A widely-used approach for extended object tracking is based on random matrices, where the scattering matrix, i.e., measurement spread, is used to update a symmetric positive definite random matrix representing an elliptic extent. However, for lidar data, a mismatch between the assumed measurement model and observed data hinders the estimation quality of the method. We propose adaptions to the random matrix approach in order to facilitate the application for tracking a rectangular extended object based on contour measurements. Specifically, we derive a suitable scaling factor for the scattering matrix of measurements in this setting. Furthermore, we propose a simple yet effective estimation scheme for the target center, adapting the shape estimate accordingly. The resulting algorithm closely follows the framework of the random matrix approach. A detailed comparison with a variety of state-of-the-art trackers is carried out in a simulation based on real-world lidar parameters, confirming the effectiveness of the approach.

A novel concept for indoor self-localization based on rotating artificial landmarks with known locations using short-range radar is proposed. First, a processing pipeline for extracting range and angle measurements to the landmarks from a raw radar image is introduced, which consists of a neural network for distance estimation and a basic angle-of-arrival estimator. Second, a particle filter for tracking the pose based on the range and angle measurements is developed. Due to the ability of radar to measure range rate, i.e., the velocity in the direction of a detection, it is possible to robustly detect and localize rotating landmarks with the help of their micro-Doppler pattern. In this way, localization is possible even under difficult conditions (e.g., light changes). Experiments with a wheeled mobile robot and common office fans as landmarks demonstrate the effectiveness of the approach for indoor localization.

Multi-sensor multi-object tracking in a track-to-track fusion framework involves the grouping of tracks (from different sensors) that belong to the same perceived object. In particular for collective perception scenarios in large-scale traffic systems the number of sensors and objects can be huge, as a large number of vehicles can be equipped with multiple sensors. In order to cope with the intractable number of possible associations, recently a stochastic optimization approach for track-to-track association was proposed. The key idea is to successively improve an initial association by means of performing random modifications, i.e., actions, on the current association. In this work, we develop a novel deterministic version of the algorithm, which employs herding in order to deterministically choose the next action. Simulations demonstrate that the deterministic version of stochastic optimization provides comparable results to the stochastic version with a significantly lower variance.

With the growing availability of high-resolution sensors, processing more than one detection per target becomes increasingly critical when tracking multiple extended objects. However, contemporary sensors often generate spurious detections that need to be considered. Naively employing standard multitarget trackers may result in poor tracking performance for multitarget–multidetection tracking in cluttered environments, and the relevant extensions are nontrivial. This paper introduces a version of the kernel symmetric measurement equation (SME) filter that considers both multidetections and clutter. For a simulated scenario, our novel filter achieved a higher accuracy than the global nearest neighbor (GNN) and a fast variant of the joint probabilistic data association filter (JPDAF).

Successful tracking of an extended object requires accurate estimation of the target’s shape. In order to evaluate the estimation performance of such tracking algorithms, the target shape must be incorporated by a suitable metric or score. In this context, a common task is to determine a consolidated scalar score for the estimation accuracy across a set of different target types. We highlight problems exhibited by existing scores in this case, particularly if the data set consists of objects of diverse sizes. To this end, we focus on elliptical targets. Furthermore, we present a scale-invariant adaption of the commonly used Gaussian Wasserstein Distance, which does not suffer from the highlighted problem.

Recursive estimation of the orientation and spatial extent of an object from sparse measurements is a challenging yet crucial problem. To this end, we propose a problem-tailored particle filter that employs a special importance density for efficient sampling from high likelihood regions. This importance density is obtained with the help of a suitable analytic method for determining the length and width of the object. Due to the geometric meaning of the state vector, a consolidated estimate from the individual particles is obtained based on the Gaussian Wasserstein distance. The resulting filter can be employed with different analytic methods, and is found to improve estimation accuracy beyond state-of-the-art algorithms in a challenging scenario. Especially for low measurement rates, the proposed filter yields improved results compared to the reference methods.

