基于足部微惯性测量单元(MIMU)和超宽带测距的协同导航技术是一种解决卫星信号受限环境下单兵自主导航难题的有效途径.根据零速修正辅助的惯性解算特点,建立了一种单兵协同导航模型,协同导航状态为各单兵的位置和航向,系统输入为足部MIMU提供的每步位移和航向增量.将算法与相关文献中提出的两种算法进行了对比分析,该算法的优点是无需对足部惯导模块做任何改动和进行反馈修正,易于工程实现且不损失精度.通过三人协同导航试验分析了算法的性能,数据分析表明协同导航在不同条件下可以不同程度地改善系统的定位性能.

在卫星受限情况下的单兵自主导航技术是近年来研究的热点.首先介绍了单兵自主导航技术的发展概况,然后重点分析了基于MIMU的单兵惯性导航技术的主要原理和方法,之后针对纯惯性导航存在的问题,介绍了地磁、场景、视觉等常用的单兵辅助导航方法,并阐述了单兵协同导航技术.最后,简要介绍了单兵自主导航技术的未来发展趋势.

In this chapter, we discuss the new spectrum paradigm for the 5th generation wireless systems (5G). It is expected that wireless data traffic will grow substantially in the next 5 years. By 2020, we expect to see 1000–10000-fold traffic increase. This increase comes from several trends, including the rapid increase of mobile devices, the increase of data traffic itself and the emergence of new applications and use cases such as the Internet of Things. Therefore, how to increase the system and link capacity becomes a key ingredient in 5G system design. Among the many solutions to meet this increased capacity demand, they can be broadly categorized into either one of two approaches. One is to increase spectrum efficiency through state of the art techniques such as massive MIMO, network cooperation or others. The other is to introduce new spectral resources and adoption of innovative approaches in the management and access to new and existing spectrum. In this chapter, the focus is on the latter i.e. the introduction and access management of new spectral resources to support the substantial traffic increase in 5G. This chapter starts with an introduction to the concept of dynamic spectrum access and the principles of radio resources sharing with Cognitive Radio. Sensing techniques and access policy are described. This section concludes with an overview of the various standardization efforts related to dynamic spectrum access for Cognitive Radio. In section “Spectrum and Channel Model”, spectrum allocation and availability for both the low and high frequencies are reviewed and the mmWave frequency bands are identified. The various challenges and ongoing studies to understand and model the channel at mmWave are explained and a Clustered Delay Line channel model produced by the Third Generation Partnership Program (3 GPP) is introduced. This section concludes with a brief introduction of the adoption of hybrid beam forming to address the challenges of high path loss with mmWave wireless communications. Finally in the last section, the concept of spectrum sharing with License Shared Access (LSA) and its 2 and 3-tie LSA architectures is explained.

The method of cooperative navigation is proposed in this study to solve the terrible results of the indoor pedestrian navigation due to GPS information unavailable and the serious divergence of inertial navigation. To improve the pedestrian navigation accuracy, the paper designs a cooperative pedestrian navigation system which is based on the distance restraint between the pedestrians and applies the information filter to get the navigation result. The experiment is designed to prove the effectiveness of cooperative pedestrian navigation using the MIMU and UWB measurement equipment. The final results show that the interactive fusion of navigation information for each pedestrian is achieved and the divergence of the navigation results is restrained.

The lack of ground-tracking resources has become a primary bottleneck for the Chinese BeiDou navigation satellite system. As crosslinks have been widely recognized as a promising augmentation for the autonomous navigation of the global navigation satellite system, this article studies a new decentralized data fusion method for orbit determination of crosslink-augmented satellite constellations. In the new solution, the system is modeled as a probabilistic graphical model, the dynamical Bayesian network, and a graphical method named junction tree is introduced to analyze the structure of the system. By dividing and arranging the predictions and estimations of junction tree in a proper sequence, a new decentralized solution with centralized equivalent precision is designed. As the solution requires satellites in the constellation cooperating with each other through communications and measurements, it is named junction-tree-based cooperative orbit determination. Simulation results indicate that junction-tree-based cooperative orbit determination has centralized equivalent precision and good robustness after satellite failures, whereas centralized solutions such as extended Kalman filter may suffer from processing center malfunctions. Junction-tree-based cooperative orbit determination could not only serve as an optional choice for global navigation satellite system autonomous navigation but also be used as a general scheme for decentralized data fusion in cooperative systems such as unmanned aerial vehicle formations and so on.

本文在阐释BOPPPS模型六个要素内涵的基础上，结合大学理工科教学特点，归纳了各要素的常用形式和设计方法。最后结合《飞行力学基础》课程研究型教学活动的开展，给出了基于BOPPPS模型教学设计的具体做法。

Secure degrees of freedom (DoF) in MIMO cognitive radio system is studied in this paper. We consider a cognitive radio system with one primary source-destination pair, multiple secondary source-destination pairs and an eavesdropper against whom the primary user intends to secure its data. In this system, multiple secondary user pairs help to secure primary user's data against eavesdropping. In return, these secondary users are allowed to access primary user's spectrum. All users, including primary user pair, K≥2 secondary user pairs and the eavesdropper are equipped with M antennas. We investigate the secure DoF without the knowledge of eavesdropper's channel state information (CSI). A beamforming design is proposed to achieve secure DoF d for primary user and DoF d for all secondary users if Kd≤M. Simulation examples corroborating the theoretical results are presented.

