A new multiple decisions fusion rule for targets detection in multiple sensors distributed detection systems with data fusion

Currently, multiple sensors distributed detection systems with data fusion are used extensively in both civilian and military applications. The optimality of most detection fusion rules implemented in these systems relies on the knowledge of probability distributions for all distributed sensors. The overall detection performance of the central processor is often worse than expected due to instabilities of the sensors probability density functions. This paper proposes a new multiple decisions fusion rule for targets detection in distributed multiple sensor systems with data fusion. Unlike the published studies, in which the overall decision is based on single binary decision from each individual sensor and requires the knowledge of the sensors probability distributions, the proposed fusion method derives the overall decision based on multiple decisions from each individual sensor assuming that the probability distributions are not known. Therefore, the proposed fusion rule is insensitive to instabilities of the sensors probability distributions. The proposed multiple decisions fusion rule is derived and its overall performance is evaluated. Comparisons with the performance of single sensor, optimum hard detection, optimum centralized detection, and a multiple thresholds decision fusion, are also provided. The results show that the proposed multiple decisions fusion rule has higher performance than the optimum hard detection and the multiple thresholds detection systems. Thus it reduces the loss in performance between the optimum centralized detection and the optimum hard detection systems. Extension of the proposed method to the case of target detection when some probability density functions are known and applications to binary communication systems are also addressed.     

不同频率激励信号下土壤水分传感器性能试验

激励信号频率是影响高频电容式土壤水分传感器性能的重要因素。利用去离子水和2—异丙氧基乙醇(2—isoproxyethanol)或二氧六环(dioxane)2种溶液混合,配制了一系列等效土壤体积含水率为0.9%~51.8%的待测介电溶液来替代土样。从与土壤含水率的函数关系、温度变异性2个方面,分别对激励信号频率为40,50,60,70,80,90,100 MHz的7种土壤水分传感器进行了性能测试与分析。结果表明:7种频率的传感器的输出电压均与土壤体积含水率呈线性负相关,其相关系数R2均大于0.94;激励信号频率不影响传感器输出电压的温度变异性,温差是影响温度变异性的主要因素,其最大变异率均小于4%。试验结果可为设计高频电容式土壤水分传感器时选择激励信号频率提供依据。     

A dielectric-based combined horizontal sensor for on-the-go measurement of soil water content and mechanical resistance

Precision agriculture is drawing widespread attention and increasing interest in the use of on-the-go sensors to extract soil and plant information. This new trend is evident from an increased number of (tillage) studies concerning the development of sensors for measuring physical soil properties. In this study, a new dielectric-based horizontal sensor was designed and stationary calibrated in pots for measurement of volumetric soil water content. Subsequently, the sensor was combined with a load cell and installed on a tine with thickness of 25 mm, and its dynamic performance was assessed. The load cell behind the tine measures horizontal soil mechanical resistance. In order to evaluate the on-the-go performance of this combined sensor, two experiments were carried out in a soil bin, with the purpose of (i) measuring soil water content along a transect of longitudinally variable water contents, (ii) investigating the difference in horizontal resistance force measured by either the combined (horizontal force and water content) or a single sensor (horizontal force only). The stationary calibration results showed that quadratic equations with coefficients of determination (R²) of 0.989 and 0.918 could be fitted to the data points obtained from measurements in soil bulk densities of 1.5 and 1.2 g cm⁻³, respectively. The results of the soil bin experiments indicated that the response of the dielectric sensor to soil water content variations was reasonable, but that the output voltage of the sensor should be amplified for a better sensitivity and resolution. The presented combined sensor can provide useful information on soil physical properties towards site specific applications in soils.     

On the performance of distributed Neyman-Pearson detection systems

The performance of a distributed Neyman-Pearson detection system is considered. We assume that the decision rules of the sensors are given and that decisions from different sensors are mutually independent conditioned on both hypotheses. The purpose of decision fusion is to improve the performance of the overall system, and we are interested to know under what conditions can a better performance be achieved at fusion center, and under what conditions cannot. We assume that the probabilities of detection and false alarm of the sensors can be different. By comparing the probability of detection at fusion center with that of each of the sensors, with the probability of false alarm at fusion center constrained equal to that of the sensor, we give conditions for a better performance to be achieved at fusion center     

LSFR算法在多传感器分布式检测中的优化研究

在多传感器分布式检测系统中,常规融合规则算法要求传感器误差概率已知,且系统中传感器和融合中心同时优化存在一定困难.提出最小二乘融合规则(LSFR)算法,算法不依赖噪声环境稳定性以及传感器的虚警概率与检测概率,融合中心根据各个传感器的硬决策,得到全局的硬决策,并在传感器和融合中心处理达到最优时,获得最佳全局性能.仿真结果表明:对比似然比融合决策算法与Neyman Pearson融合规则(NPFR)算法,LSFR算法全局检测概率显著提高,且在不同数量规模传感器和更多类型的分布式检测系统中具有较好兼容性.     

