Optimal sensor rules and unified fusion rules for multisensor multi-hypothesis network decision systems with channel errors

In this paper, for general jointly distributed sensor observations, we present optimal sensor rules with channel errors for a given fusion rule. Then, the unified fusion rules problem for multisensor multi-hypothesis network decision systems with channel errors is studied as an extension of our previous results for ideal channels, i.e., people only need to optimize sensor rules under the proposed unified fusion rules to achieve global optimal decision performance. More significantly, the unified fusion rules do not depend on distributions of sensor observations, decision criterion, and the characteristics of fading channels. Finally, several numerical examples support the above analytic results and show some interesting phenomena which can not be seen in ideal channel case.     

Integrated optimization methods in multisensor decision and estimation fusion

This paper presents a significant integrated optimization point of view behind the following three successful decision and estimation fusion results: 1) a unified fusion rule for networked sensor decision systems; 2) optimal sensor data quantization for estimation fusion and 3) integrated multi-target data association tracking systems. More precisely speaking, the integrated optimization method in 1) derives a unified objective function optimizing only sensor rules given a unified fusion rule; the method in 2) derives a unified objective function optimizing both the sensor quantization rule and the final estimation in the MSE sense, and the method in 3) integrates all associated targets and their valid observations into a whole random measurement matrix dynamic system so that the optimal random matrix Kalman filtering can be applied to estimate the states of all associated targets.     

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.     

Using dynamic time warping for online temporal fusion in multisensor systems

Sensor fusion is concerned with gaining information from multiple sensors by fusing across raw data, features or decisions. Traditionally these fusion processes only concern fusion at specific points in time. However recently, there is a growing interest in inferring the behavioural aspects of environments or objects that are monitored by multisensor systems, rather than just their states at specific points in time. In order to infer environmental behaviours, it may be necessary to fuse data acquired from (i) geographically distributed sensors at specific points of time and (ii) specific sensors over a period of time. Fusing multisensor data over a period of time (also known as Temporal fusion) is a challenging task, since the data to be fused consists of complex sequences that are multi-dimensional, multimodal, interacting, and time-varying in nature. Additionally, performing temporal fusion efficiently in real-time is another challenge due to the large amounts of data to be fused. To address this issue, we propose a robust and efficient framework that uses dynamic time warping (DTW) as the core recognizer to perform online temporal fusion on either the raw data or the features. We evaluate the performance of the online temporal fusion system on two real world datasets: (1) accelerometer data acquired from performing two hand gestures, and (2) a benchmark dataset acquired from carrying a mobile device and performing the predefined user scenarios. Performance results of the DTW-based system are compared with those of a Hidden Markov Model (HMM) based system. The experimental results from both datasets demonstrate that the proposed system outperforms HMM based systems, and has the capability to perform online temporal fusion efficiently and accurately in real-time.     

An efficient algorithm for optimal linear estimation fusion in distributed multisensor systems

Under the assumption of independent observation noises across sensors, Bar-Shalom and Campo proposed a distributed fusion formula for two-sensor systems, whose main calculation is the inverse of submatrices of the error covariance of two local estimates instead of the inverse of the error covariance itself. However, the corresponding simple estimation fusion formula is absent in a general distributed multisensor system. In this paper, an efficient iterative algorithm for distributed multisensor estimation fusion without any restrictive assumption on the noise covariance (i.e., the assumption of independent observation noises across sensors and the two-sensor system, and the direct computation of the Moore-Penrose generalized inverse of the joint error covariance of local estimates are not necessary) is presented. At each iteration, only the inverse or generalized inverse of a matrix having the same dimension as the error covariance of a single-sensor estimate is required. In fact, the proposed algorithm is a generalization of Bar-Shalom and Campo's fusion formula and reduces the computational complexity significantly since the number of iterative steps is less than the number of sensors. An example of a three-sensor system shows how to implement the specific iterative steps and reduce the computational complexities.     

Active and dynamic information fusion for multisensor systems with dynamic Bayesian networks.

Many information fusion applications are often characterized by a high degree of complexity because: (1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; (2) decisions must be made efficiently; and (3) the world situation evolves over time. To address these issues, we propose an information fusion framework based on dynamic Bayesian networks to provide active, dynamic, purposive and sufficing information fusion in order to arrive at a reliable conclusion with reasonable time and limited resources. The proposed framework is suited to applications where the decision must be made efficiently from dynamically available information of diverse and disparate sources.     

Cognitively-engineered multisensor image fusion for military applications

The fusion of imagery from multiple sensors is a field of research that has been gaining prominence in the scientific community in recent years. The technical aspects of combining multisensory image information have been and are currently being studied extensively. However, the cognitive aspects of multisensor image fusion have not received as much attention. In this study, a concurrent protocol procedure was used to identify how humans fuse information from visible and infrared imagery in low- and high-stress situations. The results of the concurrent protocol were used to develop operator function models, which were then used to develop preliminary design points for fusing multisensor image data. Fused image data were then used in a combat/target identification simulation, and operator performance, accuracy, and speed were compared with results obtained using unfused data. The results show that the model is an accurate depiction of how humans interpret information from multiple disparate sensors in this particular scenario, and that the algorithm design points show promise for assisting fighter pilots in quicker and more accurate target identification.     