As centralized state estimation algorithms for formation flying spacecraft would suffer from high computational burdens when the scale of the formation increases, it is necessary to develop decentralized algorithms. To the state of the art, most decentralized algorithms for formation flying are derived from centralized EKF by simplification and decoupling, rendering suboptimal estimations. In this paper, typical decentralized state estimation algorithms are reviewed, and a new scheme for decentralized algorithms is proposed. In the new solution, the system is modeled as a dynamic Bayesian network (DBN). A probabilistic graphical method named junction tree (JT) is used to analyze the hidden distributed structure of the DBNs. Inference on JT is a decentralized form of centralized Bayesian estimation (BE), which is a modularized three-step procedure of receiving messages, collecting evidences, and generating messages. As KF is a special case of BE, the new solution based on JT is equivalent in precision to centralized KF in theory. A cooperative navigation example of a three-satellite formation is used to test the decentralized algorithms. Simulation results indicate that JT has the best precision among all current decentralized algorithms.

In geomagnetic aided navigation (GAN), the vehicle is expected to traverse the areas with excellent matching suitability in order to obtain high matching precision. The route planning problem under matching suitability constraints is studied based on particle swarm optimization (PSO) algorithm in this article. Firstly, the PSO algorithm is briefly introduced and the expanding space of route nodes is determined with the maneuverability constraints of the vehicle. Then the minimum movement distance, the ability of avoiding threats and the proximity to suitable-matching areas are considered to construct the fitness function of PSO algorithm. Further the route planning method under matching suitability constraints is proposed. Experimental results show that the proposed method is effective, and the vehicle can successfully avoid the threats and can traverse the suitable-matching areas.

We consider a K-user multiple input multiple output (MIMO) Y channel consisting of K(≥ 3) users and a relay. Each user has K-1 independent messages for all the other K-1 users. Degrees of freedom (DoF) of such channels is not known in general but it is known that the DoF of K(K-1)/2 is achievable for a network operating in a half-duplex mode by using signal space alignment for network coding during both the multiple access phase and the broadcast phase. In this paper, a novel signal group based alignment scheme is proposed, which divides all K(K-1) signals into l groups where l = K or K-1. Then, the signals in each group are aligned into a smaller subspace at the relay. If the i-th user is equipped with M i antennas and the relay is equipped with N antennas where all antennas are used for both transmitting and receiving, we prove that when M i = K-1, N = (K-1) 2 for even K and M i = K-1, N = K(K-2) for odd K, the optimal total DoF of this K-user MIMO Y channel is K(K-1)/2. As a consequence, to achieve the total DoF of K(K-1)/2, the requirements on M i and N are M i ≥ K-1 and N ≥ (K-1) 2 for even K, and M i ≥ K-1 and N ≥ K(K-2) for odd K. In our proposed approach, we significantly decrease the minimum M i at the expense of higher N for a given number of users K and achievable DoF of K(K-1)/2, compared to an existing approach. This signal group alignment concept also motivates other signal grouping methods, which provide a tradeoff between number of antennas at end users and the relay. Also, for the K-user Y channel where all end users have a single antenna and the relay node has N antennas, it is shown that the DoF of min{K/2, (N + 1)/2} is achievable.

This paper presents an efficient, centralized equivalent and fully decentralized solution to the cooperative localization of mobile robot teams. Formulating the cooperative localization problem in the framework of Bayesian estimation, the decentralized solution is designed by interlacing the calculation steps of prediction and update in a proper sequence. In the proposed solution, each robot fuses only the sensor data relevant to itself; information is shared among the robots by a chain communication topology. The solution yields linear minimum mean-square error estimates, equivalent to a centralized extended Kalman filter. There is no information redundancy and computation duplication among the robots. The solution can also be viewed from the perspective of implementing inference on a specific junction tree. The performance of the proposed algorithm is evaluated with simulation experiments.

In this paper, we consider a K-user MIMO (multiple input multiple output) Y channel consisting of K, K ≥3, users and a relay. Each user has K - 1 independent messages for all the other K-1 users. With the deployment of multiple antennas at both end users and the relay, K(K - 1) messages can be conveyed to their desired receivers within two time slots for a network operating in a half-duplex mode. A signal group based alignment scheme is proposed which divide all K(K - 1) signals into K groups when K is odd and K - 1 groups when K is even. Then the signals in each group are aligned into a smaller subspace at the relay. If each user is equipped with M antennas and the relay is equipped with N antennas, we show that in order to exchange K(K - 1) messages, the requirements on M and N are KM ≥ N +K - 1 and N ≥ (K - 1) 2 for even K and (K - 1)M ≥ N +1 and N ≥ K(K - 2) for odd K. In our proposed approach we significantly decrease the minimum M at the expense of higher N for a given number of users K, compared to an existing approach.