Feature-level and decision-level fusion of noncoincidently sampledsensors for land mine detection

We present and compare methods for feature-level (predetection) and decision-level (postdetection) fusion of multisensor data. This study emphasizes fusion techniques that are suitable for noncommensurate data sampled at noncoincident points. Decision-level fusion is most convenient for such data, but it is suboptimal in principle, since targets not detected by all sensors will not obtain the full benefits of fusion. A novel algorithm for feature-level fusion of noncommensurate, noncoincidently sampled data is described, in which a model is fitted to the sensor data and the model parameters are used as features. Formulations for both feature-level and decision-level fusion are described, along with some practical simplifications. A closed-form expression is available for feature-level fusion of normally distributed data and this expression is used with simulated data to study requirements for sample position accuracy in multisensor data. The performance of feature-level and decision-level fusion algorithms are compared for experimental data acquired by a metal detector, a ground-penetrating radar, and an infrared camera at a challenging test site containing surrogate mines. It is found that fusion of binary decisions does not perform significantly better than the best available sensor. The performance of feature-level fusion is significantly better than the individual sensors, as is decision-level fusion when detection confidence information is also available (“soft-decision” fusion)     

具有多传感器的CPS系统的攻击检测

信息物理系统（cyber-physical systems，简称CPS）是基于环境感知实现计算、通信与物理元素紧密结合的下一代智能系统，广泛应用于安全攸关的系统和工业控制等领域.信息技术与物理世界的相互作用使得CPS容易受到各种恶意攻击，从而破坏其安全性.主要研究存在瞬态故障的CPS中传感器的攻击检测问题.考虑具有多个传感器测量相同物理变量的系统，其中一些传感器可能受到恶意攻击并提供错误的测量.此外，使用抽象传感器模型，每个传感器为控制器提供一个真实值的可能间隔.已有的用于检测传感器被恶意攻击的方法是保守的.当专业攻击者在一段时间内轻微地或不频繁地操纵传感器的输出时，现有方法很难捕获到攻击，如隐身攻击.为了解决这个问题，设计了一种基于融合间隔和历史测量的传感器攻击检测方法.该方法首先为不同的传感器构建不同的故障模型，使用系统动力学方程把历史测量融入到攻击检测方法中，从不同的方面分析传感器的测量.另外，利用历史测量和融合间隔解决了两个传感器的测量相交时是否存在故障的问题.该方法的核心思想是利用传感器之间的成对不一致关系检测和识别攻击.从EV3地面车辆上获得真实的测量数据来验证算法的性能.实验结果表明，所提出的方法优于现有方法，对各种攻击类型都有较好的检测和识别性能，特别是对于隐身攻击，检测率和识别率大约提高了90%以上.     

Unified fusion rules for multisensor multihypothesis network decision systems

In this paper, we present a fusion rule for distributed multihypothesis decision systems where communication patterns among sensors are given and the fusion center may also observe data. It is a specific form of the most general fusion rule, independent of statistical characteristics of observations and decision criteria, and thus, is called a unified fusion rule of the decision system. To achieve globally optimum performance, only sensor rules need to be optimized under the proposed fusion rule for the given conditional distributions of observations and decision criterion. Following this idea, we present a systematic and efficient scheme for generating optimum sensor rules and hence, optimum fusion rules, which reduce computation tremendously as compared with the commonly used exhaustive search. Numerical examples are given, which support the above results and provide a guideline on how to assign sensors to nodes in a signal detection networks with a given communication pattern. In addition, performance of parallel and tandem networks is compared.     

