative commonly used methods.

Data Association in Multi-target Tracking Using Belief Function

In this paper, we present a new method for data association in multi-target tracking. The representation and the fusion of the information in our method are based on the use of belief function. The proposal generates the basic belief mass assignment using a modified Mahalanobis distance. While the decision making process is based on the extension of the frame of hypotheses. Our method has been tested for a nearly constant velocity target and compared with both the nearest neighbor filter and the joint probabilistic data associations filter in highly ambiguous cases. The results demonstrate the feasibility of the proposal and show improved performance compared to the aforementioned alternative commonly used methods.

Cross Entropy Algorithms for Data Association in Multi-Target Tracking

Multiple-target tracking (MTT) poses difficult computational challenges related to the measurement-to-track data association problem, especially in the presence of spurious and missing measurements. Different approaches have been proposed to tackle this problem, including various approximations and heuristic optimization tools. The cross entropy (CE) method and the related parametric MinxEnt (PME) method are recent optimization heuristics that have proved useful in many combinatorial optimization problems. They are akin to evolutionary algorithms in that a population of solutions is evolved, however generation of new solutions is based on statistical methods of sampling and parameter estimation. In this work we apply the CE method and its recent MinxEnt variant to the multi-scan version of the data association problem in the presence of misdetections, false alarms, and unknown number of targets. We formulate the algorithms, explore via simulation their efficiency and performance compared with other recently proposed techniques, and show that they obtain state-of-the-art performance in challenging scenarios.

Efficient multisensor fusion using multidimensional data association

We present the development of a multisensor fusion algorithm using multidimensional data association for multitarget tracking. The work is motivated by a large scale surveillance problem, where observations from multiple asynchronous sensors with time-varying sampling intervals (electronically scanned array (ESA) radars) are used for centralized fusion. The combination of multisensor fusion with multidimensional assignment is done so as to maximize the in addition to sensor-width for the number S of lists handled by the assignment algorithm. The standard procedure, which associates measurements from the most recently arrived S-1 frames to established tracks, can have, in the case of S sensors, a time-depth of zero. A new technique, which guarantees maximum effectiveness for an S-dimensional data association (S/spl ges/3), i.e., maximum time-depth (S-1) for each sensor without sacrificing the fusion across sensors, is presented. Using a sliding window technique (of length S), the estimates are updated after each frame of measurements. The algorithm provides a systematic approach to automatic track formation, maintenance, and termination for multitarget tracking using multisensor fusion with multidimensional assignment for data association. Estimation results are presented for simulated data for a large scale air-to-ground target tracking problem.

An efficient algorithm for data association in multitarget tracking

An efficient algorithm is developed to compute the a posteriori probabilities of the origins of measurements in the joint probabilistic data association filter (JPDAF). The inherited parallelism of this algorithm enables it to be suitable for implementation in a multiprocessor system. In this algorithm the a posteriori probability of the origin of each measurement in the JPDAF is decomposed into two parts. The computation of one part becomes trivial and the algorithm developed here is implemented on the other part, which is shown to be related to permanents. The computational complexity of this algorithm is analyzed in the worst case as well as in the average case. An analysis of this algorithm enables us to conclude that this algorithm is more efficient than other existing ones in the average case.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A unified approach to data association in multitarget tracking

A unified framework is proposed to provide a comprehensive understanding of the problem of data association in multitarget tracking. Under this framework, a systematic scheme is developed for generating the data association hypotheses in the target-oriented, measurement-oriented, and track-oriented approaches. Since there are many data association hypotheses with identical likelihoods in the measurement-oriented and track-oriented approaches, two specialized algorithms are developed to efficiently generate the data association hypotheses in the two approaches by adapting the depth-first search (DFS) algorithm developed earlier in the target-oriented case.

Multitarget tracking in clutter: fast algorithms for data association

Three fast algorithms have been developed to solve the problem of data association in multitarget tracking in clutter. In the first algorithm, the problem of data association is identified as an exhaustive search problem in general. Subsequently, a mathematical model is proposed for the problem of data association in the joint probabilistic data association filter (JPDAF). Based on the model, a depth-first search (DFS) approach is developed for the fast generation of data association hypotheses and the computation of the conditional probabilities of the hypotheses in the JPDAF. When the density of targets is moderate, a second algorithm is developed to directly compute a posteriori probabilities in the JPDAF without generating the data association hypotheses. In the third algorithm, the effect of interference due to closely spaced targets is simplified. An approach to approximately compute the a posteriori probabilities in the JPDAF is developed. The computational complexity of the algorithms is analyzed in the worst case, as well as in the average case.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A comprehensive analysis of 'Neural solution to the multitarget tracking data association problem' by D. Sengupta and R.A. Iltis (1989)

A comprehensive analysis reveals several drawbacks of the neural network probabilistic data association (NPDA) algorithm, which is an application of the Hopfield neural network to the data association problem for multitarget tracking in clutter. >

Neural solution to the multitarget tracking data association problem

The problem of tracking multiple targets in the presence of clutter is addressed. The joint probabilistic data association (JPDA) algorithm has been previously reported to be suitable for this problem in that it makes few assumptions and can handle many targets as long as the clutter density is not very high. However, the complexity of this algorithm increases rapidly with the number of targets and returns. An approximation of the JPDA that uses an analog computational network to solve the data association problem is suggested. The problem is viewed as that of optimizing a suitably chosen energy function. Simple neural-network structures for the approximate minimization of such functions have been proposed by other researchers. The analog network used offers a significant degree of parallelism and thus can compute the association probabilities more rapidly. Computer simulations indicate the ability of the algorithm to track many targets simultaneously in the presence of moderately dense clutter.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Joint probabilistic data association for multitarget tracking with possibly unresolved measurements and maneuvers

In a multitarget environment, when tracking crossing targets, a model is needed for the situation where the measurements from two targets are merged into one due to an inherent resolution threshold. A multidimensional model for the merged measurements is proposed and the resulting pdf is presented. This model is applied to augment the Joint Probabilistic Data Association (JPDA) algorithm used for tracking multiple targets in a cluttered environment, so that it can handle, in a more realistic manner, the situation of crossing targets. An extension to maneuvering targets is also presented.