Converted Measurement Trackers for Systems with Nonlinear Measurement Functions

Converted measurement tracking is a technique that filters in the coordinate system where the underlying process of interest is linear and Gaussian, and requires the measurements to be nonlinearly transformed to fit. The goal of the transformation is to allow for tracking in the coordinate system that is most natural for describing system dynamics. There are two potential issues that arise when performing converted measurement tracking. The first is conversion bias that occurs when the measurement transformation introduces a bias in the expected value of the converted measurement. The second is estimation bias that occurs because the estimate of the converted measurement error covariance is correlated with the measurement noise, leading to a biased Kalman gain. The goal of this research is to develop a new approach to converted measurement tracking that eliminates the conversion bias and mitigates the estimation bias. This new decorrelated unbiased converted measurement (DUCM) approach is developed and applied to numerous tracking problems applicable to sonar and radar systems. The resulting methods are compared to the current state of the art based on their mean square error (MSE) performance, consistency and performance with respect to the posterior Cramer-Rao lower bound

Radar networks: A review of features and challenges

Networks of multiple radars are typically used for improving the coverage and tracking accuracy. Recently, such networks have facilitated deployment of commercial radars for civilian applications such as healthcare, gesture recognition, home security, and autonomous automobiles. They exploit advanced signal processing techniques together with efficient data fusion methods in order to yield high performance of event detection and tracking. This paper reviews outstanding features of radar networks, their challenges, and their state-of-the-art solutions from the perspective of signal processing. Each discussed subject can be evolved as a hot research topic.Comment: To appear soon in Information Fusio

Robust Multi-target Tracking with Bootstrapped-GLMB Filter

This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters

Radar/electro-optical data fusion for non-cooperative UAS sense and avoid

Abstract This paper focuses on hardware/software implementation and flight results relevant to a multi-sensor obstacle detection and tracking system based on radar/electro-optical (EO) data fusion. The sensing system was installed onboard an optionally piloted very light aircraft (VLA). Test flights with a single intruder plane of the same class were carried out to evaluate the level of achievable situational awareness and the capability to support autonomous collision avoidance. System architecture is presented and special emphasis is given to adopted solutions regarding real time integration of sensors and navigation measurements and high accuracy estimation of sensors alignment. On the basis of Global Positioning System (GPS) navigation data gathered simultaneously with multi-sensor tracking flight experiments, potential of radar/EO fusion is compared with standalone radar tracking. Flight results demonstrate a significant improvement of collision detection performance, mostly due to the change in angular rate estimation accuracy, and confirm data fusion effectiveness for facing EO detection issues. Relative sensors alignment, performance of the navigation unit, and cross-sensor cueing are found to be key factors to fully exploit the potential of multi-sensor architectures

Novel methods for multi-target tracking with applications in sensor registration and fusion

Maintaining surveillance over vast volumes of space is an increasingly important capability for the defence industry. A clearer and more accurate picture of a surveillance region could be obtained through sensor fusion between a network of sensors. However, this accurate picture is dependent on the sensor registration being resolved. Any inaccuracies in sensor location or orientation can manifest themselves into the sensor measurements that are used in the fusion process, and lead to poor target tracking performance. Solutions previously proposed in the literature for the sensor registration problem have been based on a number of assumptions that do not always hold in practice, such as having a synchronous network and having small, static registration errors. This thesis will propose a number of solutions to resolving the sensor registration and sensor fusion problems jointly in an efficient manner. The assumptions made in previous works will be loosened or removed, making the solutions more applicable to problems that we are likely to see in practice. The proposed methods will be applied to both simulated data, and a segment of data taken from a live trial in the field

