Seoul National University · 情報科学
Professor Songhwai Oh's research lab specializes in autonomous multi-target tracking and real-time decision-making systems for wireless sensor networks. The lab focuses on developing scalable, efficient, and robust algorithms for data association, state estimation, and control in dynamic, cluttered environments—particularly under constraints of limited computational resources and unreliable sensor data. Key research directions include Markov chain Monte Carlo methods for approximate Bayesian inference, hierarchical control architectures for pursuit-evasion games, and real-time sensor network systems that can autonomously detect, track, and respond to multiple moving targets. The lab emphasizes practical deployment of these algorithms in challenging scenarios involving sensor noise, packet loss, and unknown target counts.
Figures are computed from collected data and may differ slightly.
This paper presents Markov chain Monte Carlo data association (MCMCDA) for solving data association problems arising in multitarget tracking in a cluttered environment. When the number of targets is fixed, the single-scan version of MCMCDA approximates joint probabilistic data association (JPDA). Although the exact computation of association probabilities in JPDA is NP-hard, we prove that the single-scan MCMCDA algorithm provides a fully polynomial randomized approximation scheme for JPDA. For g
In this paper, we consider the general multiple-target tracking problem in which an unknown number of targets appears and disappears at random times and the goal is to find the tracks of targets from noisy observations. We propose an efficient real-time algorithm that solves the data association problem and is capable of initiating and terminating a varying number of tracks. We take the data-oriented, combinatorial optimization approach to the data association problem but avoid the enumeration o
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper considers the problem of pursuit evasion games (PEGs), where the objective of a group of pursuers is to chase and capture a group of evaders in minimum time with the aid of a sensor network. The main challenge in developing a real-time control system using sensor networks is the inconsistency in sensor measurements due to packet loss, communication delay, and false detections. We address t
Multiple-target tracking is a canonical application of sensor networks as it exhibits different aspects of sensor networks such as event detection, sensor information fusion, multi-hop communication, sensor management and decision making. The task of tracking multiple objects in a sensor network is challenging due to constraints on a sensor node such as short communication and sensing ranges, a limited amount of memory and limited computational power. In addition, since a sensor network surveill
Joint probabilistic data association (JPDA) is a powerful tool for solving data association problems. However, the exact computation of association probabilities {/spl beta//sub jk/} in JPDA is NP-hard, where /spl beta//sub jk/ is the probability that j-th observation is from k-th track. Hence, we cannot expect to compute association probabilities in JPDA exactly in polynomial time unless P = NP. In this paper, we present a simple Markov chain Monte Carlo data association (MCMCDA) algorithm that
Multi-target tracking is a representative real-time application of sensor networks as it exhibits different aspects of sensor networks such as event detection, sensor information fusion, multihop communication, sensor management, and real-time decision making. The task of tracking multiple objects in a wireless sensor network is challenging due to constraints on a sensor node such as short communication and sensing ranges, a limited amount of memory, and limited computational power. In addition,
In this paper, we consider the problem of tracking multiple targets and managing their identities in sensor networks. Each sensor is assumed to be able to track multiple targets, manage the identities of targets within its surveillance region, and communicate with its neighboring sensors. The problem is complicated by the fact that the number of targets within the surveillance region of a sensor changes over time. We propose a scalable distributed multiple-target tracking and identity management
This paper introduces a distributed networked control system (DNCS) consisting of multiple agents communicating over a lossy communication channel, e.g., wireless channel. Two aspects of DNCSs are studied in this paper - state estimation and stabilizing communication control. Based on the Kalman filter, optimal linear filtering algorithms are derived for the discrete-time linear dynamic models of the DNCS with lossy links. Then, the problem of finding a communication control which stabilizes a D
This paper presents an explorative navigation method using sparse Gaussian processes for mobile sensor networks. We first show that a near-optimal approximation is possible with a subset of measurements if we select the subset carefully, i.e., if the correlation between the selected measurements and the remaining measurements is small and the correlation between the prediction locations and the remaining measurements is small. An estimation method based on a subset of measurements is desirable f
In this paper, we present two approximate filtering algorithms for estimating states of a distributed networked control system (DNCS). A DNCS consists of multiple agents communicating over a lossy communication channel, e.g., wireless channel. While the time complexity of the exact method can be exponential in the number of communication links, the time complexity of an approximate method is not dependent on the number of communication links. In addition, we discuss the general conditions for st
Tracking multiple maneuvering targets in a cluttered environment is a challenging problem. A combination of interacting multiple model (IMM) and joint probabilistic data association (JPDA) has been successfully applied to track multiple maneuvering targets. In IMM, the motion of a maneuvering target is approximated by a finite number of simple, distinct kinematic models. However, the exact computation of the combined approach has the time complexity which is exponential in the numbers of kinemat
The problem of tracking multiple targets and managing their identities in sensor networks is considered. Each sensor is assumed to have its own surveillance region and an ability to communicate with its neighboring sensors. We propose a scalable, distributed, multitarget-tracking and identity-management algorithm that can track an unknown number of targets and manage their identities efficiently in a distributed sensor network environment. Distributed multitarget tracking and identity management
This paper proposes VibeComm, a novel communication method for smart devices using a built-in vibrator and accelerometer. The proposed approach is ideal for low-rate off-line communication, and its communication medium is an object on which smart devices are placed, such as tables and desks. When more than two smart devices are placed on an object and one device wants to transmit a message to the other devices, the transmitting device generates a sequence of vibrations. The vibrations are propag
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