分布式贝叶斯检测融合优化算法及其计算机仿真

该文研究了利用分布式多传感器获得全局决策的分布式信号检测问题。在这种检测系统中各传感器将其各自关于观测对象的决策传送至融合中心,融合中心根据融合规则给出全局决策。研究重点是基于贝叶斯准则的分布式并联检测融合系统的数据融合理论,给出了使系统全局最优的融合规则和传感器决策规则,提出了对融合规则和传感器决策规则进行优化计算的非线性高斯一赛德尔算法,具体讨论了两相同传感器、两个不同传感器和三个相同传感器在具有独立观测时的数据融合问题。给出了利用本文所提算法对上述几种情况进行计算机仿真的仿真实例。仿真结果表明：融合系统的性能相对传感器有显著改善,采用三个相同传感器的融合系统,其贝叶斯风险下降了26．5％。     

Visibility of road hazards in thermal, visible, and sensor-fused night-time imagery

Sensor fusion combines the output of multiple imaging sensors within a single composite display. Ideally, a fused image will retain important spatial information provided by individual input images, and will convey useful spatial or chromatic emergent information derived from the contrast between input images. The present experiment assessed the potential benefits of sensor fusion as a method of enhancing drivers' night-time detection of road hazards. Observers were asked to detect a pedestrian within thermal and visible images of a night-time scene, and within chromatic and achromatic renderings created by sensor fusion of grayscale thermal and visible images. Results indicated that fusion can both improve spatial image content, and can effectively embellish spatial content with emergent chromatic information. The benefits of both sensor fusion and of color rendering, however, were inconsistent, varying substantially with quality of input images submitted for fusion.     

New results on the performance of distributed Bayesian detectionsystems

The purpose of decision fusion in a distributed detection system is to achieve a performance that is better than that of local detectors (or sensors). We consider a distributed Bayesian detection system consisting of n sensors and a fusion center, in which the decision rules of the sensors have been given and the decisions of different sensors are conditionally independent. We assume that the decision rules of the sensors can be optimum or suboptimum, and that the probabilities of detection and false alarm of the sensors can be different. Theoretical analysis on the performance of this fusion system is carried out. Conditions for the fusion system to achieve a global risk that is smaller than local risks are obtained     

无线传感器网络低能耗协作频谱感知方法

针对无线传感器网络能量受限问题,以双门限能量检测为基础,以最小化系统感知能耗为目标,提出了一种适合无线传感器网络的休眠与审核相结合的频谱感知方法。在获得目标检测性能条件下,该方法让部分传感器负责频谱感知工作,其他处于休眠状态,只让有准确感知结果的传感器发送1 bit感知结果到融合中心。理论分析和仿真结果表明,与传统的能量检测方法相比,该方法既有效减少了系统的感知能耗,又节约了带宽。     

Presentation and description of two classification methods using data fusion based on sensor management

The application of multi-sensor fusion, which aims at recognizing a state among a set of hypotheses for object classification, is of major interest with regard to the performance improvement brought by the sensor complementarity. Nevertheless, this needs to take into account the most accurate information and take advantage of the statistical learning of the previous measurements acquired by sensors. When previous learning is not representative of real measurements provided by the sensors, the classical probabilistic fusion methods lack performance. The Dempster–Shafer theory is then introduced to face this disadvantage by integrating further information which is the context of the sensor acquisitions. In this paper, we propose a model formalism for the sensor reliability in a context that leads to two methods of integration when all the hypotheses, associated to the objects of the scene acquired by sensors, are previously learned: the first one models the integration of this further information in the fusion rule as degrees of trust and the second models the sensor reliability directly as probability mass. These two methods are based on the theory of fuzzy events. Simulations of typical cases are developed in order to define the respective validity domains of these two methods. Afterwards, we are interested in the development of these two methods in the case where the previous learning is unavailable for an object and a global method of contextual information integration can be deduced.     

Combining Intelligent Techniques for Sensor Fusion

Mobile robots rely on sensor data to build a representation of their environment. However, sensors usually provide incomplete, inconsistent or inaccurate information. Sensor fusion has been successfully employed to enhance the accuracy of sensor measures. This work proposes and investigates the use of Artificial Intelligence techniques for sensor fusion. Its main goal is to improve the accuracy and reliability of the distance measure between a robot and an object in its work environment, based on measures obtained from different sensors. Several Machine Learning algorithms are investigated to fuse the sensors data. The best model generated by each algorithm is called estimator. It is shown that the employment of estimators based on Artificial Intelligence can improve significantly the performance achieved by each sensor alone. The Machine Learning algorithms employed have different characteristics, causing the estimators to have different behaviors in different situations. Aiming to achieve an even more accurate and reliable behavior, the estimators are combined in committees. The results obtained suggest that this combination can further improve the reliability and accuracy of the distances measured by the individual sensors and estimators used for sensor fusion.     

