This PDF file contains the front matter associated with SPIE Proceedings Volume 10643, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

Verifying that the behavior of an autonomous systems is safe is fundamental for safety-critical properties like preventing crashes in autonomous vehicles. Unfortunately, exhaustive verification techniques fail to scale to the size of real-life systems. Moreover, these systems frequently use algorithms whose runtime behavior cannot be determined at design time (e.g., machine learning algorithms). This presents another problem given that these algorithms cannot be verified at design time. Fortunately, a technique known as runtime assurance can be used for these cases. The strategy that runtime assurance uses to verify a system is to add small components (known as enforcers) to the system that monitor its output and evaluate whether the output is safe or not. If the output is safe, then the enforcer lets it pass; if the output is unsafe, the enforcer replaces it with a safe output. For instance, in a drone system that must be restricted to fly within a constrained area (a.k.a. geo-fence) an enforcer can be used to monitor the movement commands to the drone. Then, if a movement command keeps the drone within the geo-fence, the enforcer lets it pass, but if the command takes the drone outside of this area, the enforcer replaces it with a safe command (e.g., hovering). Given that enforcers are small components fully specified at design time, it is possible to use exhaustive verification techniques to prove that they can keep the behavior of the whole system safe (e.g., the drone flying within the geo-fence) even if the system contains unverified code.

Internet of Things (IoT), an emerging network of physical objects, acts as catalyst for the future connected world. It is estimated that there will be around 50 billion connected objects by year 2020. An IoT enabled connected world improves the way human live and interact with surroundings. Through IoT valuable information and services are available to humans on demand and in real time. But these information and services may also cause harm at certain level if not thoroughly observed. With the advent of IoT, the future of the connected world will face new types of security threats since more than half of the total connected objects today are exposed to such threats and vulnerability and this number may increase as more devices are getting connected to internet. Security is the major concern in designing IoT systems since the data collected by IoT objects may be critical and also data transmitted and processed by overall IoT system may be sensitive and may lead to issues with safety, privacy, authorization and authenticity etc. Therefore while taking advantage of IoT we must also consider the ways, to the highest possible degree, to prevent the future IoT connected world from harming us. Cyber security in IoT deals with protecting connected objects for data authorization, authentication, tempering and losses as well as identifying potential risks to the system. This paper provides a brief review on how to adopt security practices in designing IoT systems to make them secure and safe.

A distributed environment, such as with IoT, drastically increases the overall cyber-attack surface. This heightens the need to maintain the highest level of trust for each system device. The goal is to provide and maintain a trusted embedded computing system while minimizing performance impact. Alion has developed a platform that allows for the development of cyber-resilience technologies. The platform core is a heterogeneous system-on-chip that includes multiple processors, programmable logic, and memory. Such a system-on-chip allows for hardware-based resilience technologies that extend or enhance traditional software techniques. Trusting the platform begins with trusting the boot environment. Secure boot using the physically unclonable function supports confidentiality, integrity, and authentication of boot partitions. After trusted boot, separation and introspection maintain that trust. Hardware sandboxes ensure that applications operate in separate hardware containers. This not only eliminates information leakage between applications but also provides a means to isolate rogue IP introduced through an untrusted third party. A combination of hardware sandboxes and reference monitors provides hardware-based memory management. Hardware-accelerated cryptography and dynamic key management limit the ability of snooping or co-opting external communications or external memory. Dynamic introspection of system components detects anomalous behavior on-the-fly, including comparing program memory against a golden image and physically monitoring buses. Should the system detect anomalous behavior, secure recovery and reprovisioning forces the system back to a trusted state. These technologies can be applied to other systems and IC designs, used in whole or in part to balance the level of trust necessary and other system constraints.

With tens of billions of devices slated for deployment as devices in the so-called Internet of Things (IoT), there will be a significant cybersecurity component. Confidentiality, integrity and availability comprise the CIA of cybersecurity and are the major focus of INFOSEC specialists. In this paper, we will outline the use of digital certificates as a means of providing availability/authentication services in the IoT while providing for secure object code update services.

