Convolutional Recurrent Predictor: Implicit Representation for Multi-Target Filtering and Tracking

Under space-based observation conditions, targets are subject to a large number of stars, clutter, false alarms, and other interferences, which can significantly impact the traditional Gaussian mixture probability hypothesis density (GM-PHD) filtering method, leading to tracking biases. To enhance the capability of the traditional GM-PHD method for multi-target tracking in space-based platform observation scenarios, in this article, we propose a GM-PHD algorithm based on spatio-temporal pipeline filtering and enhance the conventional spatio-temporal pipeline filtering method. The proposed algorithm incorporates two key enhancements: firstly, by adaptively adjusting the pipeline’s central position through target state prediction, it ensures continuous target tracking while eliminating noise; secondly, by computing trajectory similarity to distinguish stars from targets, it effectively mitigates stellar interference in target tracking. The proposed algorithm realizes a more accurate estimation of the target by constructing a target state pipeline using the time series and correlating multiple frames of data to achieve a smaller optimal sub-pattern assignment (OSPA) distance and a higher tracking accuracy compared with the traditional algorithm. Through simulations and real-world data validation, the algorithm showcased its capability for multi-target tracking in a space-based context, outperforming traditional methods and effectively addressing the challenge of stellar interference in space-based multi-target tracking.

In this paper, we consider the problem of tracking multiple targets in a passive multi-static radar system using Doppler-only measurements. The number of targets is assumed unknown and time-varying. The Doppler measurements are subject to additive noise, clutter, and missed detections. Doppler-only measurements from a single sensor provide incomplete information about the target state, commonly referred to as single-sensor unobservability. In a passive multi-static radar system, the availability of multiple bistatic links naturally lends itself to the fusion of measurements from spatially distributed sensors. However, data fusion emerges as a computationally intensive problem in multi-sensor multi-target tracking algorithms. We propose a two-step sequential approach to solve the underlying problem. We first cast the underlying problem as a group sparse problem in a discretized position-velocity space. A group sparsity based algorithm is applied to simultaneously exploit the multi-static Doppler frequency measurements to directly obtain the instantaneous target state estimates in the Cartesian coordinate system. These estimates are then fed as inputs to the linear Gaussian mixture probability hypothesis density (GMPHD) filter, which removes the false measurements, compensates for missed detections and reduces the localization error. The optimal sub-pattern assignment metric, which jointly comprises a weighted contribution of cardinality error and localization error, is used to evaluate the performance of the proposed method. Simulation results show that the proposed method successfully handles the multi-target tracking problem and outperforms the existing random receiver selection based multi-sensor implementation of the GMPHD filter.

Faults that are often invisible to existing inspection methods can significantly impact the reliability and efficiency of Photovoltaic (PV) systems. This study introduces an innovative framework for identifying PV panel faults using advanced machine learning algorithms and thermal images captured by unmanned aerial vehicles. The proposed Deep Recurrent Convolutional Neural Network (DRCNN) combines the strengths of Recurrent Neural Networks (RNNs) for learning temporal sequences and Convolutional Neural Networks (CNNs) for feature extraction. The DRCNN is designed to analyze thermal images of PV panels by capturing spatial and temporal patterns associated with fault-induced anomalies, such as hot spots, cracked cells or broken electrical connections. By leveraging CNN layers to extract critical features from thermal images and RNN layers to model the temporal dependencies and variations in thermal processes over time, the proposed approach integrates thermal image data collected under diverse environmental conditions.This dual strategy not only identifies faults based on spatial features but forecasts potential fault progression or the End-of-Life (EoL) for the panels by analyzing the evolution of thermal anomalies. The research aims to develop a highly accurate and efficient system for preventive maintenance and early problem detection, thereby improving the efficiency and durability of PV systems.The results demonstrate the DRCNN model's effectiveness in detecting various types of defects and delivering highly accurate real-time fault detection. By incorporating both spatial and temporal information, the proposed model significantly outperforms existing methods providing more robust and reliable defect identification.

DRCNNTLe: A deep recurrent convolutional neural network with transfer learning through pre-trained embeddings for automated ICD coding.

Text recognition in natural scene images is a challenging problem in computer vision. Different than the optical character recognition (OCR), text recognition in natural scene images is more complex due to variations in text size, colors, fonts, orientations, complex backgrounds, occlusion, illuminations and uneven lighting conditions. In this paper, we propose a segmentation-free method based on a deep convolutional recurrent neural network to solve the problem of cursive text recognition, particularly focusing on Urdu text in natural scenes. Compared to the non-cursive scripts, Urdu text recognition is more complex due to variations in the writing styles, several shapes of the same character, connected text, ligature overlapping, stretched, diagonal and condensed text. The proposed model gets a whole word image as an input without pre-segmenting into individual characters, and then transforms into the sequence of the relevant features. Our model is based on three components: a deep convolutional neural network (CNN) with shortcut connections to extract and encode the features, a recurrent neural network (RNN) to decode the convolutional features, and a connectionist temporal classification (CTC) to map the predicted sequences into the target labels. To increase the text recognition accuracy further, we explore deeper CNN architectures like VGG-16, VGG-19, ResNet-18 and ResNet-34 to extract more appropriate Urdu text features, and compare the recognition results. To conduct the experiments, a new large-scale benchmark dataset of cropped Urdu word images in natural scenes is developed. The experimental results show that the proposed deep CRNN network with shortcut connections outperform than other network architectures. The dataset is publicly available and can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://data.mendeley.com/datasets/k5fz57zd9z/1</uri> .

