On measuring available bandwidth in wireless networks

Many technologies need to be established to realize autonomous ships. In particular, accurate state estimation in real time is one of the most important technologies. In the ship and ocean engineering fields, there have been many studies on state estimation using nonlinear Kalman filters. Several methods have been proposed for nonlinear Kalman filters. However, there is insufficient verification on the selection of which filter should be applied among them. Therefore, this study aims to validate the filter selection to provide a guideline for filter selection. The effects of modeling error, observation noise, and type of maneuvers on the estimation accuracy of the unscented Kalman filter (UKF) and ensemble Kalman filter (EnKF) used in this study were investigated. In addition, it was verified whether filtering could be performed in real time. The results show that modeling error significantly impacts the estimation accuracy of the UKF and EnKF. However, the observation noise and types of maneuvers did not have an impact like the modeling error. Thus, we obtained the guideline that UKF and EnKF should be used differently depending on the required computation time. We also obtained that keeping the modeling error sufficiently small is essential to improving the estimation accuracy.

The article proposes an integrated approach to modeling the initial stage of tracking air targets in radar systems based on a combination of a probabilistic-temporal model of the GERT type and a recursive state estimation procedure using the Kalman filter. At the macro level, the process of interaction "radar-tracked object" is described by an exponential GERT network using moment generating functions, which allows analytically forming an equivalent W-function, determining the distribution and probability density functions of the interaction time, as well as taking into account multivariate transition trajectories, returns to previous states, and detection confirmation delays. It is shown that the GERT model effectively reproduces global temporal-probabilistic regularities of the process, but does not provide recursive correction of coordinates and velocities estimates in real time and minimization of the mean square error. To overcome these limitations, it is proposed to integrate the Kalman filter as a micro-level tool for trajectory estimation. A discrete linear stochastic model in the state space is formulated, a two-step recursion "prediction-correction", principles of selection of motion models (CV, CA, CT), structures of noise covariance matrices and tuning procedures based on RMSE and NEES criteria are presented. It is shown that the time characteristics obtained from GERT can be used to initialize the initial state and covariances, as well as for adaptive tuning of the sampling step and process noise parameters. The modeling results demonstrate the complementarity of the approaches: GERT forms a structural-probabilistic framework of the tracking process, while the Kalman filter provides smoothing of measurements, reduction of the spread of estimates and a significant reduction of RMSE compared to the macro-level estimate. The proposed integration framework "GERT → Kalman" creates a methodological basis for building a comprehensive model of goal tracking in a cluttered environment, combining the analysis of the temporal-probabilistic structure of the process with highly accurate recursive real-time state estimation.

Correcting of real-time radar rainfall bias using a Kalman filtering approach

We propose a refinement of TCP Westwood which allows management of the efficiency/friendliness-to-NewReno tradeoff. We show that the refined TCP Westwood is able to achieve higher efficiency yet at the same time maintain friendliness. TCP Westwood (TCPW) implements a novel window congestion control algorithm based on available bandwidth estimation. The performance of TCPW has been promising, exceeding that of TCP NewReno in high speed and/or wired/wireless networks. However, under certain circumstances, TCP NewReno may experience some performance degradation because TCPW possesses more information and thus can take better advantage of available bandwidth. We propose combining the original TCPW sampling strategy that produces available bandwidth estimates (BE), with a new strategy that produces rate estimates (RE). Our studies show that RE works when packet losses are mostly due to congestion. If on the other hand, the packet losses are mostly due to link errors, BE gives better performance. To achieve the best of all worlds, we introduce a method we call combined rate and bandwidth estimation (CRB). A connection first infers the predominant cause of packet losses, and then uses the most appropriate estimation method. We also introduce the efficiency/friendliness tradeoff graph that provides better tradeoff visualization. In our experiments, we found that CRB provides a better compromise between efficiency and friendliness, and the means to manage such a tradeoff.