Imaging sensors capturing the surroundings of an autonomous vehicle are vital for its understanding of the environment. While thermal infrared cameras promise improved bad weather and nighttime robustness compared with standard RGB cameras, detecting objects, such as persons, in thermal infrared imagery is a tough problem because image resolution and quality is typically far lower, especially for low-cost sensors. Currently, deep learning based object detection frameworks offer an impressive performance on high-quality images. However, applying them to low-quality data in a different spectral range causes significant performance drops. This work proposes a strategy to make use of elaborate CNN-based object detector frameworks which are pre-trained on visual RGB images. Two key steps are undertaken: First, an appropriate preprocessing strategy for the IR data is suggested which transforms the IR data as close as possible to the RGB domain. This allows pre-trained RGB features to be effective on the novel domain. Second, the remaining domain gap is addressed by fine-tuning the pre-trained CNN on a limited set of thermal IR data. Different IR preprocessing options are explored, each addressing a different aspect of the domain gap between thermal IR and RGB data. Examples include dynamic range, blur or contrast. Because no preprocessing can cover all aspects alone, providing preprocessing combinations to the CNN allows addressing more than one aspect at once and further improves the results. Experiments indicate significant person detection improvements on the public KAIST dataset with the optimized preprocessing strategy.

We have evaluated a HDR (high dynamic range) CMOS image sensor with logarithmic response for an image-based navigation system which allows vehicles to autonomously drive on reference trajectories. The goal of the evaluation is to investigate how the advantages (constant image intensities under varying illumination conditions) and the disadvantages (higher noise and artifact levels) of the sensor affect the performance of image-based navigation. We recorded HDR image sequences using a vehicle to evaluate the performance of intra-frame image analysis which is used to estimate the vehicle motion and the respective trajectories, and the performance of inter-frame image analysis which is used for the alignment of a current trajectory to a reference trajectory. We have also performed a comparison with simultaneously recorded LDR image data on itineraries with scenes with high dynamic range.

Unmanned aerial vehicles (UAVs) equipped with cameras are a valuable tool for surveillance, reconnaissance, and protection of civilians, soldiers, and real estates. Multicopters or fixed wing UAVs patrol while an automatic video change detection localizes relevant or suspicious changes in the scene between two patrols. In this way, e.g., a convoy can be protected from improvised explosive devices (IEDs) by early detecting deployment traces like excavations, skid marks, footprints, left-behind tooling equipment, and marker stones. Furthermore, in case of disasters imminent danger can be recognized quickly. Therefore, an appropriate video change detection algorithm was realized recently as a solution. Since then, two main improvements could be realized which are described in this paper. First, a novel measurement for image differences in color space is introduced that increases the detection sensitivity. Furthermore, a solution is presented to eliminate or reduce detections of cast shadows in situations where the sun intensity and/or position is slightly or strongly different in the two compared patrols. In order to do this, the impact of cast shadows is examined in Lab and LCh color space to build up a dedicated shadow model with which shadows can be filtered out. This shadow model covers the relation between image intensity reduction, color shift towards blue, and image noise influences of cast shadows. The given results document the performance of the presented approach in different situations.

The ability for sensing platforms to collect data intermittently in various settings has been explored extensively. However, many existing solutions are not intelligent and cannot be implemented in real-time. This paper addresses the need for a near real-time, low-cost intelligent autonomous unattended sensors (AAUS) integrating an interchangeable a mobile radiation sensor, with the ability to transmit actionable information to a base station. We address this through discussion of current technologies, our implementations, and experiments as well as a complete pipeline for future frameworks. Our method continuously listens for specific frequencies with the ability measure radiation counts, implements onboard audio classification via machine learning methods, and transmits the results requested. This technique utilizes existing hardware for data management and machine learning algorithms for classification, such as an inexpensive single board computer, a Artificial Neural Network (ANN) and a bgeigie Nano radiation sensor. Our approach performs a real-time Fast Fourier Transform (FFT) continuously in an environment and calculates whether the frequency is within the range of interest. If correct, the sound is recorded, and a pre-trained ANN, fine-tuned on specific data will classify the recorded sound. Depending on the requested information the node will either transmit radiation counts or the classification of the audio input. However, the transmission of audio will only occur if the degree of certainty is above a threshold value. The onboard shallow ANN implentation in this paper experiences an overall classification of 64%.