Underwater multi-targets tracking has always been a difficult problem in active sonar tracking systems. In order to estimate the parameters of time-varying multi-targets moving in underwater environments, based on the Bayesian filtering framework, the Random Finite Set (RFS) is introduced to multi-targets tracking, which not only avoids the problem of data association in multi-targets tracking, but also realizes the estimation of the target number and their states simultaneously. Usually, the conventional Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) algorithms assume that the detection probability is known as a priori, which is not suitable in many applications. Some methods have been proposed to estimate the detection probability, but most assume that it is constant both in time and surveillance region. In this paper, we model the detection probability through the active sonar equation. When fixed the false detection probability, we can get the analytic expression for the detection probability, which is related to target position. In addition, this novel detection probability is used in PHD and CPHD algorithms and applied to underwater active sonar tracking systems. Also, we use the adaptive ellipse gate strategy to reduce the computational load in PHD and CPHD algorithms. Under the linear Gaussian assumption, the proposed detection probability is illustrated in both Gaussian Mixture Probability Hypothesis Density (GM-PHD) and Gaussian Mixture Cardinalized Probability Hypothesis Density (GM-CPHD), respectively. Simulation results show that the proposed Pd-GM-PHD and Pd-GM-CPHD algorithms are more realistic and accuratein underwater active sonar tracking systems.

This paper describes the performance of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter for multiple human tracking in an intelligent vision system. Human movement trajectories were observed with a camera and tracked by the GM-PHD filter. The filter multi-target tracking ability was validated by two random motion trajectories in the paper. To evaluate the filter performance in relation to the target movement, the motion velocity and angular velocity as key evaluation factors were proposed. A circular motion model was implemented for simplified analysis of the filter tracking performance. The results indicate that the mean absolute error defined as the difference between the filter prediction and the ground truth is proportional to the motion speed and angular velocity of the target. The error is only slightly affected by the tracking targets' number.

The frequency of armed conflicts increased during the last 20 years. The problems of the emergence of military disputes, not only concern social parameters, but also economic and financial dimensions. This study examines the potential impact of global geopolitical events on the stock market prices of the Dow Jones U.S. Aerospace & Defense Index and Foreign Exchange (FOREX) markets movements. We analyse whether defence stocks and exchange rate perform similarly during military incidents or geopolitical crises. We built an Autoregressive Moving Average Model with a Generalized Autoregressive Conditional Heteroskedasticity process (ARMA-GARCH) with the machine learning methods of Neural Networks, Deep Recurrent Convolutional Neural Networks, Deep Neural Decision Trees, Quantum Neural Networks, and Quantum Recurrent Neural Networks, aimed at detecting intraday patterns for forecasting defence stock market and FOREX markets disturbances in a market microstructure framework. The empirical results provide preliminary findings on the foreseeability of market disturbances and small differences are observed before and during geopolitical events. Additionally, we confirm the effectiveness of the hybrid model ARMA-GARCH with the machine learning approaches, being ARMA-GARCH-Quantum Recurrent Neural Network the technique that achieves the best accuracy results. Our work has a large potential impact on investment market agents and portfolio managers, as shocks from geopolitical events could provide a new methodology to support the decision-making process for trading in High-Frequency Trading.

In the context of 3D geometric reconstruction from sequential inverse synthetic aperture radar (ISAR) images, the accurate scatterer trajectory association is a critical step. Aiming at the above problem, an enhanced scatterer trajectory association method is proposed by designing a modified label Gaussian mixture probability hypothesis density (ML-GM-PHD) filtering algorithm. The algorithm commences by constructing a general motion model for scatterers across sequential ISAR images, followed by an in-depth analysis of their motion characteristics. Subsequently, the actual projected positions and measurements of the scattering centers of the observed target are treated as random finite sets, which allows us to reformulate the scatterer trajectory association into a maximum a posteriori (MAP) estimation problem. After that, a ML-GM-PHD filtering algorithm is proposed to realize the scatterer trajectory association. Furthermore, the proposed method is applied to ISAR images in both the forward and reverse directions, enabling the fusion of trajectories from opposing directions to bolster the completeness of the scatterer trajectories. Finally, the factorization method is performed on the scatterer trajectory matrix to implement the 3D geometry reconstruction of the scattering centers in the observed target. Experimental results based on random points and electromagnetic data verify the effectiveness and performance priority of the proposed algorithm.

