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Novel auxiliary truncated unscented Kalman filtering (ATUKF) is proposed for bearings-only maneuvering target tracking in this paper. In the proposed algorithm, to deal with arbitrary changes in motion models, a modified prior probability density function (PDF) is derived based on some auxiliary target characteristics and current measurements. Then, the modified prior PDF is approximated as a Gaussian density by using the statistical linear regression (SLR) to estimate the mean and covariance. In order to track bearings-only maneuvering target, the posterior PDF is jointly estimated based on the prior probability density function and the modified prior probability density function, and a practical algorithm is developed. Finally, compared with other nonlinear filtering approaches, the experimental results of the proposed algorithm show a significant improvement for both the univariate nonstationary growth model (UNGM) case and bearings-only target tracking case. [ABSTRACT FROM AUTHOR]

In this paper, a novel bearings-only maneuvering target tracking algorithm based on truncated quadrature Kalman filtering (TQKF) is proposed. In the proposed method, when the target maneuvers, in order to reduce the effect on performance duo to the increasing variance of the prior distribution, a modified prior distribution based on the current measurement is proposed. In the update step, the first two moments of the modified prior distribution is approximately estimated based on the least square estimation method and Gauss–Hermite quadrature rule, and the posterior distribution is jointly updated by using the prior distribution and the modified prior distribution. Moreover, in order to adaptively choose the estimated results obtained by the prior PDF and the truncated prior PDF, a fuzzy logic approach in which a Gaussian membership function is employed is proposed to determine the weight α . Finally, the experiment results show that the proposed algorithm results in more accurate tracking than the existing one, namely, the unscented Kalman filter (UKF), the quadrature Kalman filter (QKF), interact multiple model extended Kalman filter (IMMEKF) and multiple model Rao–Blackwellized particle filter (MMRBPF). [ABSTRACT FROM AUTHOR]

A new maneuvering-deceleration guidance algorithm based on atmosphere estimation is presented for the precise guidance and velocity control of hypersonic reentry vehicle. The maneuvering-deceleration guidance command is composed of optimal guidance command and deceleration command. The optimal guidance command is obtained by guidance law with position and impact angle constraints. Sinusoid maneuvering-deceleration command is used to minimize the position disturbance caused by deceleration. And the parameters of maneuvering command are produced from mismatch between terminal predicted velocity and nominal-velocity by numerical integration method. The atmosphere parameter identification technology is also used to improve the prediction precision. The simulation results show that the maneuvering-deceleration guidance algorithm has strong robustness against uncertain conditions; it can accomplish the integrated design of precise guidance and deceleration control efficiently. [ABSTRACT FROM AUTHOR]

Object detection quality and network lifetime are two conflicting aspects of a sensor network, but both are critical to many sensor applications such as military surveillance. Partial coverage, where a sensing field is partially sensed by active sensors at any time, is an appropriate approach to balancing the two conflicting design requirements of monitoring applications. Under partial coverage, we develop an analytical framework for object detection in sensor networks, and mathematically analyze average-case object detection quality in random and synchronized sensing scheduling protocols. Our analytical framework facilitates performance evaluation of a sensing schedule, network deployment, and sensing scheduling protocol design. Furthermore, we propose three wave sensing scheduling protocols to achieve bounded worst-case object detection quality. We justify the correctness of our analyses through rigorous proof, and validate the effectiveness of the proposed protocols through extensive simulation experiments. [ABSTRACT FROM AUTHOR]

In the era of mobile internet, Location Based Services (LBS) have developed dramatically. Seamless Indoor and Outdoor Navigation and Localization (SNAL) has attracted a lot of attention. No single positioning technology was capable of meeting the various positioning requirements in different environments. Selecting different positioning techniques for different environments is an alternative method. Detecting the users' current environment is crucial for this technique. In this paper, we proposed to detect the indoor/outdoor environment automatically without high energy consumption. The basic idea was simple: we applied a machine learning algorithm to classify the neighboring Global System for Mobile (GSM) communication cellular base station's signal strength in different environments, and identified the users' current context by signal pattern recognition. We tested the algorithm in four different environments. The results showed that the proposed algorithm was capable of identifying open outdoors, semi-outdoors, light indoors and deep indoors environments with 100% accuracy using the signal strength of four nearby GSM stations. The required hardware and signal are widely available in our daily lives, implying its high compatibility and availability. [ABSTRACT FROM AUTHOR]

Wind farms can affect the power quality (PQ) of the power supply grid, with subsequent impacts on the safe and stable operation of other electrical equipment. A novel PQ prediction, early warning, and control approach for the common coupling points between wind farms and the network is proposed in this paper. We then quantify PQ problems and provide rational support measures. To obtain predicted PQ data, we first establish a trend analysis model. The model incorporates a distance-based cluster analysis, probability distribution analysis based on polynomial fitting, pattern matching based on similarity, and Monte Carlo random sampling. A data mining algorithm then uses the PQ early warning flow to analyze limit-exceeding and abnormal data, quantify their severity, and output early warning prompts. Finally, PQ decision support is applied to inform both the power suppliers and users of anomalous changes in PQ, and advise on corresponding countermeasures to reduce relevant losses. Case studies show that the proposed approach is effective and feasible, and it has now been applied to an actual PQ monitoring platform. [ABSTRACT FROM AUTHOR]