Explicit solutions for some simple decentralized detection problems

A decentralized detection problem is considered in which a number of identical sensors transmit a finite-valued function of their observations to a fusion center which makes a final decision on one of M alternative hypotheses. The authors consider the case in which the number of sensors is large, and they derive (asymptotically) optimal rules for determining the messages of the sensors when the observations are generated from a simple and symmetrical set of discrete distributions. They also consider the tradeoff between the number of sensors and the communication rate of each sensor when there is a constraint on the total communication rate from the sensors to the fusion center. The results suggest that it is preferable to have several independent sensors transmitting low-rate (coarse) information instead of a few sensors transmitting high-rate (very detailed) information. They also suggest that an M-ary hypothesis testing problem can be viewed as a collection of M(M-1)/2 binary hypothesis testing problems. From this point of view the most useful messages (decision rules) are those that provide information to the fusion center that is relevant to the largest possible numbers of these binary hypothesis testing problems     

Efficient multisensor fusion using multidimensional dataassociation

We present the development of a multisensor fusion algorithm using multidimensional data association for multitarget tracking. The work is motivated by a large scale surveillance problem, where observations from multiple asynchronous sensors with time-varying sampling intervals (electronically scanned array (ESA) radars) are used for centralized fusion. The combination of multisensor fusion with multidimensional assignment is done so as to maximize the “time-depth” in addition to “sensor-width” for the number S of lists handled by the assignment algorithm. The standard procedure, which associates measurements from the most recently arrived S-1 frames to established tracks, can have, in the case of S sensors, a time-depth of zero. A new technique, which guarantees maximum effectiveness for an S-dimensional data association (S⩾3), i.e., maximum time-depth (S-1) for each sensor without sacrificing the fusion across sensors, is presented. Using a sliding window technique (of length S), the estimates are updated after each frame of measurements. The algorithm provides a systematic approach to automatic track formation, maintenance, and termination for multitarget tracking using multisensor fusion with multidimensional assignment for data association. Estimation results are presented for simulated data for a large scale air-to-ground target tracking problem     

一种分布式异步航迹融合算法

在多被动传感器目标跟踪中,融合中心处理的信息一般是同步的,然而实际情况并非如此。另外,一些被动传感器只能得到目标的方位信息,无法单独形成有效航迹,这就需要将各传感器数据同步到相同时刻,然后应用同步融合算法。针对被动传感器探测系统,采用传感器到传感器融合和系统到传感器融合的分布式融合结构,并对各局部传感器引入全局反馈,对相关信息采用协方差交叉算法进行处理,完成被动传感器异步数据的融合,仿真结果表明,该算法具有较好的融合效果。     

Practical Fusion of Quantized Measurements via Particle Filtering

Most treatments of decentralized estimation rely on some form of track fusion, in which local track estimates and their associated covariances are communicated. This implies a great deal of communication; and it was recently proposed that by an intelligent quantization directly of measurements, the communication needs could be considerably cut. However, several issues were not discussed. The first of these is that estimation with quantized measurements requires an update with a non-Gaussian distribution, reflecting the uncertainty within the quantization "bin."; In general this would be a difficult task for dynamic estimation, but Markov-chain Monte-Carlo (MCMC, and specifically here particle filtering) techniques appear quite appropriate since the resulting system is, in essence, a nonlinear filter. The second issue is that in a realistic sensor network it is to be expected that measurements should arrive out-of-sequence. Again, a particle filter is appropriate, since the recent literature has reported particle filter modifications that accommodate nonlinear-filter updates based on new past measurements, with the need to refilter obviated. We show results that indicate a compander/particle-filter combination is a natural fit, and specifically that quite good performance is achievable with only 2-3 bits per dimension per observation. The third issue is that intelligent quantization requires that both sensor and fuser share an understanding of the quantization rule. In dynamic estimation this is a problem since both quantizer and fuser are working with only partial information; if measurements arrive out-of-sequence the problem is compounded. We therefore suggest architectures, and comment on their suitabilities for the task. A scheme based on delta-modulation appears to be promising.     

