Interpolation Method for Update with Out-of-Sequence Measurements: The Augmented Fixed-Lag Smoother

In this study, the authors propose a novel method to handle OOSMs in Kalman filtering. The proposed method, called the augmented fixed-lag smoother (AFLS), is based on the fixed-lag smoother (FLS) formulation, which has been shown to be optimal [10]. We generate the OOSM node from the two adjacent nodes, plug the generated estimations into the state vector and the covariance matrix, and update the filter with OOSMs using the FLS update equation. This approach gives a generalized solution that can handle any number of OOSMs. We also extend the AFLS algorithm to nonlinear system, called the extended AFLS (EAFLS), and give an application example on a satellite-tracking problem

Rao-Blackwellized Out-of-Sequence Processing for Mixed Linear/Nonlinear State-Space Models

We investigate the out-of-sequence measurements particle filtering problem for a set of conditionally linear Gaussian state-space models, known as mixed linear/nonlinear state-space models. Two different algorithms are proposed, which both exploit the conditionally linear substructure. The first approach is based on storing only a subset of the particles and their weights, which implies low memory and computation requirements. The second approach is based on a recently reported Rao-Blackwellized forward filter/backward simulator, adapted to the out-of-sequence filtering task with computational considerations for enabling online implementations. Simulation studies on two examples show that both approaches outperform recently reported particle filters, with the second approach being superior in terms of tracking performance

Rao-Blackwellized Particle Filters with Out-of-Sequence Measurement Processing

This paper addresses the out-of-sequence measurement (OOSM) problem for mixed linear/nonlinear state-space models, which is a class of nonlinear models with a tractable, conditionally linear substructure. We develop two novel algorithms that utilize the linear substructure. The first algorithm effectively employs the Rao-Blackwellized particle filtering framework for updating with the OOSMs, and is based on storing only a subset of the particles and their weights over an arbitrary, predefined interval. The second algorithm adapts a backward simulation approach to update with the delayed (out-of-sequence) measurements, resulting in superior tracking performance. Extensive simulation studies show the efficacy of our approaches in terms of computation time and tracking performance. Both algorithms yield estimation improvements when compared with recent particle filter algorithms for OOSM processing; in the considered examples they achieve up to 10% enhancements in estimation accuracy. In some cases the proposed algorithms even deliver accuracy that is similar to the lower performance bounds. Because the considered setup is common in various estimation scenarios, the developed algorithms enable improvements in different types of filtering applications

A robust navigation filter fusing delayed measurements from multiple sensors and its application to spacecraft rendezvous

A filter is an essential part of many control systems. For example guidance, navigation and control systems for spacecraft rendezvous require a robust navigation filter that generates estimates of the state in a smooth and stable way. This is important for a safe spacecraft navigation within rendezvous missions. Delayed, asynchronous measurements from possibly different sensors require a new filter technique which can handle these different challenges. A new method is developed which is based on an Extended Kalman Filter with several adaptations in the prediction and correction step. Two key aspects are extrapolation of delayed measurements and sensor fusion in the filter correction. The new filter technique is applied on different close-range rendezvous examples and tested at the hardware-in-the-loop facility EPOS 2.0 (European Proximity Operations Simulator) with two different rendezvous sensors. Even with realistic delays by using an ARM-based on-board computer in the hardware-in-the-loop tests the filter is able to provide accurate, stable and smooth state estimates in all test scenarios

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