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This paper aims to find an appropriate approach to improve estimation accuracy of bearings-only tracking (BOT) and Doppler bearing tracking (DBT) by making use of the constraint on target speed. Targets usually travel within a valid speed zone so this contextual information (speed inequality constraint) should hypothetically help tracking algorithms (filters) achieve better accuracy. However the inequality...
The method of analytic combinatorics (AC) is a unified approach to multiple object tracking that encodes joint probability distributions into probability generating functionals (PGFLs). PGFLs characterize distributions exactly. A high level view of the tracking applications of PGFLs is outlined in this paper. Assignment models in well-known filters are modeled as products of PGFLs. MHT and multiBernoulli...
Approximate analytical formulae are proposed for the solutions of the weight optimization problems involved in Covariance Intersection (CI) and Inverse Covariance Intersection (ICI). The methodology used for obtaining the analytic approximations boils down to using just two Newton iterations with the initial weight value 1/2. The simulation results show that quite acceptable root-mean-square (RMS)...
The continuous transfer of messages in vehicular ad hoc networks leads to a heavy network traffic load. This causes congestion in the wireless channel which degrades the reliability of the network and significantly affects the Quality of Service (QoS) parameters such as packet loss, throughput and average delay. Therefore, it is vital to adapt the transmitting data rates in a way that ensure that...
This paper studies a selection of estimation schemes for tracking problems where sensors measure range and Doppler while no directional measurements are provided. The selected estimators are relatives of the Kalman filter where either the state propagation equation or the measurement equation is non-linear. Known schemes are compared with a new filter operating in Cartesian state space. The new filter...
Fully automated vehicles and mobile robots operate in a shared environment with pedestrians. To minimize the risk for pedestrians, it is very important to track them in a precise way. As cameras are often installed in surveillance situations, they are used for tracking pedestrians in a shared environment. To improve the accuracy of the tracking, it is necessary to include all available context information...
During the last decades, radar sensors have become an established component in assisted driving systems. Light detection and ranging (LIDAR) sensors are commonly perceived as the key technology to enable fully autonomous driving, but are still expensive. Recent advances in radar technology, enable modern radar sensors to sense the environment in higher detail, thus adding significant value to the...
One of the huge challenges of map-based localization is a rapidly changing environment. The present contribution addresses this problem by first constructing a new framework for feature-based long-term mapping using a Bernoulli filter. This framework is then applied to construct a continuously learning map. It is based on Simultaneous Localization and Mapping (SLAM) to create a short-term map which...
Accurate environmental perception is a key requirement for autonomous driving. While the robust and precise estimation of the dynamic state of nearby objects is sufficient for ordinary driver assistance systems like adaptive cruise control, higher levels of autonomy require knowledge of the extent of objects for measurement data association and path finding algorithms. Extent estimation is known to...
This paper presents a tightly-coupled INS/GNSS integration for personal navigation systems using foot-mounted sensors. Our precise relative positioning INS based on high accurate Zero-Velocity-Updates (ZUPTs) is fused with GNSS pseudo-range and doppler measurements for absolute position and heading estimation. Unique is the decision depending on the classified motion state if doppler measurements...
In this paper, the passive localization and tracking of aircrafts sending Automatic Dependent Surveillance-Broadcast (ADS-B)/Mode-S transponder signals is investigated. A sensor network taking Time (Difference) of Arrival (TOA/TDOA) measurements is considered. TDOA/TOA-based geolocation is used along with a Kalman filter to localize and track air-crafts. Experimental results show the feasibility to...
Modern advanced driver assistance systems (ADAS) and automated driving functions for automobiles rely on an accurate model of the environment. To this end, the exploitation of complementary advantages of the measurement principles used by radar, lidar and camera sensors is an important prerequisite. We develop a framework for sensor data fusion that incorporates heterogeneous sensor data from multiple...
This paper presents a model for the deviation of distances measured by radar and by optical sensors (3D point clouds). The measured 3D point clouds are typically clustered to objects and the cluster centers are then associated with the radar targets. However, the physical extent and the object geometry cause a divergence of the measured radar range from the cluster centers of the 3D point cloud. Existing...
Small/micro Unmanned Aerial Systems (UAVs) require the ability to operate with constraints of a diverse, automated airspace where obstacle telemetry is denied. This paper proposes a novel Sense, Detect and Avoid (SDA) algorithm with inherit resilience to sensor uncertainty. This is achieved through the interval geometric formulation of the avoidance problem, which by the use of interval analysis,...
We present research with regard to a world modeling component called Object-Oriented World Model (OOWM) in order to perform High-Level Information Management (HLIM) in Joint ISR (Intelligence, Surveillance, and Reconnaissance). The OOWM allows to structure a priori available domain knowledge as well as current information gathered, e.g., according to specific collection and exploitation tasks, in...
Bayesian recursive estimation using large volumes of data is a challenging research topic. The problem becomes particularly complex for high dimensional non-linear state spaces. Markov chain Monte Carlo (MCMC) based methods have been successfully used to solve such problems. The main issue when employing MCMC is the evaluation of the likelihood function at every iteration, which can become prohibitively...
Robust semantic knowledge of the environment is one of the building blocks for autonomous driving. If different sensor types are employed for the same task independently, the overall accuracy and safety of the system can increase. Therefore, it is desirable to maximize each sensor's capabilities and to build up redundancies, as it is often required by functional safety. To this end, this paper demonstrates...
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