A new distributed constant false alarm rate detector

A new constant false alarm rate (CFAR) test termed signal-plus-order statistic CFAR (S+OS) using distributed sensors is developed. The sensor modeling assumes that the returns of the test cells of different sensors are all independent and identically distributed In the S+OS scheme, each sensor transmits its test sample and a designated order statistic of its surrounding observations to the fusion center. At the fusion center, the sum of the samples of the test cells is compared with a constant multiplied by a function of the order statistics. For a two-sensor network, the functions considered are the minimum of the order statistics (mOS) and the maximum of the order statistics (MOS). For detecting a Rayleigh fluctuating target in Gaussian noise, closed-form expressions for the false alarm and detection probabilities are obtained. The numerical results indicate that the performance of the MOS detector is very close to that of a centralized OS-CFAR and it performs considerably better than the OS-CFAR detector with the AND or the OR fusion rule. Extension to an N-sensor network is also considered, and general equations for the false alarm probabilities under homogeneous and nonhomogeneous background noise are presented.     

Decision fusion rules in multi-hop wireless sensor networks

The decision fusion problem for a wireless sensor network (WSN) operating in a fading environment is considered. In particular, we develop channel-aware decision fusion rules for resource-constrained WSNs where binary decisions from local sensors may need to be relayed through multi-hop transmission in order to reach a fusion center. Each relay node employs a binary relay scheme whereby the relay output is inferred from the channel impaired observation received from its source node. This estimated binary decision is subsequently transmitted to the next node until it reaches the fusion center. Under a flat fading channel model, we derive the optimum fusion rules at the fusion center for two cases. In the first case, we assume that the fusion center has knowledge of the fading channel gains at all hops. In the second case, we assume a Rayleigh fading model, and derive fusion rules utilizing only the fading channel statistics. We show that likelihood ratio (LR) based optimum decision fusion statistics for both cases reduce to respective simple linear test statistics in the low channel signal-to-noise ratio (SNR) regime. These suboptimum detectors are easy to implement and require little a priori information. Performance evaluation, including a study of the robustness of the fusion statistics with respect to unknown system parameters, is conducted through simulations.     

弹载大视场星惯组合深度融合导航技术

为了降低弹载星惯组合（Stellar-INS）飞行中段对调姿观星的要求，提高星惯组合姿态精度，提出了大视场（LFOV）星惯组合深度融合导航方法。小视场（NFOV）星敏感器输出星矢量为主，大视场星敏感器可同时输出姿态和星矢量信息，分别推导了基于星敏感器输出姿态和星矢量信息的观测方程，分析了星矢量和姿态观测方法之间的关联性。建立了包含星惯安装误差、陀螺误差以及初始平台误差角的星惯组合全误差项模型，基于线性卡尔曼滤波给出了深度融合导航方法。开展了数学仿真验证，分析了不同调姿观星路径约束下，大/小视场星惯组合性能差异。结果表明，大视场星惯组合深度融合导航方法不仅可以降低调姿观星约束要求，还可以实现组合姿态性能提升。     

Fault Detection in Dynamic Systems via Decision Fusion

Due to the growing demands for system reliability and availability of large amounts of data, efficient fault detection techniques for dynamic systems are desired. In this paper, we consider fault detection in dynamic systems monitored by multiple sensors. Normal and faulty behaviors can be modeled as two hypotheses. Due to communication constraints, it is assumed that sensors can only send binary data to the fusion center. Under the assumption of independent and identically distributed (1ID) observations, we propose a distributed fault detection algorithm, including local detector design and decision fusion rule design, based on state estimation via particle filtering. Illustrative examples are presented to demonstrate the effectiveness of our approach.     