The aeroelastic behaviour of aircraft is parameter variant. Changing flight conditions, such as e.g. flight velocity and altitude may change the vibration damping. When the vibration damping becomes zero or negative, self-excitation of the vibration occurs, called flutter. Modal parameter identification can be applied to extract eigenfrequencies and damping ratios based on e.g. acceleration data. In order to avoid flutter, modal parameters can be identified in flight testing of a new aircraft type close to real-time using optimized algorithms. Real-time identification of modal parameters has significant uncertainties, especially with respect to damping ratios. Those uncertainties cannot be calculated, but qualitatively estimated. In this study, a Kalman filter tracking is applied to reduce the uncertainties of modal parameter monitoring of aircraft. Since the process noise of such a system is impossible to foresee and is expected to change throughout a flight, the process noise is adapted with respect to the innovation and changing flight conditions. This context-aware adaptive Kalman filter is tested on data from a simulated aeroelastic model as well as on real flight test data of a small-scale fixed-wing UAV. The results show significant reduction of the identification uncertainties for both simulated and real data.

Multi-sensor fusion can improve environment perception, e.g., by increasing the field of view in collective perception, where intelligent vehicles communicate. Track-to-track fusion in a collective perception scenario makes it necessary to associate tracks from multiple sensors. Especially in settings with many sensors that have limited field of view, track-to-track association can be quite challenging. In this work, we develop a stochastic optimization-based approach for an arbitrary number of sensors with a limited field of view, which utilizes a cluster likelihood to sample joint associations. The proposed method clearly outperforms a greedy approach and computes the most likely associations with only a few samples. We evaluate the approaches on simulated data in static and dynamic scenarios.

Global Navigation Satellite Systems (GNSS) provide precise positioning under open-sky conditions, such as highways. However, in urban canyons, buildings block and reflect the signals, causing multipath positioning errors. Multi-frequency transmission and collaborative positioning are two technologies that have been proposed to reduce multipath errors. Still, the magnitude of their individual and combined advantage in reducing multipath errors is unknown. To fill this gap, we simulated dual-frequency collaborative positioning with four vehicles in an open-sky environment and in an urban environment. We compared two solution algorithms for position estimation: the Gauss-Newton solver (GN) and the extended Kalman filter (EKF). This paper presents the performance of these two algorithms under the previously mentioned assumptions. Furthermore, we show how the information from dual-frequency reception can be used to select the most relevant satellites. In the urban environment, the GN and the EKF using dual-frequency reception and collaborative positioning are the solutions with the smallest RMS positioning error (under 2.5 m), Additionally, in the simulated urban environment, dual-frequency reception contributes more to reducing multipath errors than collaborative positioning. As a consequence, when developing automotive positioning systems, multi-frequency reception and collaborative positioning should ideally be combined, but with higher priority on multi-frequency reception.

In this work, we discuss and compare Kalman filter-based approaches for tracking an elliptic extended object parameterized with orientation and semi-axes lengths. The methods include an Extended Kalman filter (EKF) implementation of the Random Hypersurface Model (RHM) approach using a radial function, an EKF-based approach for the Multiplicative Error Model (MEM), called MEM-EKF*, and a method for tracking the semi-axes independently, the Independent Axes Estimation (IAE) approach. We discuss pros and cons of the methods and compare them in various scenarios with a maneuvering object.

In the case of high-resolution or near field sensors, an object normally gives rise to multiple measurements per scan. One of the key tasks in tracking such objects is to differentiate the origins of the measurements. In this work, a new data association approach for extended object tracking, which is inspired by Joint Integrated Probabilistic Data Association (JIPDA), is proposed. The key idea is to calculate marginal association probabilities for individual measurements (instead of considering measurement partitions). Our problem formulation allows us to obtain the marginal association probabilities without collective exhaustion of association hypotheses and partitions. The proposed data association method is illustrated first using a simulation with Gaussian distributed measurements. Combined with an extended object measurement model, the data association quality is further assessed in a simulation and an experiment by tracking pedestrians using Lidar data from the KITTI dataset.

This work presents a method for qualitatively improving the output of an existing video and radar-based multitarget tracking system by means of post-processing. The proposed method is a novel two-pass track stitching method, which involves a breaking phase for tracklet creation and a linking phase to create putative assignments. Minimum-cost network flow techniques are utilized to find optimal tracks. The method has been tested with data from a research intersection in Braunschweig, Germany, which is operated by the German Aerospace Center for the purpose of traffic safety analysis. Furthermore, an evaluation with the publicly available MOTChallenge benchmark is provided. Improvements achieved are: 14% to 33% less ID switches and 40% less track fragmentations. Both criteria are especially relevant for traffic safety analysis.