This letter reports an experimental evaluation of a three-axis magnetometer into an inertial navigation system (INS) for underwater localization. The magnetometer measurements of geomagnetic field are compared with map values to provide position updates to the INS. The concept of such navigation system is not new but lacks test verification and actual application. We examine the capabilities of the integrated navigation by using a localization algorithm based on the interval knowledge of geomagnetic field values. The underwater experimental result indicates that the use of geomagnetic values significantly reduces the growth of position errors of an INS.

Geomagnetic matching is to utilize the geomagnetic field for positioning, and the matching suitability of candidate matching areas (CMAs) is a key factor of affecting matching precision. In underwater geomagnetic navigation, the underwater geomagnetic map is usually obtained by downward continuation, and therefore the influence of downward continuation on matching suitability is studied in this article. Firstly, the downward continuation algorithm is briefly introduced. Then the comparison analysis of matching suitability before and after downward continuation is made from an experimental point of view. Results show that the CMAs that own excellent matching suitability can still keep the well matching suitability, and moreover, for the CMAs whose matching suitability is normal or bad, the matching suitability may be improved after downward continuation. The conclusions of this article may provide helpful lessons for matching suitability analysis in underwater geomagnetic navigation.

We consider joint optimization of cooperative spectrum sensing, channel access and power allocation in an overlay multiband cognitive radio network. A soft-decision cooperative spectrum sensing concept using continuous-valued sensing test statistics is considered, instead of making hard binary decisions as in traditional hypothesis testing spectrum sensing schemes. The channel access decision about whether to access the channel or not is relaxed into allowing the secondary user to access channels with some probability. The sensing decision is made at the secondary base station based on the sensing statistics received from all or a subset of secondary users. This joint optimization problem is aimed at maximizing the secondary users' sum instantaneous throughput while keeping the interference to primary users under a specified threshold. The problem is shown to be a convex optimization problem and the Lagrangian dual method is employed to obtain the optimal solution. Two heuristic algorithms are also proposed to reduce computational complexity. We also discuss an alternative formulation where additionally interference to individual PUs is also constrained. Simulation results show that our soft sensing based algorithm significantly outperforms a traditional hard decision sensing algorithm.

In this paper, we design relay precoder in a MIMO cognitive radio network where two-way transmission of multiple secondary user pairs occurs concurrently with primary network's transmission. We propose an interference alignment like precoder design which jointly aligns the direction of interference and the desired signal while interference to primary network is completely canceled. When the secondary transmitters and receivers are equipped with multiple antennas, sources, relay and receivers can all participate in aligning the interference and signal directions. It is shown that zero-forcing relay beamforming in which inter-pair interference is aligned to the null space of desired signal space is a special case of our algorithm. Our proposed algorithm can also work in the scenario where the number of antennas at relay node is not large enough and therefore zero-forcing is not possible. The effectiveness of the proposed algorithm is illustrated via simulations and compared with zero-forcing and MSE based designs.

This paper presents a distributed algorithm for performing joint localisation of a team of robots. The mobile robots have heterogeneous sensing capabilities, with some having high quality inertial and exteroceptive sensing, while others have only low quality sensing or none at all. By sharing information, a combined estimate of all robot poses is obtained. Inter-robot range-bearing measurements provide the mechanism for transferring pose information from well-localised vehicles to those less capable. In our proposed formulation, high frequency egocentric data (e.g., odometry, IMU, GPS) is fused locally on each platform. This is the distributed part of the algorithm. Inter-robot measurements, and accompanying state estimates, are communicated to a central server, which generates an optimal minimum mean-squared estimate of all robot poses. This server is easily duplicated for full redundant decentralisation. Communication and computation are efficient due to the sparseness properties of the information-form Gaussian representation. A team of three indoor mobile robots equipped with lasers, odometry and inertial sensing provides experimental verification of the algorithms effectiveness in combining location information.

The problem of cooperative navigation for a team of platforms employing inter-platform observations is investigated. A decentralised solution in the framework of an information filter with delayed states is presented. In this structure, each platform first estimates its motion using only local sensor data, then shares its information across the network using an algorithm that employs a distributed Cholesky modification. The decentralised solution permits each platform to act in the same modular manner, providing robustness to individual platform failure. The solution yields linear minimum mean-square error estimation performance. As such the estimates generated are optimal; it generates exactly the same estimates as would a conventional extended Kalman filter (EKF), if given the same data. Efficient sparse implementation is accomplished without resorting to approximate methods. Simulation experiments employing a team of ten mobile platforms are described and used to evaluate the decentralised estimation performance. The robustness, flexibility, and cost of the decentralised approach are analyzed and compared with an existing distributed solution.