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Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system.

First, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly.

Compared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance.

In this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.

The purpose of this paper is to present the scientific background from which grey systems theory came into being, the astonishing progress that grey systems theory has made in the world of learning and its wide‐ranging applications in the entire spectrum of science.

The grey uncertainty is compared with other kinds of uncertainty such as stochastic uncertainty, unascertainty, fuzzy and rough uncertainty.

The advances in grey systems theory and its various successful applications are introduced individually by algorithms of grey numbers and grey algebraic systems, grey dynamic models and grey predictions, grey optimization analysis for decision making, grey control models.

Many scientific theories require the unremitting efforts of several generations of people and have gone through hundreds of years before reaching maturity and perfection. Grey systems theory is still in its growth period. So, it is unavoidable that there exist immature and imperfect parts in the theory.

Grey systems theory is a new method for studying problems of uncertainty with few data points and poor information. This new theory studies small samples and systems with poor information, which have partial information known, partial information unknown. It describes adequately and monitors effectively systems' operations and evolutions through extracting valuable information from the little known information. Grey systems theory comes into being along with the development of modern systems science and uncertainty systems theories and methods. It is also a result of deepened perceptivity about uncertain systems.

An interactive processing scheme is proposed to improve the target detection probability as well as the tracking performance of the radar system.

Firstly, with the spatial-correlated features extracted from the foreground and background statistical models, the thresholds were adapted to distinguish the dim small targets from clutters in the complex incoherent radar images. Then, the target trajectories were constructed with the target tracking algorithm. According to the temporal correlation with the target life cycle, the thresholding values were modified in the neighbourhood of the predicted positions to improve the detection sensitivity in these areas during the tracking process. Finally, the temporal-correlated features of the remained clutters were used to further reduce the false alarm rate.

The proposed algorithm was applied on the simulated data, as well as the image sequences obtained with the incoherent marine radars. The detection results demonstrated that the interactive algorithm could detect and track the dim small targets with relatively low false alarm rate.

The interactive processing scheme could be applied for low-altitude airspace surveillance with incoherent marine radar.

The proposed scheme outperforms the classical radar target detection algorithms and the state-of-the-art image processing algorithms for video-based surveillance.

Incoherent primary radar is an applicable means for security surveillance of low-altitude airspace. An experimental airspace surveillance radar system has been developed for such applications. Target detector based on radar images is a powerful technique for the system. The main difficulty in designing such a detector is the rejection of heavy edge clutter for the plane position indicator (PPI) radar images after background subtraction.

The paper proposes an edge clutter rejection (ECR) detector with spatial characteristics to detect target instead of sheer threshold segmentation.

The paper chooses the optimal parameter values for the ECR detector and compares it with the existing techniques. Detection results show that the proposed detector achieves higher probability of detection with low false alarm rate, outperforming the fixed-threshold detector and the popular constant false alarm rate detectors. The ECR detector also presents limited computational cost due to its concentration on the pixels detected by the fixed-threshold algorithm with low threshold.

The aviation security of low-altitude airspace can be greatly increased by designing affordable airspace surveillance radar system.

The paper presents critical techniques for clutter rejection with PPI images, which is a significant part of the surveillance system.

This paper aims to review the critical technology development of avian radar system at airports.

After the origin of avian radar technology is discussed, the target characteristics of flying birds are analyzed, including the target echo amplitude, flight speed, flight height, trajectory and micro-Doppler. Four typical airport avian radar systems of Merlin, Accipiter, Robin and CAST are introduced. The performance of different modules such as antenna, target detection and tracking, target recognition and classification, analysis of bird information together determines the detection ability of avian radar. The performances and key technologies of the ubiquitous avian radar are summarized and compared with other systems, and their applications, deployment modes, as well as their advantages and disadvantages are introduced and analyzed.

The ubiquitous avian radar achieves the long-time integration of target echoes, which greatly improves detection and classification ability of the targets of birds or drones, even under strong background clutter at airport. In addition, based on the big data of bird situation accumulated by avian radar, the rules of bird activity around the airport can be mined to guide the bird avoidance work.

This paper presented a novel avian radar system based on ubiquitous digital radar technology. The authors’ experience has confirmed that this system can be effective for airport bird strike prevention and management. In the future, the avian radar system will see continued improvement in both software and hardware, as the system is designed to be easily extensible.