Optimal Decision Fusion in Multiple Sensor Systems

The problem of optimal data fusion in the sense of the Neyman-pearson (N-P) test in a centralized fusion center is considered. The fusion center receives data from various distributed sensors. Each sensor implements a N-P test individually and independently of the other sensors. Due to limitations in channel capacity, the sensors transmit their decision instead of raw data. In addition to their decisions, the sensors may transmit one or more bits of quality information. The optimal, in the N-P sense, decision scheme at the fusion center is derived and it is seen that an improvement in the performance of the system beyond that of the most reliable sensor is feasible, even without quality information, for a system of three or more sensors. If quality information bits are also available at the fusion center, the performance of the distributed decision scheme is comparable to that of the centralized N-P test. Several examples are provided and an algorithm for adjusting the threshold level at the fusion center is provided.     

Nonstochastic adaptive decision fusion in distributed-detection systems

Implementing the optimal Neyman-Pearson (NP) fusion rule in distributed detection systems requires the sensor error probabilities to be a priori known and constant during the system operation. Such a requirement is practically impossible to fulfil for every resolution cell in a multiflying target multisensor environment. The true performance of the fusion center is often worse than expected due to fluctuations of the observed environment and instabilities of sensor thresholds. This work considers a nonparametric data fusion situation in which the fusion center knows only the number of the sensors, but ignores their error probabilities and cannot control their thresholds. A data adaptive approach to the problem is adopted, and combining P reports from Q independent distributed sensors through a least squares (LS) formulation to make a global decision is investigated. Such a fusion scheme does not entail strict stationarity of the noise environment nor strict invariance of the sensor error probabilities as is required in the NP formulation. The LS fusion scheme is analyzed in detail to simplify its form and determine its asymptotic behavior. Conditions of performance improvement as P increases and of quickness of such improvement are found. These conditions are usually valid in netted radar surveillance systems.     

Joint sensor registration and track-to-track fusion for distributed trackers

Sensor registration deals with the correction of registration errors and is an inherent problem in all multisensor tracking systems. Traditionally, it is viewed as a least squares or a maximum likelihood problem independent of the fusion problem. We formulate it as a Bayesian estimation problem where sensor registration and track-to-track fusion are treated as joint problems and provide solutions in cases 1) when sensor outputs (i.e., raw data) are available, and 2) when tracker outputs (i.e., tracks) are available. The solution to the latter problem is of particular significance in practical systems as band limited communication links render the transmission of raw data impractical and most of the practical fusion systems have to depend on tracker outputs rather than sensor outputs for fusion. We then show that, under linear Gaussian assumptions, the Bayesian approach leads to a registration solution based on equivalent measurements generated by geographically separated radar trackers. In addition, we show that equivalent measurements are a very effective way of handling sensor registration problem in clutter. Simulation results show that the proposed algorithm adequately estimates the biases, and the resulting central-level trucks are free of registration errors.     

Proposed airborne optical fiber asynchronous CDMA systems usingfully optical orthogonal codes for real-time avionic data distributionsand computer interconnects

We report the design of optical fiber asynchronous code-division multiple-access (CDMA) systems using fully optical orthogonal codes (FOOCs), which can be employed to support future airborne real-time communication services. The use of incoherent optical CDMA (IO-CDMA) can result in lower complexity and more reliable implementation than that of the coherent version, so this makes IO-CDMA more adequate for operation in the severe aircraft environment. When FOOCs are used in the IO-CDMA systems, the peaks of all the cross-correlation functions and all the sidelobes of any auto-correlation function are then limited to “1” (i.e., the minimum correlation constraint for incoherent optical processing) in fully asynchronous transmissions. Moreover, proposed systems can effectively support multi-rate and variable-rate data communications with no violation of the minimum correlation constraint and no change of IO-CDMA encoders/decoders. This characteristic can be used to improve the system flexibility and availability. A basic experiment is also demonstrated to verify the characteristics of FOOCs     

