Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems

Cancellation of Towing Ship Interference in Passive SONAR in a Shallow Ocean Environment

Towed array sonars are preferred for detecting stealthy underwater targets that emit faint acoustic signals in the ocean, especially in shallow waters. However, the towing ship being near to the array behaves as a loud target, introducing additional interfering signals to the array, severely affecting the detection and classification of potential targets. Canceling this underlying interference signal is a challenging task and is investigated in this paper for a shallow ocean operational scenario where the problem is more critical due to the multipath phenomenon. A method exploiting the eigenvector analysis of spatio-temporal covariance matrix based on space time adaptive processing is proposed for suppressing tow ship interference and thus improving target detection. The developed algorithm learns the interference patterns in the presence of target signals to mitigate the interference across azimuth and to remove the spectral leakage of own-ship. The algorithm is statistically analyzed through a set of relevant metrics and is tested on simulated data that are equivalent to the data received by a towed linear array of acoustic sensors in a shallow ocean. The results indicate a reduction of 20-25dB in the tow ship interference power while the detection of long-range low SNR targets remain largely unaffected with minimal power-loss. In addition, it is demonstrated that the spectral leakage of tow ship, on multiple beams across the azimuth, due to multipath, is also alleviated leading to superior classification capabilities. The robustness of the proposed algorithm is validated by the open ocean experiment in the coastal shallow region of the Arabian Sea at Off-Kochi area of India, which produced results in close agreement with the simulations. A comparison of the simulation and experimental results with the existing PCI and ECA methods is also carried out, suggesting the proposed method is quite effective in suppressing the tow ship interference and is immensely beneficial for the detection and classification of long-range targets

Numerical simulation of flooding from multiple sources using adaptive anisotropic unstructured meshes and machine learning methods

Over the past few decades, urban floods have been gaining more attention due to their increase in frequency. To provide reliable flooding predictions in urban areas, various numerical models have been developed to perform high-resolution flood simulations. However, the use of high-resolution meshes across the whole computational domain causes a high computational burden. In this thesis, a 2D control-volume and finite-element (DCV-FEM) flood model using adaptive unstructured mesh technology has been developed. This adaptive unstructured mesh technique enables meshes to be adapted optimally in time and space in response to the evolving flow features, thus providing sufficient mesh resolution where and when it is required. It has the advantage of capturing the details of local flows and wetting and drying front while reducing the computational cost. Complex topographic features are represented accurately during the flooding process. This adaptive unstructured mesh technique can dynamically modify (both, coarsening and refining the mesh) and adapt the mesh to achieve a desired precision, thus better capturing transient and complex flow dynamics as the flow evolves. A flooding event that happened in 2002 in Glasgow, Scotland, United Kingdom has been simulated to demonstrate the capability of the adaptive unstructured mesh flooding model. The simulations have been performed using both fixed and adaptive unstructured meshes, and then results have been compared with those published 2D and 3D results. The presented method shows that the 2D adaptive mesh model provides accurate results while having a low computational cost. The above adaptive mesh flooding model (named as Floodity) has been further developed by introducing (1) an anisotropic dynamic mesh optimization technique (anisotropic-DMO); (2) multiple flooding sources (extreme rainfall and sea-level events); and (3) a unique combination of anisotropic-DMO and high-resolution Digital Terrain Model (DTM) data. It has been applied to a densely urbanized area within Greve, Denmark. Results from MIKE 21 FM are utilized to validate our model. To assess uncertainties in model predictions, sensitivity of flooding results to extreme sea levels, rainfall and mesh resolution has been undertaken. The use of anisotropic-DMO enables us to capture high resolution topographic features (buildings, rivers and streets) only where and when is needed, thus providing improved accurate flooding prediction while reducing the computational cost. It also allows us to better capture the evolving flow features (wetting-drying fronts). To provide real-time spatio-temporal flood predictions, an integrated long short-term memory (LSTM) and reduced order model (ROM) framework has been developed. This integrated LSTM-ROM has the capability of representing the spatio-temporal distribution of floods since it takes advantage of both ROM and LSTM. To reduce the dimensional size of large spatial datasets in LSTM, the proper orthogonal decomposition (POD) and singular value decomposition (SVD) approaches are introduced. The performance of the LSTM-ROM developed here has been evaluated using Okushiri tsunami as test cases. The results obtained from the LSTM-ROM have been compared with those from the full model (Fluidity). Promising results indicate that the use of LSTM-ROM can provide the flood prediction in seconds, enabling us to provide real-time flood prediction and inform the public in a timely manner, reducing injuries and fatalities. Additionally, data-driven optimal sensing for reconstruction (DOSR) and data assimilation (DA) have been further introduced to LSTM-ROM. This linkage between modelling and experimental data/observations allows us to minimize model errors and determine uncertainties, thus improving the accuracy of modelling. It should be noting that after we introduced the DA approach, the prediction errors are significantly reduced at time levels when an assimilation procedure is conducted, which illustrates the ability of DOSR-LSTM-DA to significantly improve the model performance. By using DOSR-LSTM-DA, the predictive horizon can be extended by 3 times of the initial horizon. More importantly, the online CPU cost of using DOSR-LSTM-DA is only 1/3 of the cost required by running the full model.Open Acces

Novel Approaches for Structural Health Monitoring

The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field

Adapting pedestrian detectors to new domains: A comprehensive review.

Successful detection and localisation of pedestrians is an important goal in computer vision which is a core area in Artificial Intelligence. State-of-the-art pedestrian detectors proposed in literature have reached impressive performance on certain datasets. However, it has been pointed out that these detectors tend not to perform very well when applied to specific scenes that differ from the training datasets in some ways. Due to this, domain adaptation approaches have recently become popular in order to adapt existing detectors to new domains to improve the performance in those domains. There is a real need to review and analyse critically the state-of-the-art domain adaptation algorithms, especially in the area of object and pedestrian detection. In this paper, we survey the most relevant and important state-of-the-art results for domain adaptation for image and video data, with a particular focus on pedestrian detection. Related areas to domain adaptation are also included in our review and we make observations and draw conclusions from the representative papers and give practical recommendations on which methods should be preferred in different situations that practitioners may encounter in real-life

A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns

Monitoring of modal frequencies under an unsupervised learning framework is a practical strategy for damage assessment of civil structures, especially bridges. However, the key challenge is related to high sensitivity of modal frequencies to environmental and/or operational changes that may lead to economic and safety losses. The other challenge pertains to different environmental and/or operational variation patterns in modal frequencies due to differences in structural types, materials, and applications, measurement periods in terms of short and/or long monitoring programs, geographical locations of structures, weather conditions, and influences of single or multiple environmental and/or operational factors, which may cause barriers to employing stateof-the-art unsupervised learning approaches. To cope with these issues, this paper proposes a novel double-hybrid learning technique in an unsupervised manner. It contains two stages of data partitioning and anomaly detection, both of which comprise two hybrid algorithms. For the first stage, an improved hybrid clustering method based on a coupling of shared nearest neighbor searching and density peaks clustering is proposed to prepare local information for anomaly detection with the focus on mitigating environmental and/or operational effects. For the second stage, this paper proposes an innovative non-parametric hybrid anomaly detector based on local outlier factor. In both stages, the number of nearest neighbors is the key hyperparameter that is automatically determined by leveraging a self-adaptive neighbor searching algorithm. Modal frequencies of two full-scale bridges are utilized to validate the proposed technique with several comparisons. Results indicate that this technique is able to successfully eliminate different environmental and/or operational variations and correctly detect damage

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean