基于动态Radio Map的粒子滤波室内无线定位算法

针对目前大多数基于射频信号强度匹配定位算法在定位精度及鲁棒性方面不足,提出了一种基于动态Radio Map的粒子滤波室内无线定位算法.该算法利用参考节点构建基于空间关联性的动态Radio Map模型,以反映信号环境的实时变化,并将移动目标定位由分类问题转化为回归问题,打破了传统网格式Radio Map模型的限制,降低了算法的时空复杂度.实验结果表明,相对于静态Radio Map模型,动态Radio Map模型将定位精度平均提高了约20%,表现出良好的环境动态自适应能力.

煤矿井下基于虚拟Radio-map及Markov链的定位算法

为了在煤矿井下获得更高的定位精度,提出一种基于虚拟Radio-map及Markov链的定位方法。结合井下复杂环境,采用信道衰减模型及线性插值法实现了动态衰减因子,建立虚拟Radio-map的同时降低了工作量;考虑到每处采样点接收信号强度分布先验假设和统计特征,在线阶段采用基于贝叶斯准则框架的加权核函数算法,为每个样本数据赋予一个以自身为"核心"的函数,构建的概率密度分布避免了确定模型带来的误差,从而提高了定位精度;为进一步优化定位结果,考虑先验概率对贝叶斯后验概率的影响,提出了基于高斯模型的Markov链定位算法,抑制了运动目标位置的大幅度跳变,使目标定位更加精确。实验表明,所提算法可以通过较低数据采集工作量达到一定的定位精度,满足井下目标定位需求。

Radio-map Establishment based on Fuzzy Clustering for WLAN Hybrid KNN/ANN Indoor Positioning

A novel radio-map establishment based on fuzzy clustering for hybrid K-Nearest Neighbor (KNN) and Artifi cial Neural Network (ANN) position algorithm in WLAN indoor environment is proposed. First of all, the Principal Component Analysis (PCA) is utilized for the purpose of simplifying input dimensions of position estimation algorithm and saving storage cost for the establishment of radio-map. Then, reference points (RPs) calibrated in the off-line phase are divided into separate clusters by Fuzzy C-means clustering (FCM), and membership degrees (MDs) for different clusters are also allocated to each RPs. However, the singular RPs cased by the multi-path effect signifi cantly decreases the clustering performance. Therefore, a novel radio-map establishment method is presented based on the modifi cation of signal samples recorded at singular RPs by surface fitting. In the on-line phase, the region which the mobile terminal (MT) belongs to is estimated according to the MDs firstly. Then, in estimated small dimensional regions, MT's coordinates are calculated byKNN positioning method for efficiency purpose. However, for the regions including singular RPs, ANN method is utilized because ofits great pattern matching ability. Furthermore, compared with other typical indoor positioning methods, feasibility and effectiveness of this hybrid KNN/ANN method are also verified by the experimental results in static and tracking situations.

Coordinated Resource Allocation for Satellite-Terrestrial Coexistence Based on Radio Maps

Coexistence of satellite and terrestrial wireless communication systems in the same frequency band is quite promising for addressing the challenge of spectrum scarcity. To cope with the inevitable inter-system interference, radio resource allocation at both sides should be carefully re-optimized. In this paper, we focus on a scenario where a satellite communication system and a terrestrial distributed antenna system(DAS) coexist via spectrum sharing. We particularly utilize the radio map(RM) to reduce the system overhead for channel acquisition. Based on the large-scale channel state information at the transmitter(CSIT), which is derived from the RM, we propose an optimized power allocation scheme to improve the achievable sum rate of the terrestrial system. For the satellite side, an opportunistic user scheduling scheme is presented, to reduce the harmful leakage interference to the terrestrial mobile users. Simulation results demonstrate that the proposed RM-based coordination scheme can significantly promote the performance of satellite terrestrial coexistence, although the small-scale channel fading has been ignored in the formulated optimization.

An effective indoor radio map construction scheme based on crowdsourced samples

The crowdsourcing-based WLAN indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps.Aiming at the problem of the inaccurate location annotation of the crowdsourced samples,the existing invalid access points(APs)in collected samples,and the uneven sample distribution,as well as the diverse terminal devices,which will result in the construction of the wrong radio map,an effective WLAN indoor radio map construction scheme(WRMCS)is proposed based on crowdsourced samples.The WRMCS consists of 4 main modules:outlier detection,key AP selection,fingerprint interpolation,and terminal device calibration.Moreover,an online localization algorithm is put forward to estimate the position of the online test fingerprint.The simulation results show that the proposed scheme can achieve higher localization accuracy than the peer schemes,and possesses good effectiveness and robustness at the same time.

基于深度强化学习的无人机路径规划与无线电测绘

针对传统无人机轨迹优化设计方法在构建通信模型上具有局限性的问题,本文面向蜂窝连接无人机通信方式,引入一种基于深度强化学习的无人机路径规划与无线电测绘方法。该方法利用扩展后的双深Q网络模型,结合无线电预测网络,生成无人机轨迹并预测由于动作选择而累计的奖励值。此外,基于Dyna框架将实际飞行和模拟飞行相结合,进一步训练双深Q网络模型,从而大大提高学习效率。仿真结果表明,与Direct-RL算法相比,该方法能更有效地利用学习到的覆盖区域概率图,使无人机避开弱覆盖区域,减小飞行时间和预期中断时间的加权和。

基于反障碍距离加权的复杂场景电磁频谱地图构建方法

针对复杂场景中存在电磁波不可穿透的障碍物导致电磁频谱地图(REMs)构建性能不佳、反距离加权(IDW)算法受限于插值邻域的人工选择等问题,该文提出一种基于Voronoi图的反障碍距离加权(VIODW)的复杂场景电磁频谱地图构建算法。该算法通过创建包含障碍物的Voronoi图,为每一个待插值点自适应选定插值邻域用于电磁频谱数据构建,并利用任意角度路径寻优(ANYA)算法计算得到待插值点与插值邻域内每个监测站点之间的障碍距离,最后以障碍距离的反幂次作为权重加权获得待插值点处的电磁频谱数据,实现高精度的复杂场景电磁频谱地图构建。理论分析和仿真结果表明,该方法具有良好的构建精度,能够准确拟合出电磁波在复杂场景中的功率分布情况,为复杂场景下电磁频谱地图高精度构建提供了一种有效方法。

基于切比雪夫混沌映射和PUF的RFID三方认证协议

针对射频识别(RFID)三方认证协议存在的安全需求和资源开销的平衡问题,利用切比雪夫多项式的半群性质以及混沌性质提出了一个基于切比雪夫混沌映射和物理不可克隆函数(PUF)的RFID三方认证协议:使用切比雪夫混沌映射来实现标签、阅读器和服务器三方共享秘密;使用随机数实现协议每轮会话的新鲜性以抵抗重放攻击,同时也实现了阅读器与标签的匿名性;使用PUF函数实现标签本身的安全认证以及抵抗物理克隆攻击。安全分析表明,该协议能有效抵抗追踪、重放、物理克隆和去同步攻击等多种恶意攻击,使用BAN逻辑分析方法和Scyther工具验证了其安全性。与近期协议对比分析表明,该协议弥补了同类RFID协议的安全缺陷,在满足各种安全属性需求的同时尽量平衡硬件开销,契合了RFID硬件资源受限的处境,适用于RFID三方认证场景。

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algo- rithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.

Cognitive radio resource allocation based on coupled chaotic genetic algorithm

A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.

Space-Time Correlation Based Fast Regional Spectrum Sensing in Cognitive Radio

In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.

Fast Converging Generalized Turbo Decoding Scheme with Enhanced Throughput for Mobile Radio

The use of turbo codes enhances the data transmission efficiency and optimizes the performance of a communication system over wireless fading channels. In this paper, we present a brief overview of the various components of the turbo coding scheme, analyze the complexities of the most popular turbo decoding algorithms, and discuss the various implementation methods of the maximum a posteriori (MAP) algorithm. The paper considers the well-known log-MAP decoding algorithm by a linear approximation of the correction function used by the max* operator. We propose a generalized decoding scheme that optimizes the existing MAP algorithm for faster convergence and better throughput on the basis of varying channel conditions. The proposed scheme of decoding reduces complexity and enhances the throughput with only a negligible loss in BER performance.

电磁频谱多维态势压缩测绘技术研究进展

在日益复杂的电磁频谱环境中,精准获取完备的频谱态势信息是做出准确频谱决策的重要前提。首先,介绍了频谱测绘并对比了其与频谱感知的主要区别。接着,综述了现有频谱态势生成方法的研究现状。然后,提出了异构性、大尺度缺失、动态性、环境复杂性等挑战下的多维频谱态势压缩测绘技术研究工作,有效弥补了传统频谱态势生成方法忽略频谱态势感知过程而导致的频谱测绘框架不完整性,该研究可进一步为提升频谱利用效率、增强频谱安全维护以及强化军事电磁对抗的决策提供更精准的指导。最后,对未来频谱压缩测绘的发展趋势进行了展望。

基于边缘智能感知的无人机空间航迹规划方法

随着海量用频设备的涌现,无人机执行任务的电磁环境愈加复杂,对无人机认知环境和自主避障能力提出了更高的要求。鉴于此,提出了一种基于边缘智能感知的无人机空间航迹规划方法。首先,提出了一个基于边缘智能感知的无人机航迹规划框架,通过边缘服务器、传感器终端和无人机的协同通信与计算,提高无人机的环境感知和自主避障能力;其次,提出了一种基于深度确定性策略梯度(Deep Deterministic Policy Gradient,DDPG)算法优化的人工势场方法,避免无人机航迹规划陷入局部最小值点,同时行能耗;最后,在静态和动态干扰环境中对所提算法进行仿真实验,结果表明,与现有航迹规划方法相比,所提方法可以优化无人机的飞行航迹和传输数据速率,在静态和动态干扰环境中,无人机飞行能耗分别降低5.59%和11.99%,传输速率分别提高7.64%和16.52%,显著提高了无人机的通信稳定性和对复杂电磁环境的适应性。

基于余弦-指数非线性混沌映射的射频隐身抗分选信号设计技术

射频隐身是当前雷达领域的研究热点之一,抗分选信号是一种主要的射频隐身信号。研究抗分选信号的设计原理,并以此为基础在充分考虑分选算法的容差限制后,提出一种运用余弦-指数新型非线性混沌映射调制产生强随机、宽间隔脉冲重复间隔信号的方法。然后对新型混沌映射的随机性、复杂性、平衡性和所设计信号的抗分选性能进行仿真。研究结果表明,所设计的新型混沌映射较传统经典的一维映射有更强的随机性、更高的复杂性,设计信号与脉冲重复间隔随机抖动信号相比具有良好的抗分选性能。

基于地图重构的蜂窝连接无人机在线路径规划方法

为了减少无人机在复杂实时信道状态下与地面蜂窝网络的连接中断,并且降低无人机在飞行任务过程中的能量消耗,针对蜂窝连接无人机系统,提出了一种基于无线电地图重构的无人机在线路径规划方法。无人机首先从目标区域中稀疏采样并计算采样点中断概率,随后利用基于深度图像先验的算法对无线电地图进行重构。基于重构的无线电地图,以最小化无人机任务执行的能耗为目标,建立了无人机路径规划优化问题,并提出了一种基于深度强化学习的路径规划算法进行求解。仿真结果表明,所提出方法能够在确保无人机与蜂窝连接性的前提下减少无人机的飞行能耗。

基于改进混沌映射和WFRFT的射频隐身信号设计

为了提高雷达射频隐身信号的低截获性能,提出了改进一维混沌映射和加权分数阶傅里叶变换结合的方法,设计一种射频隐身信号.该信号利用改进一维混沌映射脉内调频和脉间调相,对调制后的复合信号加权分数阶傅里叶变换,分析了该信号的相关特性、功率谱以及模糊函数,同时与其他2种混沌信号对比.仿真结果表明,该复合调制信号自相关PSL为-81.27 dB,ISL为-72.08 dB,信号平均功率为-34.41 dB,并且模糊函数近似为"图钉型",该信号具有较好的低截获和抗识别性能,射频隐身性能良好.

Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

A robust radio map is essential in implementing a fingerprint-based indoor positioning system(IPS).However,the offline site survey to manually construct the radio map is time-consuming and labour-intensive.Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys.This paper presents a novel fingerprint interpolator using a multi-path loss model(MPLM)to create the virtual fingerprints from the collected sample data based on different signal paths from different access points(APs).Based on the historical signal data,the poor signal paths are identified using their standard deviations.The proposed method reduces the positioning errors by smoothing out the wireless signal fluctuations and stabilizing the signals for those poor signal paths.By consideringmultipath signal propagations from different APs,the inherent noise from these signal paths can be alleviated.Firstly,locations of the signal data with standard deviations higher than the threshold are identified.The new fingerprints are then generated at these locations based on the proposed M-PLM interpolation function to replace the old fingerprints.The proposed technique interpolates virtual fingerprints based on good signal paths with more stable signals to improve the positioning performance.Experimental results show that the proposed scheme enhances the positioning accuracy by up to 44%compared to the conventional interpolation techniques such as the Inverse DistanceWeighting,Kriging,and single Path LossModel.As a result,we can overcome the site survey problems for IPS by building an accurate radio map with more reliable signals to improve indoor positioning performance.

Deep Learning-Based Radio Map for MIMO-OFDM Downlink Precoding

Radio map is an advanced technology that mitigates the reliance of multiple-input multiple-output(MIMO)beamforming on channel state information(CSI).In this paper,we introduce the concept of deep learning-based radio map,which is designed to be generated directly from the raw CSI data.In accordance with the conventional CSI acquisition mechanism of MIMO,we first introduce two baseline schemes of radio map,i.e.,CSI prediction-based radio map and throughput predictionbased radio map.To fully leverage the powerful inference capability of deep neural networks,we further propose the end-to-end structure that outputs the beamforming vector directly from the location information.The rationale behind the proposed end-to-end structure is to design the neural network using a task-oriented approach,which is achieved by customizing the loss function that quantifies the communication quality.Numerical results show the superiority of the task-oriented design and confirm the potential of deep learning-based radio map in replacing CSI with location information.

基于多传感器信息融合的机器人导航与定位技术研究

移动机器人已经服务于各个领域,作为室内移动机器人服务的前提是实现机器人精准定位。文章介绍了一种射频识别(Radio Frequency IDentification,RFID)和即时定位与地图构建(Simultaneous Localization and Mapping,SLAM)多传感器融合库存盘点机器人精准导航与定位的方法。通过读取布置在环境中的RFID标签,获取机器人的大概位置信息和准确运动状态,然后结合SLAM技术实现精准定位。实验结果表明,该方法可以使库存盘点机器人精准定位,且系统搭建简单、成本低,对室内机器人定位具有参考价值。