The Sensor Analysis and Intelligence Laboratory (SAIL) at Mississippi State University's (MSU's) Center for Advanced Vehicular Systems (CAVS) and the Social, Therapeutic and Robotic Systems Lab (STaRS) at MSU's Computer Science and Engineering department have designed and implemented a modular platform for automated sensor data collection and processing, named the Hydra. The Hydra is an open-source system (all artifacts and code are published to the research community), and it consists of a modular rigid mounting platform (sensors, processors, power supply and conditioning) that utilize the Picatinny rail (a standardized mounting system originally developed for firearms) as a rigid mounting system, a software platform utilizing the Robotic Operating System (ROS) for data collection, and design packages (schematics, CAD drawings, etc.). The Hydra system streamlines the assembly of a configurable multi-sensor system. This system is motivated to enable researchers to quickly select sensors, assemble them as an integrated system, and collect data (without having to recreate the Hydras hardware and software). Prototype results are presented from a recent data collection on a small robot during a SWAT-robot training.

R-DEX Systems is developing a low-cost 3D security camera for residential, commercial, and military applications. This innovative system reduces false alarms, classifies detected events, provides actionable information to the end user, and combines video monitoring and perimeter surveillance in a low-cost package.

Autonomous unmanned ground vehicles (UGVs) are beginning to play a more critical role in military operations. As the size of the fighting forces continues to draw down, the U.S. and coalition partner Armed Forces will become increasingly reliant on UGVs to perform mission-critical roles. These roles range from squad-level manned-unmanned teaming to large-scale autonomous convoy operations. However, as more UGVs with increasing levels of autonomy are entering the field, tools for accurately predicting these UGVs performance and capabilities are lacking. In particular, the mobility of autonomous UGVs is a largely unsolved problem. While legacy tools for predicting ground vehicle mobility are available for both assessing performance and planning operations, in particular the NATO Reference Mobility Model, no such toolset exists for autonomous UGVs. Once autonomy comes into play, ground vehicle mechanical-mobility is no longer enough to characterize vehicle mobility performance. Not only will vehicle-terrain interactions and driver concerns impact mobility, but sensor-environment interactions will also affect mobility. UGV mobility will depend in a large part on the sensor data available to drive the UGVs autonomy algorithms. A limited amount of research has been focused on the concept of perception-based mobility to date. To that end, the presented work will provide a review of the tools and methods developed thus far for modeling, simulating, and assessing autonomous mobility for UGVs. This review will highlight both the modifications being made to current mobility modeling tools and new tools in development specifically for autonomous mobility modeling. In light of this review, areas of current need will also be highlighted, and recommended steps forward will be proposed.

It is very challenging to develop a mission critical control for networked heterogeneous UAS that is intelligent and resilient even when implemented into a complex environment with practical constraints such as limited communication, uncertainty system dynamics etc. Meanwhile, lacking applicable decentralized control seriously limits the usage of networked UAS into critical military and civilian missions. Recently, many learning-based decentralized control algorithms have been developed. However, there are two significant limitations, i.e. 1) slow learning convergence speed which cannot catch the environmental changing rate and 2) The gap between mission planning and real-time control. Our proposed algorithm will overcome these challenges and reap the advantages from networked UAS. Deeply integrating online fast reinforcement learning with real-time networked control, a novel mission critical decentralized resilient and intelligent control algorithm will be developed for network heterogeneous unmanned autonomous systems (UAS) in presence of limited communication, system uncertainties and harsh environment. Different from traditional decentralized control and learning algorithms, proposed design is a real-time applicable optimal and resilient solution that has particularly considered real-time mission completeness, the convergence speed of learning algorithm and the impacts from limited communication, system uncertainties and harsh environment.