HTTP-based adaptive streaming (HAS) has recently been widely deployed on the Internet. In the HAS system, a video content is encoded at multiple bitrates and the encoded video content is segmented into small parts of fixed durations. The HAS client requests a video segment and stores it in the playout buffer. The rate adaptation algorithm employed in HAS clients dynamically determines the video bitrate depending on the time-varying bandwidth. Many studies have shown that an efficient rate adaptation algorithm is critical to ensuring quality-of-experience in HAS systems. However, existing algorithms have problems estimating the network bandwidth because bandwidth estimation is performed on the client-side application stack. Without the help of transport layer protocols, it is difficult to achieve accurate bandwidth estimation due to the inherent segment-based transmission of the HAS. In this paper, we propose an alternative approach that utilizes the playout buffer occupancy rather than using bandwidth estimates obtained from the application layer. We start with a queueing analysis of the playout buffer. Then, we present a buffer-aware rate adaptation algorithm that is solely based on the mean buffer occupancy. Our simulation results show that compared to conventional algorithms, the proposed algorithm achieves very smooth video quality while delivering a similar average video bitrate.

Bandwidth information is critical to robust real-time multimedia transmission. However, existing bandwidth estimation (BWE) methods developed for the Internet or wireless networks are inadequate for power line networks due to the difference in media access control scheduling policies and channel noise characteristics. In this paper, we investigate the characteristics of in-home power line networks and present a real-time BWE scheme. By connecting different number of power line communication (PLC) modems, we find that the bandwidth is approximately inversely proportional to the number of PLC modems connected. Moreover, PLC modems have different characteristics of crossing different circuits under the same power meter and crossing different power meter under the same transformer. Based on the evaluation of in-home power line networks with multiple streaming, we propose an available bandwidth estimation scheme that estimates the cutoff rate from physical-layer line rate, data transmission rate, packet size, and packet loss rate. This scheme applies an iterative packet probing algorithm to estimate the available bandwidth. Experimental results show that the proposed scheme achieves good estimation accuracy.

The performance of a Kalman filter that estimates core power in a pressurized water reactor was evaluated using data from the Loss of Fluid Test (LOFT) Reactor and linear error analysis techniques. The Kalman filter provides a more accurate core power estimate in real time than does the conventional method of power estimation. Accuracies of better than 0.75% of rated power are obtained with the Kalman filter. If this accuracy improvement can be translated into higher levels of power operation, then more revenue can be generated by a commercial power plant. Scaling the LOFT 150-MW(thermal) l accuracy improvement up to a 1000-MW(electric) commercial power plant size, assuming that average power production is increased by the amount of the accuracy improvement, results in more than $800 000 of extra revenue per year ($0.06/kW·h).

Trade-off between accuracy, cost, and QoS using a beacon-on-demand strategy and Kalman filtering over a VANET

Real-time inverse estimation of multi-directional random waves from vessel-motion sensors using Kalman filter

We provide algorithms for inferring GPS (Global Positioning System) location and for quantifying the uncertainty of this estimate in real time. The algorithms are tested on GPS data from locations in the Southern Hemisphere at four significantly different latitudes. In order to rank the algorithms, we use the so-called log-score rule. The best algorithm uses an Ornstein–Uhlenbeck (OU) noise model and is built on an enhanced Kalman Filter (KF). The noise model is capable of capturing the observed autocorrelated process noise in the altitude, latitude and longitude recordings. This model outperforms a KF that assumes a Gaussian noise model, which under-reports the position uncertainties. We also found that the dilution-of-precision parameters, automatically reported by the GPS receiver at no additional cost, do not help significantly in the uncertainty quantification of the GPS positioning. A non-learning method using the actual position measurements and employing a constant uncertainty does not even converge to the correct position. Inference with the enhanced noise model is suitable for embedded computing and capable of achieving real-time position inference, can quantify uncertainty and be extended to incorporate complementary sensor recordings, e.g., from an accelerometer or from a magnetometer, in order to improve accuracy. The algorithm corresponding to the augmented-state unscented KF method suggests a computational cost of , where is the dimension of the augmented state-vector and is an adjustable, design-dependent parameter corresponding to the length of “past values” one wishes to keep for re-evaluation of the model from time to time. The provided algorithm assumes . Hence, the algorithm is likely to be suitable for sensor fusion applications.

