Indoor navigation could be used in many applications to enhance performance in its specific area. Anything from serious life critical tasks like aiding firefighters or coordinating military attacks to more simple every day use like finding a desired shop in a large supermarket could be considered. Smartphones of today introduce an interesting platform with capabilities like existing, more clumsy, indoor navigation systems. The iPhone 3GS is a powerful smartphone that lets the programmer use its hardware in an efficient and easy way. The iPhone 3GS has a 3-axis accelerometer, a 3-axis magnetometer and hardware accelerated image rendering which is used in this thesis to track the user on an indoor map. A particle filter is used to track the position of the user. The implementation shows how many particles the iPhone will be able to handle and update in real time without lag in the application.

In modern systems for air surveillance, it is important to have a high quality situationassessment. SAAB has a system for air surveillance, and in this thesis possibleimprovements of the tracking performance of this system are explored. The focushas been on improving the tracking of highly maneuverable targets observed withlow sampling rate. To evaluate improvements of the tracking performance, a componentthat is similar to the one used in SAAB’s present tracker was implementedin an Interacting Multiple Model (IMM) structure. The use of an Auxiliary ParticleFilter for improving the tracking performance is explored, and a way to fita particle filter into SAAB’s existing IMM framework is proposed. The differentfilters were implemented in Matlab, and evaluation was done by the meansof Monte Carlo simulations. The results from Monte Carlo simulations show significantimprovement when tracking in two dimensions. However, the results inthree dimensions do not display any substantial overall improvement when usingthe particle filter compared to using SAAB’s present filter. It is therefore notworthwhile to switch the filter used in SAAB’s present tracker for a particle filter,at least not under the high SNR circumstances presented in this thesis. However,further studies within this area are recommended before any final decisions aremade.

In decentralized data fusion system, if the probability model of the noise is Gaussian and the innovation informations from the sensors are uncorrlated,the information filtering technique can be the best method to fuse the information from different sensors. However, in the realistic environments, information filter cannot provide the best solution of state estimation and data integration when the noises are non-Gaussian and correlated. Since particle filter are capable of dealing with non-linear and non-Gaussian problems, it is an intuitive approach to replace the information filter by particle filter with some suitable data fusion techniques.In this thesis, we investigate a decentralized data fusion system with improved particle filters for target tracking. In order to achieve better tracking performance, the Iterated Extended Kalman Filter framework is used to incorporate the newest observations into the proposal distribution of the particle filter. In our proposed architecture, each sensor consists of one particle filter, which is used in generating the local statistics of the system state. Gaussian mixture model (GMM) is adopted to approximate the posterior distribution of the weighted particles in the filters, thereby more compact representations of the distribution for transmmision can be obtained. To achieve information sharing and integration, the GMM-Covariance Intersection algorithm is used in formulating the decentralized fusion solutions. Simulation resluts of target tracking cases in a sensor system with two sensor nodes are given to show the effectiveness and superiorty of the proposed architecture.

In this thesis, we investigate a decentralized data fusion system with improved particle filters for target tracking. In many application areas, it becomes essential to use nonlinear and non-Gaussian elements to accurately model the underlying dynamics of a physical system. Particle filters have a great potential for solving highly nonlinear and non-Gaussian estimation problems, in which the traditional Kalman filter and extended Kalman filter may generally fail. To improve the tracking performance of particle filters, initialization of the particles is studied. We construct an initial state distribution by using least square estimation. In addition, to enhance the tracking capability of particle filters, representation of target velocity by another set of particles is considered. We include another layer of particle filter inside the original particle filter for updating the velocity. In our proposed architecture, we assume that each sensor node contain a particle filter and there is no fusion center in the sensor network. Approximated a posteriori distribution at the sensor is obtained by using the local particle filters with the Gaussian mixture model (GMM), so that more compact representations of the distribution for transmission can be obtained. To achieve information sharing and integration, the GMM-covariance intersection algorithm is used in formulating the decentralized fusion solutions. Simulation results are presented to illustrate that the performance of the improved particle filter is better than standard particle filter. In addition, simulation results of target tracking in the sensor system with three sensor nodes are given to show the effectiveness and superiority of the proposed architecture.

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This thesis considers the problem of learning the structure of gene regulatory networks using gene expression time series data. A more realistic scenario where the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter based state estimation algorithm is studied instead of the contemporary linear approximation based approaches. The parameters signifying the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then fed to a LASSO based least squares regression operation, which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with Extended Kalman filtering (EKF), employing Mean Square Error as fidelity criterion using synthetic data and real biological data. Extensive computer simulations illustrate that the particle filter based gene network inference algorithm outperforms EKF and therefore, it can serve as a natural framework for modeling gene regulatory networks.

Many multiple target tracking algorithms operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field of view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field of view. We propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves then re-enters the UAV’s field of view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, exhaustive receding horizon control (ERHC) and a neural net trained with deep reinforcement learning (Deep-RL), and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target’s entropy. While the ERHC achieved optimal stead-state estimates the DeepRL controller identified more efficient sweeping search patterns when there is limited information regarding target locations. The neural net achieves O(1) computational complexity during decision making but must first be trained on a given map. In addition, we provide a theorem that calculates the lower-bound for the average-entropy of the RBPF. Particle Filter entropy is used as a unit of measurement as it gives a way of accurately comparing the precision of complex multi-modal estimates. This gives a reliable way of establishing the resources needed to accomplish mission objectives as well as providing a reliable method of determining the effectiveness of different multi-agent path planners. Finally we outline results both in simulation and hardware. In simulation we obtained the results for our different path planners over 2000 Monte Carlo runs and show how the different path planners compare and measure up to the lower-bound of average-entropy. The results from a hardware test provide evidence that the ideas presented in this thesis hold true in an end-to-end solution.

Many multiple target tracking algorithms operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field of view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field of view. We propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves then re-enters the UAV's field of view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, exhaustive receding horizon control (ERHC) and a neural net trained with deep reinforcement learning (Deep-RL), and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target's entropy. While the ERHC achieved optimal stead-state estimates the Deep-RL controller identified more efficient sweeping search patterns when there is limited information regarding target locations. The neural net achieves O(1) computational complexity during decision making but must first be trained on a given map. In addition, we provide a theorem that calculates the lower-bound for the average-entropy of the RBPF. Particle Filter entropy is used as a unit of measurement as it gives a way of accurately comparing the precision of complex multi-modal estimates. This gives a reliable way of establishing the resources needed to accomplish mission objectives as well as providing a reliable method of determining the effectiveness of different multi-agent path planners. Finally we outline results both in simulation and hardware. In simulation we obtained the results for our different path planners over 2000 Monte Carlo runs and show how the different path planners compare and measure up to the lower-bound of average-entropy. The results from a hardware test provide evidence that the ideas presented in this thesis hold true in an end-to-end solution.

Removing the effects of the atmosphere from remote sensing data requires accurate knowledge of the physical properties of the atmosphere during the time of measurement. There is a nonlinear relationship that maps atmospheric composition to emitted spectra, but it cannot be easily inverted. The time evolution of atmospheric composition is approximately Markovian, and can be estimated using hyperspectral measurements of the atmosphere with particle filters. The difficulties associated with particle filtering high-dimension data can be mitigated by incorporating future measurement data with the proposal density.

No description available.