Autonomous structural health monitoring (SHM) of aerostructures strengthens the reliability, increases the lifetime, and reduces the maintenance cost of aerovehicles such as airplanes and unmanned aerial vehicles (UAV). The continuous monitoring of aerostructures for early damage detection and identification is made possible through a wireless network of sensors deployed on the structure. Usually, the data collected by these sensors is communicated to a central unit for real-time data processing using electromagnetic waves at radio frequencies (RF). However, the emission of RF signals for autonomous SHM creates additional sources of interference to on-board RF communication systems used for aircraft control and safety-related services. To overcome this issue, we propose in this paper an acoustic data communication system for autonomous health monitoring of aerostructures which are modeled as thin plate-like structures. In the proposed system, both damage detection and wireless communication are performed using guided elastic waves. Data communication across an elastic channel is challenging because of the severe frequency-dispersive and multimodal propagation in solid media which distorts, delays, and greatly attenuates the transmitted data signals. To cope with this problem, we introduce a sensor network based on time-reversal pulse position modulation that compensates for channel dispersion and improves the signal-to-noise ratio of the communication link without relying on sophisticated channel estimation algorithms. We demonstrate the viability of the presented system by conducting experiments on an homogeneous and isotropic aluminum plate specimen using Lead Zirconate Titanate (PZT) sensor discs at a resonant frequency of 300 kHz.

Person re-identification is the task of matching visual appearances of the same person in image or video data while distinguishing appearances of different persons. With falling hardware costs cameras mounted on unmanned aerial vehicles (UAVs) have become increasingly useful for security and surveillance tasks in recent years. Re-identification approaches have to adapt to the new challenges posed by this type of data, such as unusual and changing viewpoints or camera motion. Furthermore, the characteristics of the data will change between the scenarios the UAV is used in. This requires robust models that can handle a wide range of characteristics.

In this work, we train convolutional neural networks for person re-identification. However, datasets of sufficient size for training all consist of data from fixed camera networks. We show that the resulting models, while performing strongly on camera network data, struggle to handle the different characteristics of aerial imagery, likely because of an overfitting to data bias inherent in the training data. To address this issue we combine the deep features with hand-crafted covariance features which introduce a higher degree of invariance into our combined representation. The fusion of both types of features is achieved by including the covariance information into the training process of the deep model.

We evaluate the combined representation on a dataset consisting of twelve people moving through a scene recorded by four fixed cameras and one mobile aerial camera. We discuss strengths and weaknesses of the features and show that our combined approach outperforms baselines as well as previous work.

In the last decade, research was conducted to develop measurement solutions dedicated to forest fires and based on image processing and computer vision. Significant progress was achieved in developing such tools for fire propagation in controlled laboratory environments. However, these developments are not suitable for outdoor unstructured environments. Additionally, wildland fires cover large areas; this limits the use of vision-based ground systems. Unmanned Aerial Vehicles (UAV) with cameras for remote sensing are promising as their performance/price ratio is increasing over time. They can provide a low-cost alternative for the prevention, detection, propagation monitoring and real-time support for fire fighting. In this paper, we give an overview of past work dealing with the use of UAVs in the context of wildland and forest fires, and propose a framework based on cooperative UAVs and UGVs for fires monitoring on a larger scale.

Position, navigation, and timing (PNT) user equipment produces position, velocity, and time (PVT) estimates by combining measurements from multiple Global Navigation Satellite Systems (GNSS) and from additional sensors. PVT estimates are computed using linear estimators or Bayesian filters. However, because linear estimators and Bayesian filters are susceptible to adversarial manipulation, it is challenging to assess the trust of PVT estimates that rely on these approaches.