Heterogeneous multisensor fusion for mapping dynamic environments

In this paper, we propose a heterogeneous multisensor fusion algorithm for mapping in dynamic environments. The algorithm synergistically integrates the information obtained from an uncalibrated camera and sonar sensors to facilitate mapping and tracking. The sonar data is mainly used to build a weighted line-based map via the fuzzy clustering technique. The line weight, with confidence corresponding to the moving object, is determined by both sonar and vision data. The motion tracking is primarily accomplished by vision data using particle filtering and the sonar vectors originated from moving objects are used to modulate the sample weighting. A fuzzy system is implemented to fuse the two sensor data features. Additionally, in order to build a consistent global map and maintain reliable tracking of moving objects, the well-known extended Kalman filter is applied to estimate the states of robot pose and map features. Thus, more robust performance in mapping as well as tracking are achieved. The empirical results carried out on the Pioneer 2DX mobile robot demonstrate that the proposed algorithm outperforms the methods a using homogeneous sensor, in mapping as well as tracking behaviors.     

A process distribution approach for multisensor data fusion systems based on geographical dataspace partitioning

In this work, we present a new approach to distributed sensor data fusion (SDF) systems in multitarget tracking, called TSDF (Tessellated SDF), centered around a geographical partitioning (tessellation) of the data. A functional decomposition divides SDF into components that can be assigned to processing units, parallelizing the processing. The tessellation implicitly defines the set of tracks potentially yielding correlations with the sensor plots (observations) in a tile. Some tracks may occur as correlation candidates for multiple tiles. Conflicts caused by correlations of such tracks with plots in different tiles, are resolved by combining all involved tracks and plots into independent data association problems. The benefit of the TSDF approach to a clustering-based process distribution is independence of the problem space, which yields better scalability and manageability characteristics. The TSDF approach allows scaling in more than one way. It allows SDF for single sensor, multiple sensors on a single platform, and even for multiple sensors on multiple platforms. It also provides the flexibility to scale the processing to the size of the problem. This enables a better control of the throughput, to meet various timing constraints.     

多传感器信息融合概述及其应用

多传感器数据融合广泛应用于自动目标识别、工业过程控制、遥感、医疗诊断、图像处理、模式识别等领域。介绍了多传感器信息融合技术的概念,对信息融合的算法进行了概述,提出了基于粗糙集理论的多源信息融合算法,最后对多传感器融合技术的研究动向进行了展望。     

一种多传感器数据自适应空间分级融合算法

当采用分布在不同空间位置上的多传感器观测值对测量噪声干扰下的参数进行融合估计时,被测量的空间分散性对融合结果影响较大.针对该问题,以自适应加权融合算法为基础,提出了自适应空间分级融合算法,并给出了误差分析和应用方法.该算法将融合过程分解为两次寻优,第1次是局部空间的自适应加权寻优,第2次是在全局空间内的融合寻优.计算机仿真结果表明:该算法在估计空间分布不均匀的被测量时优于自适应加权融合算法.     

Redundant versus orthogonal wavelet decomposition for multisensor image fusion

The wavelet decomposition has become an attractive tool for fusing multisensor images. Usually, the input images are decomposed with an orthogonal wavelet in order to extract features, which are combined through an appropriate fusion rule. The fused image is then reconstructed by applying the inverse wavelet transform. In this paper, we investigate the use of the nonorthogonal (or redundant) wavelet decomposition as an alternative approach for feature extraction. By using test and remote sensing images, various fusion rules are considered and the detailed comparison indicates the superiority of this approach compared to the standard orthogonal wavelet decomposition for image fusion.     

多传感器数据的统计融合方法

在多传感数据融合过程中 ,各传感器的可靠程度的确定是至关重要的。利用统计方法理论 ,将各传感器的可靠程度模糊化 ,进而给出各传感器的综合支持程度指标 ,并在此指标基础上给出多传感器数据的融合结果。该方法计算简便 ,其结论较为稳定     

Minimal representation multisensor fusion using differentialevolution

Fusion of information from multiple sensors is required for planning and control of robotic systems in complex environments. The minimal representation approach is based on an information measure as a universal yardstick for fusion and provides a framework for integrating information from a variety of sources. In this paper, we describe the principles of minimal representation multisensor fusion and evaluate a differential evolution approach to the search for solutions. Experiments in robot manipulation using both tactile and visual sensing demonstrate that this algorithm is effective in finding useful and practical solutions to this problem for real systems. Comparison of this differential evolution algorithm with more traditional genetic algorithms shows distinct advantages in both accuracy and efficiency     

Unified methodology of neural analysis in decision support systems built for pharmaceutical technology

The objective of this study was to create universal methodology of artificial neural networks (ANNs) application in construction of decision support systems designed for various dosage forms. Two different dosage forms (solid dispersions and microemulsions) were modeled with use of the same methodology, software and hardware environments. Completely different models prepared confirmed their generalization ability both for solid dosage forms (solid dispersions) and liquid dosage forms (microemulsions). ME_expert and SD_expert systems basing on the neural expert committees were created. In the pilot study their application allowed for appropriate choice of qualitative and quantitative composition of particular pharmaceutical formulation. It was also proposed that ME_expert and SD_expert might provide in silico formulation procedures. Unified methodology of neural modeling in pharmaceutical technology was confirmed to be effective in providing valuable tools for pharmaceutical product development.