In this paper, a novel moving target detection and tracking approach is presented based on Gaussian mixture probability hypothesis density (GMPHD) filter in synthetic aperture radar (SAR). Based on the advantages of GMPHD filter and the characteristics of moving target in SAR imagery, GMPHD filter is employed on potential moving target candidates extracted from a sequence of temporal and spatial sub-aperture SAR images to detect and track the moving targets in heavy clutter environment. Utilizing the tracking algorithm, the states and the number of moving targets are obtained over time. Simultaneously, the strong ground stationary clutter lacking of the dynamic behavior is eliminated finally. Both simulation and real Gotcha data set processing results are provided to demonstrate the effectiveness of the proposed approach.

A driver stress detection model via data augmentation based on deep convolutional recurrent neural network

&amp;lt;p&amp;gt;The amount of wind farms and wind power production in Europe, both on- and off-shore, increased rapidly in the past years. To ensure grid stability, on-time (re)scheduling of maintenance tasks and mitigate fees in energy trading, accurate predictions of wind speed and wind power are needed. It has become particularly important to improve wind speed predictions in the short range of one to six hours as wind speed variability in this range has been found to pose the largest operational challenges. Furthermore, accurate predictions of extreme wind events are of high importance to wind farm operators as timely knowledge of these can both prevent damages and offer economic preparedness. We propose in this work a deep convolutional recurrent neural network (RNN) based regression model for the spatio-temporal prediction of extreme wind speed events over Europe in the short-to-medium range (12 hour lead-time in 1 hour intervals). This is achieved by training a multi-layered convolutional long short-term memory (ConvLSTM) network with imbalanced regression loss, to which end we investigate three different loss functions: the inversely weighted mean absolute error (W-MAE) loss, the inversely weighted mean squared error (W-MSE) loss and the squared error-relevance area (SERA) loss.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The results indicate superior performance of the SERA loss, showing significant improvements on high intensity extreme events. The W-MAE and W-MSE shows no improvements over the standard MSE loss and we thus discourage the usage of the inverse weighting method. We conclude that the SERA loss provides an effective way to adapt deep learning to the task of imbalanced spatio-temporal regression and&amp;amp;#160;its application to the forecasting of extreme wind events in the short-to-medium range.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;This work was performed as a part of the MEDEA project, which is funded by the Austrian Climate Research Program to further research on renewable energy and meteorologically induced extreme events.&amp;lt;/p&amp;gt;

Multi-target tracking in distributed sensor networks using particle PHD filters

Multi-target tracking is a difficult task in radar data processing field. To track multiple targets with the video data collected by a X-band surface surveillance radar, a two-level cascade detection algorithm is designed at first. We combine the motion model and appearance model of the targets together for multi-target tracking and propose a multi-target tracking algorithm based on scale adaptive kernelized correlation filter (SAKCF) and Kalman filter (KF) in this paper. Firstly, the extended targets are detected by the designed tow-level cascade detection algorithm and the centroids of the extended targets are extracted by centroid algorithm too. Then, a SAKCF tracker is assigned to each target. KF is utilized to predict the position of each target and each SAKCF tracker searches the region near the predicted position to determine the real position and scale of each target. The association between tracks and trace points is performed based on Hungary algorithm. Finally, the abnormal tracks are deleted. Experimental results based on the radar video data show that compared with the classical simple online real-time target tracking (SORT) algorithm and joint probabilistic data association (JPDA) algorithm, the proposed algorithm improves multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA).

The amount of wind farms and wind power production in Europe, both on- and off-shore, has increased rapidly in the past years. To ensure grid stability, on-time (re)scheduling of maintenance tasks and mitigate fees in energy trading, accurate predictions of wind speed and wind power are needed. It has become particularly important to improve wind speed predictions in the short range of one to six hours as wind speed variability in this range has been found to pose the largest operational challenges. Furthermore, accurate predictions of extreme wind events are of high importance to wind farm operators as timely knowledge of these can both prevent damages and offer economic preparedness. In this work we propose a deep convolutional recurrent neural network (RNN) based regression model, for the spatio-temporal prediction of extreme wind speed events over Europe in the short-to-medium range (12 hour lead-time in one hour intervals). This is achieved by training a multi-layered convolutional long short-term memory (ConvLSTM) network with so-called imbalanced regression loss. To this end we investigate three different loss functions: the inversely weighted mean absolute error (W-MAE) loss, the inversely weighted mean squared error (W-MSE) loss and the squared error-relevance area (SERA) loss. We investigate forecast performance for various high-threshold extreme events and for various numbers of network layers, and compare the imbalanced regression loss functions to the commonly used mean squared error (MSE) and mean absolute error (MAE) loss. The results indicate superior performance of an ensemble of networks trained with either W-MAE, W-MSE or SERA loss, showing substantial improvements on high intensity extreme events. We conclude that the ConvLSTM trained with imbalanced regression loss provides an effective way to adapt deep learning to the task of imbalanced spatio-temporal regression and its application to the forecasting of extreme wind events in the short-to-medium range, and may be best utilised as an ensemble. This work was performed as a part of the MEDEA project, which is funded by the Austrian Climate Research Program to further research on renewable energy and meteorologically induced extreme events.