THE COHERENT PULSE DOPPLER RADAR ALTIMETER developed by Hoffman Electronics Corporation, operates with only a single antenna, the highly reliable, solid‐state altimeter set, electronic AN/APN‐201, fig 1, provides pilots with precise altitudes down to ground zero without fear of false lock or double bounce readings.

The Paris/Le Bourget International Air and Space Show is the world's oldest international show and is of considerable importance. It will have 548 exhibitors from 23 countries and there will be 165,000 square metres of display area — with 10,000 square metres for the out‐door static exhibits. The numbers of chalets is to be increased by 55.

AS little information has been published, the subject of bombsights is shrouded in mystery, and hence from time to time it is natural that the daily press should make amazing disclosures concerning the alleged performances of new types of bombsights; including an American one capable of dropping bombs into a barrel from the sub‐stratosphere. Such statements can be viewed in their proper perspective if the functions of a bombsight are understood. The state of the enemy ground defences dictate the requirements of a bombsight, since if no opposition is met it is possible to drop delayed action bombs from a low altitude without using a bombsight. As ground defence improves, it is necessary to bomb from a greater height, and so the need for a bombsight increases. It is also necessary to bomb from a great height if armour‐piercing bombs are used, so that they can attain their terminal velocity. Unfortunately, or fortunately, depending on one's position at bomb release, accurate bomb aiming is a complicated matter, and errors increase with height. Thus, as high altitude bombing becomes necessary, so the bombsight becomes more complicated, and as ground defences become more accurate the operational difficulties of the aircraft increase and make still further demands on the bombsight. Thus, as the war proceeds a steady evolution can be observed in the types of bombsight used, as will be seen from those described in this article. Anti‐aircraft gunsights have progressed from simple open ones to those incorporated in the predictor system, and in a like manner bombsights have made similar progress. As each sight has had a bearing on the design of the other, it is interesting to compare the requirements and operation of a bombsight with those required for an anti‐aircraft gunsight.

This paper aims to study a neural network-based fault-tolerant controller to improve the tracking control performance of an unmanned autonomous helicopter with system uncertainty, external disturbances and sensor faults, using the prescribed performance method.

To ensure that the tracking error satisfies the prescribed performance, the authors adopt an error transformation function method. A control scheme based on the neural network and high-order disturbance observer is designed to guarantee the boundedness of the closed-loop system. A simulation is performed to prove the validity of the control scheme.

The developed adaptive fault-tolerant control method makes the system with sensor fault realize tracking control. The error transformation function method can effectively handle the prescribed performance requirements. Sensor fault can be regarded as a type of system uncertainty. The uncertainty can be approximated accurately using neural networks. A high-order disturbance observer can effectively suppress compound disturbances.

The tracking performance requirements of unmanned autonomous helicopter system are considered in the design of sensor fault-tolerant control. The inequality constraint that the output tracking error must satisfy is transformed into an unconstrained problem by introducing an error transformation function. The fault state of the velocity sensor is considered as the system uncertainty, and a neural network is used to approach the total uncertainty. Neural network estimation errors and external disturbances are treated as compound disturbances, and a high-order disturbance observer is constructed to compensate for them.

A fixed wing unmanned aerial vehicle (FWUAV) is targeted to perform processor in the loop (PIL) simulations for the flight scenarios such as straight and level, level climb, level turn, climbing turn and level steady heading sideslip under windy conditions such as steady wind (SW) and wind gust (WG) in a desired and controlled manner.

The constrained multi-input–multi-output (MIMO) lateral and longitudinal linear models-based model predictive controllers (MPCs) which are proposed in a previous study (Ulker et al., 2017) are tested in the PIL simulations under specified windy conditions. BeagleBone Black Rev C is used as a target hardware or processor in the PIL simulations.

The results of the PIL simulations show that the MPCs proposed in the previous study can achieve satisfactory performance and flying qualities for the all flight scenarios handled in this paper under windy conditions.

The MPCs proposed in the previous study can be easily implemented in the real world to a low-cost and small-sized board like BeagleBone Black Rev C which is used in this paper.

The proposed MPCs in the previous study which are capable of providing more flexibility in terms of tracking complex trajectories are showed to be able to be implemented to real system by means of PIL simulations under the changeable windy conditions which are difficult for performance tests.