Relationship between Finite Set Statistics and the Multiple Hypothesis Tracker

The multiple hypothesis tracker (MHT) and finite set statistics (FISST) are two approaches to multitarget tracking, which both have been heralded as optimal. In this paper, we show that the multitarget Bayes filter with basis in FISST can be expressed in terms the MHT formalism, consisting of association hypotheses with corresponding probabilities and hypothesis-conditional densities of the targets. Furthermore, we show that the resulting MHT-like method under appropriate assumptions (Poisson clutter and birth models, no target death, linear-Gaussian Markov target kinematics) only differs from Reid's MHT with regard to the birth process

The multiple hypothesis tracker derived from finite set statistics

The multiple hypothesis tracker (MHT) has historically been considered a gold standard for multi-target tracking. In this paper we show that the key formula for hypothesis probabilities in Reid's MHT can be derived from the modern theory of finite set statistics (FISST) insofar as appropriate assumptions (Poisson models for clutter and undetected targets, no target-death, linear-Gaussian Markov target kinematics) are adhered to

Compensation of navigation uncertainty for target tracking on a moving platform

Established state-of-the art methods for target tracking assume perfect knowledge of the sensor position and orientation. This assumption is violated when the tracking sensor is mounted on a moving platform such as a ship. Two methods for solving this problem are compared. The Schmidt-Kalman filter maintains correlations between the ownship and the target, while a converted measurement approach merely translates the navigation uncertainty into the measurement model of the target. Simulation results indicate that the Schmidt-Kalman filter yields the best improvements with regard to consistency, while the converted measurement approach yields better improvements in root mean square error

Kayak Tracking using a Direct Lidar Model

This paper proposes a direct approach for extended object tracking (EOT) using light detection and ranging (lidar) measurements. The method does not use any clustering operations, but processes the individual laser beams directly in an extended Kalman filter (EKF), and resolves data association by means of techniques reminiscent of the probabilistic data association filter (PDAF). The method is particularly tailored to tracking of kayaks, and parameterizes the shape of the kayak as a stick whose length is part of the state vector. The proposed method is evaluated through a simulation study and tested on real lidar data

Suboptimal Kalman filters for target tracking with navigation uncertainty in one dimension

The vast majority of literature on target tracking assumes that the position and orientation of the tracking sensor is stationary and/or known. However, for many applications the sensor is mounted on a moving vehicle, whose motion only can be estimated with a non-negligible uncertainty. In this paper, we suggest seven possible architectures for Kalman filtering in the simplest such scenario that we can construct: Assuming that both the ownship and a single target moves along a straight line according to linear kinematics. Some of the tracking filters are parameterized in the stationary world frame, while others are parameterized the body frame of the ownship. Also, some of the tracking filters take correlations between target state and ownship state into account, while others neglect such correlations. Simulations demonstrate that the suboptimal architectures may or may not reach similar performance as the optimal filter, depending on the process noise of the target and the performance measure chosen. Simulations of multi-target scenarios demonstrate that compensation of navigation uncertainty generally can reduce track-loss rates and OSPA distance

On Collision Risk Assessment for Autonomous Ships Using Scenario-Based MPC

Collision Avoidance (COLAV) for autonomous ships is challenging since it relies on track estimates of nearby obstacles which are inherently uncertain in both state and intent. This uncertainty must be accounted for in the COLAV system in order to ensure both safe and efficient operation of the vessel in accordance with the traffic rules. Here, a COLAV system built on the Scenario-based Model Predictive Control (SB-MPC) with dynamic probabilistic risk treatment is presented. The system estimates the probability of collision with all nearby obstacles using a combination of Monte Carlo simulation (MCS) and a Kalman Filter (KF), taking the uncertainty in both position and velocity into account. A probabilistic collision cost is then used in the MPC to penalize risk-taking maneuvers. Simulation results show that the proposed method may provide increased robustness due to increased situational awareness, while also being able to efficiently follow the nominal path and adhere to the traffic rules

Cascade Attitude Observer for the SLAM filtering problem

This article presents an attitude observer that exploits both bearing and range measurements from landmarks, in addition to reference vectors such as magnetometer and accelerometer. It is a gyro bias observer in cascade with a simplified complementary filter, driven by a gyro measurement, in which the gyro bias is estimated by comparing the bearing dynamics with the gyro measurements. The observer is compared to a full complimentary filter, and it is shown that it is more robust to initial gyro bias estimation error compared to the complimentary filter. The article also reveals how this new observer handles magnetometer failure and can use landmarks as reference vectors

Cooperative remote sensing of ice using a Spatially Indexed Labeled Multi-Bernoulli filter

In polar region operations, drift ice positioning and tracking is useful for both scientific and safety reasons. Many sensors can be employed to generate detections of sea ice, such as satellite-carried Synthetic Aperture Radar (SAR) and, recently, imagery equipment carried by Unmanned Aerial Systems (UAS). Satellite-carried SAR has the advantage of being able to cover large areas and provide consistent imagery largely independent of weather, albeit at a relatively coarse resolution. Using UAS, the resolution and precision of the tracking can be locally improved. To track the large amount of individual objects present in an area as large as the Arctic, it is necessary to efficiently select and exclusively work with the objects in the relevant field-of-view. In this paper, a Spatially Indexed Labeled Multi-Bernoulli filter is presented and applied to a tracking problem representing a mission setup for field-tests due this year. In the setup, satellite and UAS imagery is combined to provide real-time Multi-Target Tracking of sea ice objects. A brief introduction is given to the implementation of the proposed Spatially Indexed Labeled Multi-Bernoulli Ilter, which is made available under an Open Source license

Vision Restricted Path Planning and Control for Underactuated Vehicles

Autonomous vehicles can obtain navigation information by observing a source with a camera or an acoustic system mounted on the frame of the vehicle. This information properly fused provides navigation information that can overcome the lack of other sources of positioning. However, these systems often have a limited angular field-of-view (FOV). Due to this restriction, motion along some paths will make it impossible to obtain the necessary navigation information as the source is no longer in the vehicle’s FOV. This paper proposes both a path planning approach and a guidance control law that allows the vehicle to preserve a certain object or feature inside the FOV while at the same time converging to the proposed path