Mission Planning Tool for space debris studies with the MeerKAT radar

The Radar Remote Sensing Group at the University of Cape Town is currently investigating the feasibility of building an active radar system employing the MeerKAT radio telescope as receiver for space debris detection, tracking and imaging. This dissertation details the development of a Mission Planning Tool (MPT) to perform sensor scheduling and to support the performance prediction and analysis of the proposed MeerKAT radar. The MeerKAT radar project proposal is made in the context of developing space surveillance and tracking capacities in South Africa. The MeerKAT radar is intended to operate bistatically, with a transmitter located in Bredasdorp (South Africa) and the MeerKAT radio telescope as receiver. The system design and radar signal processing design are currently under development in another RRSG project. Before the feasibility study can progress further, a Mission Planning Tool has been developed to assist in scheduling the bistatic radar to perform an observation experiment, to calculate the predicted radar measurements and errors as well as to estimate the orbit of the observed object. This report documents how these objectives were met by the MPT software developed in Python. Given a LEO space object of interest’s Two Line Element set, the MPT performs orbit propagation with an SGP4 method to generate trajectories for radar performance evaluation. The MPT determines the most opportune epoch (the longest possible target dwell-time within the antenna beam) for executing an observation experiment with the MeerKAT radar. Space objects investigated in this project were found to be have spent between 4.5 s to 12.8 s in the transmitter’s illuminating beam. The MeerKAT radio telescopes are tasked to act as receivers at the appropriate antenna pointing and time period. Based on the bistatic geometry of the specific observation experiment, the MPT predicts the signal-to-noise ratio at the radar receiver as well as the bistatic range, bistatic Doppler shift and look angles. The integrated SNR values for the experiments considered in this report ranged from 11 dB to 68 dB. From the coherently integrated SNR, the MPT estimates the radar measurement errors. Finally, the orbit determination module was engineered with two radar measurement schemes: a bistatic range and Doppler shift scheme and a bistatic range and look angles scheme. Monte Carlo experiments were run to evaluate the tracking performance resulting from the two tracking schemes. The Gauss-Newton tracking filter based on the first scheme fails to converge whereas it produces accurate results with the second scheme (estimated position error of 2 m and velocity error of 0.08 m/s). It is therefore recommended to opt for the bistatic range and look angles measurement scheme in future work. Since the current MeerKAT radar design cannot create look angles measurements, an observables estimation scheme was adopted. It was found that this scheme produced accurate elevation and azimuth angles with an estimation error of ±0.04◦ . Since the quoted values result from a preliminary design of the MeerKAT radar, they are bound to change in the final design. Therefore the MPT should be loaded with the final radar design’s parameters and run again to produce useful results. This reports shows that, with the help of the Mission Planning Tool developed in this project, the proposed MeerKAT radar can be feasibly scheduled to observe and track space objects in the LEO regime based on a single target pass

Mehrobjekt-Zustandsschätzung mit verteilten Sensorträgern am Beispiel der Umfeldwahrnehmung im Straßenverkehr

Umfeldwahrnehmung im automobilen Kontext kann als Zustandsschätzproblem mit mengenwertigem Systemzustand betrachtet werden. Basierend auf FISST wird eine SLAM-ähnliche Methodik gewählt, welche explizit die Unsicherheit bei der Lokalisierung des Sensorträgers berücksichtigt. Diese wird auf die PHD-, JIPDA- und MEMBER-Filteransätze angewandt. Hierbei ist eine Modifikation des Standardmessmodells nötig, um zu implementierbaren Korrekturgleichungen zu gelangen

Mehrobjekt-Zustandsschätzung mit verteilten Sensorträgern am Beispiel der Umfeldwahrnehmung im Straßenverkehr

Umfeldwahrnehmung im automobilen Kontext kann als Zustandsschätzproblem mit mengenwertigem Systemzustand betrachtet werden. Basierend auf FISST wird eine SLAM-ähnliche Methodik gewählt, welche explizit die Unsicherheit bei der Lokalisierung des Sensorträgers berücksichtigt. Diese wird auf die PHD-, JIPDA- und MEMBER-Filteransätze angewandt. Hierbei ist eine Modifikation des Standardmessmodells nötig, um zu implementierbaren Korrekturgleichungen zu gelangen