This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the clas...This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators. This is the extension of the previous one from the two category cases to the multi-category case. The novel closed-form expressions for the Bayes classification probability and actual correct classification rate associated with PBCR are derived. These correct classification rates are suggested as performance measures for the classifications procedure. An empirical study has been carried out to analyze the dependence of derived classification rates on category parameters.展开更多
Standard automatic dependent surveillance broadcast (ADS-B) reception algorithms offer considerable performance at high signal-to-noise ratios (SNRs). However, the performance of ADS-B algorithms in applications can b...Standard automatic dependent surveillance broadcast (ADS-B) reception algorithms offer considerable performance at high signal-to-noise ratios (SNRs). However, the performance of ADS-B algorithms in applications can be problematic at low SNRs and in high interference situations, as detecting and decoding techniques may not perform correctly in such circumstances. In addition, conventional error correction algorithms have limitations in their ability to correct errors in ADS-B messages, as the bit and confidence values may be declared inaccurately in the event of low SNRs and high interference. The principal goal of this paper is to deploy a Long Short-Term Memory (LSTM) recurrent neural network model for error correction in conjunction with a conventional algorithm. The data of various flights are collected and cleaned in an initial stage. The clean data is divided randomly into training and test sets. Next, the LSTM model is trained based on the training dataset, and then the model is evaluated based on the test dataset. The proposed model not only improves the ADS-B In packet error correction rate (PECR), but it also enhances the ADS-B In terms of sensitivity. The performance evaluation results reveal that the proposed scheme is achievable and efficient for the avionics industry. It is worth noting that the proposed algorithm is not dependent on conventional algorithms’ prerequisites.展开更多
由于跟踪过程目标不规则形变的影响,采用固定纵横比的尺度模型无法精确地估计目标的尺度.为解决该问题,本文提出基于纵横比自适应的相关滤波跟踪算法.基于fDSST(fast Discriminative Scale Space Tracking)算法,训练学习纵横比模型,更...由于跟踪过程目标不规则形变的影响,采用固定纵横比的尺度模型无法精确地估计目标的尺度.为解决该问题,本文提出基于纵横比自适应的相关滤波跟踪算法.基于fDSST(fast Discriminative Scale Space Tracking)算法,训练学习纵横比模型,更新目标的纵横比,获取更精确的目标尺度.在此基础上,本文设计了平滑修正方案以及学习率自适应机制,可以有效地缓解因目标出现遮挡导致的模型漂移问题.在OTB100、VOT2016和VOT2018数据集上与其他跟踪算法进行对比实验,结果表明本文算法改善了基准算法的性能,特别是在OTB100上的总体准确率和成功率比fDSST提高了9.6%和6.2%.展开更多
文摘This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators. This is the extension of the previous one from the two category cases to the multi-category case. The novel closed-form expressions for the Bayes classification probability and actual correct classification rate associated with PBCR are derived. These correct classification rates are suggested as performance measures for the classifications procedure. An empirical study has been carried out to analyze the dependence of derived classification rates on category parameters.
文摘Standard automatic dependent surveillance broadcast (ADS-B) reception algorithms offer considerable performance at high signal-to-noise ratios (SNRs). However, the performance of ADS-B algorithms in applications can be problematic at low SNRs and in high interference situations, as detecting and decoding techniques may not perform correctly in such circumstances. In addition, conventional error correction algorithms have limitations in their ability to correct errors in ADS-B messages, as the bit and confidence values may be declared inaccurately in the event of low SNRs and high interference. The principal goal of this paper is to deploy a Long Short-Term Memory (LSTM) recurrent neural network model for error correction in conjunction with a conventional algorithm. The data of various flights are collected and cleaned in an initial stage. The clean data is divided randomly into training and test sets. Next, the LSTM model is trained based on the training dataset, and then the model is evaluated based on the test dataset. The proposed model not only improves the ADS-B In packet error correction rate (PECR), but it also enhances the ADS-B In terms of sensitivity. The performance evaluation results reveal that the proposed scheme is achievable and efficient for the avionics industry. It is worth noting that the proposed algorithm is not dependent on conventional algorithms’ prerequisites.
文摘由于跟踪过程目标不规则形变的影响,采用固定纵横比的尺度模型无法精确地估计目标的尺度.为解决该问题,本文提出基于纵横比自适应的相关滤波跟踪算法.基于fDSST(fast Discriminative Scale Space Tracking)算法,训练学习纵横比模型,更新目标的纵横比,获取更精确的目标尺度.在此基础上,本文设计了平滑修正方案以及学习率自适应机制,可以有效地缓解因目标出现遮挡导致的模型漂移问题.在OTB100、VOT2016和VOT2018数据集上与其他跟踪算法进行对比实验,结果表明本文算法改善了基准算法的性能,特别是在OTB100上的总体准确率和成功率比fDSST提高了9.6%和6.2%.