Pose estimation in real time is a challenging research area with numerous applications. Obtaining detected key points in each pose presents us with useful applications, one such application being in the augmented reality domain. This paper presents an approach to 3D model overlay on an image with pose key point information. The model used in this work is based on the paper titled Multi-Person Pose Estimation. An FCN is employed for extracting key point information from the data. The method used in this work involves intersecting the image plane and the desired 3D model in real time and orienting the model correctly in accordance with the key point information. The performance will greatly depend on the GPU specifications.KeywordsAugmented realityPose estimation3D modelReal-time applications

End-to-end available bandwidth estimation is very important for bandwidth dependent applications, quality of service verification and traffic engineering. Although several techniques and tools have been developed in the past, producing reliable estimations in real-time still remains challenging -- it is necessary to ensure that the measurement process is accurate, non-intrusive and robust to non-deterministic delays or traffic bursts. This paper presents ASSOLO, a new active probing tool for estimating available bandwidth based on the concept of ``self-induced congestion''. ASSOLO features a new probing traffic profile called reach (Reflected ExponentiAl Chirp), which tests a wide range of rates being more accurate in the center of the probing interval. Moreover, the tool runs inside a real-time operating system and uses some de-noising techniques to improve the measurement process. Experimental results show that ASSOLO outperforms pathChirp, a state-of-the-art measurement tool, estimating available bandwidth with greater accuracy and stability.

Real-time assessment of flash flood impacts at pan-European scale: The ReAFFINE method

This paper considers a wireless network where multiple flows are delivering status updates about their respective information sources. An end-user aims to make accurate real-time estimations about the status of each information source using its received packets. As the accuracy of estimation is most impacted by events in the recent past, we propose to measure the Confidence-in-Estimation by the number of timely deliveries in a window of the recent past, and say that a flow suffers from a Loss-of-Confidence (LoC) if this number is insufficient for the end user to make a reliable estimation with small confidence intervals. We then study the problem of minimizing the system-wide LoC in wireless networks where each flow has a different requirement and link quality. We show that the problem of minimizing the system-wide LoC requires the control of the temporal variance of timely deliveries for each flow. This feature makes our problem significantly different from other optimization problems that only involve the average of control variables. Surprisingly, we show that there exists a simple online scheduling algorithm that is near-optimal. Simulation results show that our proposed algorithm is significantly better than other state-of-the-art policies. The practical value of this work is further evaluated by a case study of the real-time estimation problem of linear Gaussian processes, where we show that, under the optimal estimate algorithm, our scheduling policy results in better estimate accuracy, both in terms of the average mean square error and 95-percentile of mean square error, than other policies, including one that aims to optimize Age-of-Information, another performance metric for the application of real-time estimation.

This paper proposes an efficient real-time optimal estimation scheme that uses accurate but delayed measurements obtained periodically from high-performance sensing devices. For real-time optimal estimation, we employ two Kalman filters: one to conventionally estimate the current state and the other to precompute for the future state estimation to be carried out, when a new, accurate but delayed measurement arrives. The precomputing Kalman filter does the necessary computation in advance, for the future state estimation, from the available measurements for distributing the computational burden over time, thereby obtaining an optimal estimate in real time. By optimally incorporating accurate but delayed measurements, the optimality is preserved at all times, without imposing a heavy computational burden in a short sampling time interval. It is demonstrated through experiments that the proposed scheme can significantly improve the estimation performance with the least detriment to the real-time computation and memory size, when delayed and periodic measurements are available.