We investigate the suitability of open-universe probabilistic models OUPMs introduced by Milch and Russell as a foundation to design PVT assurance metrics and adaptive PVT estimators. These estimators output PVT information together with trust assessments of PVT inputs and outputs. OUPMs model structural uncertainty (object uncertainty and relational uncertainty) necessary to measure assurance when the availability of sensors and the absence of adversaries cannot be guaranteed.

We describe the challenges of designing PVT assurance metrics using traditional methods, and we illustrate how OUPMs represented as probabilistic programs allow us to address these challenges. In particular, we provide concrete examples of how to combine multiple sources of information to compute assurance assessments using the Texas Spoofing Test Battery. Furthermore, we demonstrate how to leverage PVT assurance metrics to design adaptive PVT estimators designed to operate through attacks.

Today, sensing resources (both devices and processes) play a crucial role in the success of critical tasks such as border monitoring and surveillance. Although there are various types of resources available, each with different capabilities, only a subset of these resources is useful for a specific task. This is due to the dynamism in tasks' environment and the heterogeneity of the resources. Thus, an effective mechanism to select resources for tasks is needed so that the selected resources cater for the needs of the tasks whilst respecting the context of operation. When we started our research a few years back, there was a critical gap between the state-of-the-art and the need to perform context-aware resource selection for tasks. In this paper, we summaries our knowledge-based approach which introduces the context of operation to the resource selection process. First, we present a formalism to represent sensor domain. We then introduce sound and complete mechanisms through which effective resource solutions for tasks are discovered. An extension to the representation is then proposed so that the agility in resource selection is increased. Finally, we present an architecture whereby a multitude of such knowledge bases are exposed as services so that a coalition can fully benefit from its networked resources. Our approach is general in that, it can be applied in many other domains—especially in service sciences; we have provided some evidence towards this.

Abnormal detection using cameras in UAV platform become more and more popular for operation and maintenance, in particularly for large-scale constructions like building, bridge etc. UAV-used detection system could be expected to reduce the cost, ensure the safety and provide stability for O&M on infrastructures. As imaging technology, Image registration and change detection method plays a central role in an abnormal detection system. Two key factors in this respect are needed to be improved. Firstly, due to the near-distance photographing and complex surface composition of structures, a robust plane-level matching method is significant to make high-precision image registration for the change detection. However, as many part of the surface of structures do not have enough feature points, it seems difficult to make a plane matching using homography transformation based on the correspondence feature points. Secondly, plane-level change detection have much noise in the border area because of homography transfer deviation and information redundancy. In order to solve these two problems, a robust method based on a combination of edge detection and geometry constraint is proposed to make plane-level registration and change detection noise reduction. For registration, making good use of pixel information in the border area, we expand the border area to extract each plane regardless of the number of feature points. And for noise reduction, we excise the border information to reduce the effect of information redundancy. Validation experiments were performed with several sets of image pairs. We succeed to extract planes in images with a 92% coverage and 91% precision while the number of noise is reduced as 30% as before for average. The evaluation shows that our proposed method is of high precision with high robustness for abnormal detection system.

Artificial neural networks affect our everyday life. But every neural network depends on the appropriate training set and setting of internal properties with hyperparameters. Even accurate and complete training set doesnt imply high performance of neural network algorithm. Tuning of hyperparameters is crucial for correct functionality, fast learning and high accuracy of neural networks. The hyperparameter selection relies on manual fine-tuning based on multiple full training trials. There are a lot of neural network implementation available for public and commercial use, but the setting of hyperparameters is often a neglected problem. Choosing the best structure and hyperparameters is the primary challenge in designing a neural network. This article describes a genetic algorithm for automatic selection of hyperparameters and their tuning for increasing the performance of neural networks without human interaction. The optimization algorithm accelerates the discovery of configuration, which is otherwise a time-consuming task. We evaluate the results of optimizations in comparison to naïve approach and compare pro and cons of different techniques.