In this paper a novel representation of the contour of an spatially extended object is proposed, which is tailored to vehicles with an unknown size and orientation that are tracked based on measurements from an automotive Light Detection and Ranging (LIDAR). We deploy quadratic uniform periodic B-Splines to directly represent a star-convex shape approximation of the object in Cartesian space. In contrast to previous approaches that work in polar space, we introduce a new walk parameter to model the contour function of an object such that the shapes parameters are well defined and lie within the same space as the measurements. A major advantage of the approach is that a scaling of the length and the width can be performed independently by scaling the basis points of the Splines.

In this paper, we present a new approach for solving the SLAM problem using the Ensemble Kalman Filter (EnKF). In contrast to other Kalman filter based approaches, the EnKF uses a small set of ensemble members to represent the state, thus circumventing the computation of the large covariance matrix traditionally used with Kalman filters, making this approach a viable application in high-dimensional state spaces. Our approach adapts techniques from the geoscientific community such as localization to the SLAM problem domain as well as using the Optimal Subpattern Assignment (OSPA) metric for data association. We then compare the results of our algorithm with an extended Kalman filter (EKF) and FastSLAM, showing that our approach yields a more robust, accurate, and computationally less demanding solution than the EKF and similar results to FastSLAM.

In this paper, an implementation of an extended target tracking filter using measurements from high-resolution automotive Radio Detection and Ranging (RADAR) is proposed. Our algorithm uses the Cartesian point measurements from the target's contour as well as the Doppler range rate provided by the RADAR to track a target vehicle's position, orientation, and translational and rotational velocities. We also apply a Gaussian Process (GP) to model the vehicle's shape. To cope with the nonlinear measurement equation, we implement an Extended Kalman Filter (EKF) and provide the necessary derivatives for the Doppler measurement. We then evaluate the effectiveness of incorporating the Doppler rate on simulations and on 2 sets of real data.

Direction-of-arrival (DOA) estimation and tracking of signals using passive sensor arrays is a classic problem that becomes challenging when the number of sources varies over time and the signal-to-noise ratio is low. In this paper, we pose this problem as minimum mean OSPA (MMOSPA) estimation, which minimizes the the optimal sub-pattern assignment (OSPA) metric of the posterior random finite set (RFS). A particle filter implementation of the MMOSPA estimator is developed for simultaneous source enumeration and DOA tracking. The performance of the new method is demonstrated by means of an experiment with a large sonar array in Florida.

We develop an Expectation-Maximization (EM) algorithm for the simultaneous tracking and shape estimation of a star-convex object based on multiple spatially distributed measurements. In order to formulate the problem within the EM framework, the unknown measurement sources on the object are modeled as hidden variables. As the measurement sources are continuous quantities, we develop a suitable discretization method that allows for a closed-form EM iteration. The performance of the EM approach is demonstrated in comparison with a recursive Gaussian filter based on the Random Hypersurface Model (RHM).

We present a particle filter for multi-object tracking that is based on the ideas of the Approximate Bayesian Computation (ABC) paradigm. The main idea is to avoid the explicit computation of the likelihood function by means of simulation. For this purpose, a large amount of particles in the state space is simulated from the prior, transformed into measurement space, and then compared to the real measurement by using an appropriate distance function, i.e., the OSPA distance. By selecting the closest simulated measurements and their corresponding particles in state space, the posterior distribution is approximated. The algorithm is evaluated in a multi-object scenario with and without clutter and is compared to a global nearest neighbour Kalman filter.

In this paper, a plain data-driven and simulation-based approach to object tracking is investigated. The basic idea is to use the probabilistic model of the tracking problem to simulate a large amount of state and observation sequences. Both are fed into a regression algorithm that learns a mapping from the observations to the states. In particular, we consider random forest regression and apply it to an object tracking problem using bearing-range measurements. The performance of the random forest tracking is compared to a Kalman smoother and particle filter.