基于贝叶斯数据融合的轨道车辆pt100温度检测系统设计

Pt100温度传感器是轨道车辆温度数据实时监测的常用传感器,其性能的好坏影响着人们对轨道车辆运行状态的判断.为了准确判断出存在故障的传感器,检测系统应包含精准的信号采集系统和有效的数据融合处理方法.首先对轨道车辆Pt100温度传感器信号采集系统的放大电路、A/D转换电路等进行设计,采集系统采用分段非线性多项式拟合算法,得到不同温度区间上的标度变换表达式;将一种基于贝叶斯估计算法的多传感器测量数据融合方法应用于采集信号的处理,判断轨道车辆Pt100温度传感器是否存在故障.研究结果表明,采用上述过程测量温度精准,融合误差小,能够有效筛选出发生故障的传感器.     

Optimal decision f usion given sens or rules

When all the rules of sensor decision are known ,the optimal distributed decision fusion ,which relies only on the joint conditional probability densities , can be derived for very general decision systems. They include those systems with interdependent sensor observations and any network structure. It is also valid for m-ary Bayesian decision problems and binary problems under the Neyman- Pearson criterion. Local decision rules of a sensor with communication from other sensors that are optimal for the sensor itself are also presented ,which take the form of a generalized likelihood ratio test . Numerical examples are given to reveal some interesting phenomena that communication between sensors can improve performance of a senor decision ,but cannot guarantee to improve the global fusion performance when sensor rules were given before fusing.     

介电溶液测试土壤水分传感器性能的实证研究

利用土壤等效介电溶液代替土壤测试与评估土壤水分传感器的性能,具有便捷、快速的优点。为验证传感器在介电溶液中的测试结果是否与实际土壤中一致,利用去离子水和2—异丙氧基乙醇(2-isoproxyethanol)或二氧六环(dioxane)2种溶液混合,配制了一系列等效土壤体积含水率为0.9%~51.8%的待测介电溶液,同时取广州地区的典型红壤和雷州半岛的典型砖红壤,配制一系列与介电溶液等效含水率相对应的土样,分别从电气特性、与土壤含水率的函数关系、温度变异性和稳定性4个方面测试传感器的性能,并将介电溶液中的测试结果与2种土样中比较。实验结果表明:介电溶液中的测试结果与红壤和砖红壤中基本上一致,介电溶液在某种程度上可以代替土样进行土壤水分传感器的性能测试。     

Optimal decision fusion given sensor rules

When all the rules of sensor decision are known,the optimal distributed decision fusion,which relies only on the joint conditional probability densities, can be derived for very general decision systems. They include those systems with interdependent sensor observations and any network structure. It is also valid for m-ary Bayesian decision problems and binary problems under the Neyman-Pearson criterion. Local decision rules of a sensor withfrom other sensors that are optimal for the sensor itself are also presented, which take the form of a generalized likelihood ratio test. Numerical examples are given to reveal some interesting phenomem that communication between sensors can improve performance of a senor decision,but cannot guarantee to improve the global fusion performance when sensor rules were given before fusing.     

基于信号波形幅度检测放大技术的电容式土壤水分传感器研究

土壤水分量测对于农田系统的评估和选择、水资源的合理利用是十分重要的。本文根据土壤水分与土壤相对介电常数的关系,基于信号波形幅度检测放大技术设计了一种电容式土壤水分传感器。在标准溶液样品、粘壤土、砂壤土、粘土土样中对本文提出的传感器进行测试。实验结果显示,传感器输出电压与被测介质含水量有良好的线性关系（R2=0.81~0.99）,传感器灵敏度受土质影响明显,实际应用中时需要根据土质进行校准,传感器功耗低,在2.5V供电电压下功耗低于8mW。本文设计的传感器可推广至精准农业系统应用中。     

基于主成分分析的多源特征融合故障诊断方法

目前故障诊断的实际应用中,因噪声的千扰,基于单传感器的故障诊断稳定性较差,很难达到满意的诊断精确度。提出了一种多传感器多特征数据融合的故障诊断方法。该方法利用多传感器从不同部位获取同一部件的运行状况,并通过构建多源特征融合模型,提高特征信息的抗干扰性,最后通过融合特征信息来完成部件的故障诊断。在将新方法应用于滚动轴承故障诊断的试验中,可以看到新方法能够获得较好的性能,比基于单传感器故障诊断的精确度更高。