Sensor fusion remains a difficult task, particularly in noisy environments or when data are incomplete. The Soar cognitive architecture provides the ability to reason over these data sources and make decisions based on the situational context. To feed into the reasoning system, we have created a hierarchical system of analysis components operating at differing levels of complexity. This approach of building up the reasoning system provides robust behavior is applicable across a variety of domains.

A need exists for a self-forming, self-organizing, cognitive, cooperative, automated unmanned aerial vehicle (UAV) network system to more efficiently perform UAV-based maritime search and rescue (SAR) operations. Although current search patterns (e.g., traditional “lawn mower” methods) are thorough, they result in too much time spent searching lowprobability areas. This decreases the chances of a successful rescue and increases the risk of lost recovery opportunities (e.g., death due to hypothermia in the case of human search targets). Our goal is to optimize UAV-based SAR operations. As directed by an onboard computer, UAVs would fly coordinated search patterns based on the target’s last known position and the direction and speed of winds and currents. By enabling the UAVs to act collectively and cooperatively, we can enhance the efficiency and effectiveness of a multi-UAV network’s SAR mission. To achieve this, we applied cooperative game theory as an enabling function in the development of a cognitive system encompassing multiple vehicles. Based on simulations, we showed that an optimal dynamic search pattern and vehicle positioning strategy can be realized using decision algorithms based on elements of game theory.

Unmanned aerial vehicles (UAVs) are being used to reduce the cost and risk of facility inspections. For the power distribution companies, power line inspection for providing stable power supply is an important but costly task. It includes deterioration diagnosis, detection of foreign matter adhesion, and estimation of power line-tree conflict risk, all of which is currently performed visually on foot. In this study, we explore the methods of detection and visualization of a power line-tree conflict using aerial images taken by drones. To detect a power line-tree conflict, we should firstly recognize the power lines and trees in the aerial images in order to identify the “candidate” regions of the conflict, and secondly, estimate the actual positional relationship between them in 3D. However, as previous studies have shown, the detection of power lines in an image is a challenging task because they are very narrow and monochromatic, which results in difficulty in extracting features. This specific character of the power lines could also cause failure in 3D reconstruction, in which feature matching among images is necessary. Here, we show that convolutional neural networks (CNNs) can be effectively applied in recognition of power lines and trees in an image. We also found that in mapping the candidate region of conflict to a 3D model the power line position could be estimated by taking the pole height into account. This way, even if it is difficult to reconstruct the power line in 3D, a user can make the final decision about the conflict by checking the depth and/or the height directional relationship.

Survivability has always been of interest in the defense of any armored vehicles. There has been many reports and papers on the survivability of U.S. Army ground vehicles. A Survivability severity model can be best described as the analogy to the layers of an onion, in which each layer of onion describes a different severity level and the phase of threat detected and severity to apply countermeasures. The objective of this paper is to suggest an evaluation tool that contains an algorithm and procedure for the reliability of manned and unmanned ground vehicles. A decision-making system is proposing for the theoretical survivability and is calculated from a threat level in the form of severity. A generic framework algorithm, consists of both linear and non-linear vehicle dynamics systems, and is included in this paper, which consists of the Fuzzy approach and various scenarios, based on straight path projection. Further, to increase the level of rigor, a layered fuzzy control system using various vehicle dynamics parameters [1] and a methodology for designing an adaptive hierarchical fuzzy model [2] and to accommodate various system parameters dependencies, are describing in this paper as a part of the survivability model. It is hoped that different users will tailor this evaluation tool and used extensively by various research workers working in different area. Several probabilistic cases were included in this paper and implemented by converting to linguistic fuzzy parameters to evaluate the algorithm. A simulation model is designed using supervisory fuzzy rule set and several simulation studies have been done which illustrate the effectiveness of the given approach. The result is a robust d flexible control system.

