Bearing-only acoustic tracking of moving speakers for robot audition

This paper focuses on speaker tracking in robot audition for human-robot interaction. Using only acoustic signals, speaker tracking in enclosed spaces is subject to missing detections and spurious clutter measurements due to speech inactivity, reverberation and interference. Furthermore, many acoustic localization approaches estimate speaker direction, hence providing bearing-only measurements without range information. This paper presents a probability hypothesis density (PHD) tracker that augments the bearing-only speaker directions of arrival with a cloud of range hypotheses at speaker initiation and propagates the random variates through time. Furthermore, due to their formulation PHD filters explicitly model, and hence provide robustness against, clutter and missing detections. The approach is verified using experimental results

Estimation and control of multi-object systems with high-fidenlity sensor models: A labelled random finite set approach

Principled and novel multi-object tracking algorithms are proposed, that have the ability to optimally process realistic sensor data, by accommodating complex observational phenomena such as merged measurements and extended targets. Additionally, a sensor control scheme based on a tractable, information theoretic objective is proposed, the goal of which is to optimise tracking performance in multi-object scenarios. The concept of labelled random finite sets is adopted in the development of these new techniques

비지도식 베이지안 온라인 학습을 이용한 미지 환경에서의 다중 로봇 탐사 기법

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2017. 2. 김현진.Exploring an unknown environment with multiple robots is an enabling technology for many useful applications. This paper investigates decentralized motion planning for multi-agent exploration in a field with unknown distributions such as received signal strength (RSS) and terrain elevation. We present both supervised with RSS distribution and unsupervised methods with terrain data. The environment is modelled with a Gaussian process using Bayesian online learning by sharing the information obtained from the measurement history of each robot. Then we use the mean function of the Gaussian process to infer the multiple source locations or peaks of the distribution. The inferred locations of sources or peaks are modelled as the probability distribution using Gaussian mixture-probability hypothesis density (GM-PHD) filter. This modelling enables nonparametric approximation of mutual information between peak locations and future robot positions. We combine the variance function of the Gaussian process and the mutual information to design an informative and noise-robust planning algorithm for multiple robots. At the end, the proposed algorithm is extended by applying an unsupervised method with Dirichlet process mixture of Gaussian processes. The experimental performance of supervised method and unsupervised method are analysed by comparing with the variance-based planning algorithm. The experimental results show that the proposed algorithm learns the unknown environmental distribution more accurately and faster.1 Introduction 1 1.1 Literature review 3 1.2 Thesis contribution 4 1.3 Thesis outline 4 2 Gaussian process model 6 2.1 Gaussian process 6 2.2 Hyperparameter optimization 8 3 Parametrization of signal source location 9 3.1 Conventional GM-PHD lter 9 3.2 Spatial prior on the birth process 11 4 Information-based multi-agent control 12 4.1 Nonparametric computation of mutual information 12 4.2 Concatenated objective-based control policy 14 5 Unsupervised implementation 17 5.1 Dirichlet process mixture of Gaussian processes 17 5.2 Parameter optimization with adaptive rejection sampling 19 6 Simulation and experiment 22 6.1 Experimental settings and results for supervised method 22 6.1.1 Experimental settings 22 6.1.2 RSS distribution learning experiment result 23 6.2 Terrain mapping simulation settings and results for unsupervised method 29 6.2.1 Simulation settings 29 6.2.2 Terrain mapping simulation result 29 6.3 RSS distribution mapping experimental settings and results for unsupervised method 36 6.3.1 Experimental settings 36 6.3.2 RSS distribution mapping experimental result 36 7 Conclusion 43 References 44 국문초록 47Maste

Sensor Fusion and Resource Management in MIMO-OFDM Joint Sensing and Communication

This study explores the promising potential of integrating sensing capabilities into multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM)-based networks through innovative multi-sensor fusion techniques, tracking algorithms, and resource management. A novel data fusion technique is proposed within the MIMO-OFDM system, which promotes cooperative sensing among monostatic joint sensing and communication (JSC) base stations by sharing range-angle maps with a central fusion center. To manage data sharing and control network overhead introduced by cooperation, an excision filter is introduced at each base station. After data fusion, the framework employs a three-step clustering procedure combined with a tracking algorithm to effectively handle point-like and extended targets. Delving into the sensing/communication trade-off, resources such as transmit power, frequency, and time are varied, providing valuable insights into their impact on the overall system performance. Additionally, a sophisticated channel model is proposed, accounting for complex urban propagation scenarios and addressing multipath effects and multiple reflection points for extended targets like vehicles. Evaluation metrics, including optimal sub-pattern assignment (OSPA), downlink sum rate, and bit rate, offer a comprehensive assessment of the system's localization and communication capabilities, as well as network overhead

Acoustic SLAM

An algorithm is presented that enables devices equipped with microphones, such as robots, to move within their environment in order to explore, adapt to and interact with sound sources of interest. Acoustic scene mapping creates a 3D representation of the positional information of sound sources across time and space. In practice, positional source information is only provided by Direction-of-Arrival (DoA) estimates of the source directions; the source-sensor range is typically difficult to obtain. DoA estimates are also adversely affected by reverberation, noise, and interference, leading to errors in source location estimation and consequent false DoA estimates. Moroever, many acoustic sources, such as human talkers, are not continuously active, such that periods of inactivity lead to missing DoA estimates. Withal, the DoA estimates are specified relative to the observer's sensor location and orientation. Accurate positional information about the observer therefore is crucial. This paper proposes Acoustic Simultaneous Localization and Mapping (aSLAM), which uses acoustic signals to simultaneously map the 3D positions of multiple sound sources whilst passively localizing the observer within the scene map. The performance of aSLAM is analyzed and evaluated using a series of realistic simulations. Results are presented to show the impact of the observer motion and sound source localization accuracy

Group and extended target tracking with the probability hypothesis density filter

Multiple target tracking concerns the estimation of an unknown and time-varying number of objects (targets) as they dynamically evolve over time from a sequence of measurements obtained from sensors at discrete time intervals. In the Bayesian ltering framework the estimation problem incorporates natural phenomena such as false measurements and target birth/death. Though theoretically optimal, the generally intractable Bayesian lter requires suitable approximations. This thesis is particularly motivated by a rst-order moment approximation known as the Probability Hypothesis Density (PHD) lter. The emphasis in this thesis is on the further development of the PHD lter for handling more advanced target tracking problems, principally involving multiple group and extended targets. A group target is regarded as a collection of targets that share a common motion or characteristic, while an extended target is regarded as a target that potentially generates multiple measurements. The main contributions are the derivations of the PHD lter for multiple group and extended target tracking problems and their subsequent closed-form solutions. The proposed algorithms are applied in simulated scenarios and their estimate results demonstrate that accurate tracking performance is attainable for certain group/extended target tracking problems. The performance is further analysed with the use of suitable metrics.Engineering and Physical Sciences Research Council (EPSRC) Industrial CASE Award Studentshi