An impedance measurement based level sensor is proposed using a co-axial probe for sensing liquid level in a container. The co-axial sensing probe is made with a hollow stainless steel outer conductor enclosing an insulated inner conductor. The impedance of the co-axial probe varies with the water level in a nonlinear fashion. The supporting electronics was developed using MSP 432 microcontroller unit (MCU) platform from Texas Instruments (TI) and a newly designed Impedance Analyzer-Analog Front End (IA-AFE) developed at TI. An inverter amplifier based circuit was implemented within the IA-AFE for impedance measurement. Discrete Fourier Transform (DFT) is calculated on the MCU platform from the sampled input and output square wave voltages of the IA-AFE. The proposed sensor shows a maximum error within ±1.5 mm, for the probe of length 40 cm. The proposed system offers an accurate and economical liquid level measurement platform outperforming the state-of-art level sensors to the best of our knowledge.

The phase array radar has been widely used in military due to the dramatic advance in computer technique and signal processing technology. However, the phase array always has thousands of antenna elements. It is a huge amount cost of hardware resource. In order to reduce the cost, we need to perform subarray level signal processing for phase array radar. In this paper, we propose the linear minimum variance adaptive beamforming approach to calculate the adaptive weighs at the subarray level for digital adaptive beamforming. The proposed method helps to decrease the hardware cost and make the phase array radar available to suppress the interference adaptively. During the calculation of the adaptive weights, the inverse of the matrix needs to be derived. The float round-off error in the calculation is accumulated. It is very important to analyse the impact of the float round-off error on the result accuracy. In this paper, the float round-off error in the proposed method is analysed.

As the dramatic development in the array signal processing, the phase array radar is widely used. The phase array radar is more flexible in beamforming since we just need to adapt the phase shift to change the direction of beamforming. However, it is always a huge cost of hardware resources since each antenna antenna element needs its own receiving path. Therefore, the phase array radar should be divided into subarrays and each subarray has one receive path. It is very difficult to maintain good performance of the phase array radar after the subarray division. In this paper, we propose a novel subarray division method which is based on the genetic algorithm. The proposed method helps to divide the subarrays and maintain good mainlobe for the expected signal and low sidelobe for the interference suppression. In addition, the simulation results are given to show our proposed method has good performance in the antenna pattern and reduce the hardware cost dramatically.

The phase array radar has been widely used in military due to the dramatic advance in computer technique and signal processing technology. In the meantime, the electromagnetic environment becomes more and more complicated. The phase array radar should have the ability to survive in the complicated electromagnetic environment. Therefore, the phase array radar should have good antenna pattern. The antenna pattern needs to maintain good performance at the pitch and azimuth for the sum beam and differential beam. In this paper, we propose the coefficient weights method to obtain the multi-objective optimization for the pitch and azimuth for the sum beam and differential beam. In addition, the adaptive interference suppression is another important part of the phase array radar. The proposed method in the paper also take this into consideration. Moreover, the paper gives sufficient validation results to show that the proposed method has good performance.

As the dramatic advancement in array signal processing, the phase array radar becomes widely used in military. However, the electromagnetic environment is very complicated with different kinds of electromagnetic interference. It makes the expected signal very difficult to be accurately received. The adaptive interference suppression method is adopted in the phase array radar to suppress the different kinds of interference. But this method works well only if the interference is the sidelobe interference. In this paper, we propose a two layer adaptive monopulse method to suppress the mainlobe and sidelobe interference. The main idea is to divide the mainlobe interference suppression and sidelobe interference suppression into two layers. First, the mainlobe maintains the same when performing sidelobe interference suppression. Then, the mainlobe interference is suppressed at the pitch and azimuth while the monopulse ratio stays unchanged. The simulation results show that our proposed method has good performance in estimation of the direction of arrival and interference suppression.

As the electromagnetic environment becomes more and more complicated, the interference suppression becomes a big challenge for the phase array radar. If the interference cannot be suppressed, the performance of the phase array radar decreases dramatically. However, most of the interference suppression method based on the assumption that the direction of the interference is already known. Actually, the direction of the interference also needs to be estimated. In this paper, we propose a advanced spatial spectrum estimation method to estimate the direction of the interference. In our proposed method, in order to reduce the cost and complexity of the calibration for the thousands of antenna elements in the phase array radar, the calibration is at the subarray level which makes the calibration much easier. Each subarray forms a super element. The simulation results shows the proposed method has good performance for the interference direction estimation.

The headlong rush by manufacturers to make everything connected is increasing the number of cyber targets dramatically. Unfortunately, many consumers are totally unaware of how vulnerable all of their connected technology devices make them. In this paper, we will try to highlight the nature of the IoT and what we should be doing to close up the security holes before computer network operations can take devices over.

As the Internet of Things hype train continues to gather steam, there are a number of solution platforms that are vying for mindshare. Both the commercial and industrial/medical IoT are rich with different offerings. Some of them are open, and some are totally proprietary. In this paper, we will try to highlight the different dimensions of many of these platforms so the reader can make an informed decision as to which platform may be best for their applications.

The IPv4 address space is exhausted. The regional Internet registries are now only handing out IPv6 addresses. But, how does IPv6 enable access to the 20+ billion devices that are estimated to be in the IoT by the year 2020? And, since IPv4 isn't going away any time soon, how do we make IPv6 work side by side with IPv4? In this paper, we will describe IPv6 addressing and its operation. Additionally, we will show techniques for writing one application that can support both IPv4 and IPv6 protocols simultaneously.

With the headlong rush to move everything into the cloud, there are some cases where this may be counterproductive. In the case of distributed sensor networks, getting the data into the cloud has several hurdles. The connectivity of the sensors is a significant challenge if all of them must transfer data to remote, cloud-based servers. And, if the sensors can see the Internet, then the Internet can see them. In this paper, we will discuss an alternative connectivity model known as the Fog model. We will compare and contrast the cloud-centric versus fog-centric models across several dimensions including latency, jitter, cost of connectivity and most importantly, security.

Honeyman, named for the American Revolutionary War spy and source of disinformation, is an IoT distributed deception platform (DDP), aka “honeynet”, based approach to military deception and indications and warning (I&W) generation. While DDP approaches have evolved from single honeypots to complex network architectures and have resolved previous challenges associated with revealing a DDP’s signature or “fingerprint” including virtual device information, and therefore have become applicable for IoT uses, these approaches are still bounded in their application to cybersecurity purposes only. For example, data positioned as cyber-bait is meant only to draw in a cyber attacker but not to influence a strategic level of decision-making such as military or national security decisions. Additionally, monitoring within the DDP gathers data to model attackers’ cyber behavior and patterns for explicit purpose of identifying new offensive cyber techniques and thwarting new attacks. Honeyman combines a proxy military logistics and readiness reporting IoT comprised of a mixture of virtual and physical devices with non-cyber information operations for military deception and to stimulate nation-state adversary behavior within the DDP. A machine learning (ML)-based traffic analysis model leverages observations within the honeynet to forecast an adversary’s physical military activity thereby providing critical I&W. Further research is needed to optimize the combination of physical and virtual IoT devices for best deception performance, to evolve the tradecraft of dynamic cyber-bait, and to refine appropriate ML-based I&W models.

Nowadays, the array signal processing is becoming more and more popular. There are a lot of applications based on the array signal processing. One of the key application is the phase array radar. Since phase array radar has a huge amount of antenna elements, we need to perform subarray level processing. In this paper, we propose the subspace projection adaptive approach to calculate the adaptive weighs at the subarray level for digital adaptive beamforming. The proposed method helps to decrease the hardware cost and make the phase array radar available to suppress the interference adaptively. During the calculation of the adaptive weights, the inverse of the matrix needs to be derived. The float round-off error in the calculation is accumulated. It is very important to analyse the impact of the float round-off error on the result accuracy. In this paper, the float round-off error in the proposed method is analysed.