Optimal distributed decision fusion

The problem of decision fusion in distributed sensor systems is considered. Distributed sensors pass their decisions about the same hypothesis to a fusion center that combines them into a final decision. Assuming that the sensor decisions are independent of each other for each hypothesis, the authors provide a general proof that the optimal decision scheme that maximizes the probability of detection at the fusion for fixed false alarm probability consists of a Neyman-Pearson test (or a randomized N-P test) at the fusion and likelihood-ratio tests at the sensors     

Nonparametric methods for clutter removal

Methods of clutter rejection are discussed which furnish an inherent counterpart of target tracking and detection algorithms. We describe how nonparametric curve estimation methods reduce the original sensor data to a “signal-plus-noise” model which is well suited for various hypotheses testing and dynamical filtering algorithms. We also verify a “white noise” assumption for the model of residuals     

Blind adaptive decision fusion for distributed detection

We consider the problem of decision fusion in a distributed detection system. In this system, each detector makes a binary decision based on its own observation, and then communicates its binary decision to a fusion center. The objective of the fusion center is to optimally fuse the local decisions in order to minimize the final error probability. To implement such an optimal fusion center, the performance parameters of each detector (i.e., its probabilities of false alarm and missed detection) as well as the a priori probabilities of the hypotheses must be known. However, in practical applications these statistics may be unknown or may vary with time. We develop a recursive algorithm that approximates these unknown values on-line. We then use these approximations to adapt the fusion center. Our algorithm is based on an explicit analytic relation between the unknown probabilities and the joint probabilities of the local decisions. Under the assumption that the local observations are conditionally independent, the estimates given by our algorithm are shown to be asymptotically unbiased and converge to their true values at the rate of O(1/k/sup 1/2/) in the rms error sense, where k is the number of iterations. Simulation results indicate that our algorithm is substantially more reliable than two existing (asymptotically biased) algorithms, and performs at least as well as those algorithms when they work.     

Synthetic vision for enhancing poor visibility flight operations

Visibility is important for the pilot controlling an aircraft in flight conditions close to the ground, particularly when landing. Therefore, poor visibility yields a great restriction for aircraft operations. Restrictions exist for landing sites which are equipped with facilities providing a landing approach aid like ILS since a minimum is required for visibility. For landing sites providing no approach aids, restrictions are much more severe. This holds even if aircraft are equipped with modern instrumentation and navigation devices. The natural view of the pilot is dependent on various meteorological conditions like darkness, dust, fog, rain etc. The degradation in view caused by these conditions can be compensated for partially or even completely by technical means providing artificial vision cues. Such technical means may be based on radar or optical sensor information. Concepts which employ these techniques are known as “Synthetic Visual Systems” or “Enhanced Visual Systems,” . The present paper is concerned with computer generated vision as a further technique providing visual cues for the pilot. Computer generated vision may be used in combination with the aforementioned sensor based techniques. Thus, it is possible to compensate for limitations which sensor based visual systems have in providing sufficient visibility range or in generating a normal looking image. In addition, computer generated imagery has the potential providing additional information to the pilot for controlling the flight path or for warning purposes. This potential can yield improved and/or more information as compared with the natural view when looking out of the cockpit window     

非线性系统中多传感器目标跟踪融合算法研究

 研究了在非线性系统中 ,基于转换坐标卡尔曼滤波器的多传感器目标跟踪融合算法。通过分析得出 :在非线性系统的多传感器目标跟踪中 ,基于转换坐标卡尔曼滤波器 ( CMKF)的分布融合估计基本可以重构中心融合估计。仿真实验也证明了此结论。由此可见分布的 CMKFA是非线性系统中较优的分布融合算法     

Surveillance through walls and other opaque materials

The Department of Defense (DoD) has funded a dazzling array of “high tech” solutions for many of the problems facing our military forces. Many of these “solutions” have been effective for long range mass destruction but have not been applicable for the close-in hand-to-hand combat that is on our streets. Our goal at the Hughes AET Center has been to convert “high tech” DoD capabilities into cost effective tools to help law enforcement agencies do their jobs better. Surveillance systems presently used by law enforcement officers make extensive use of television, infrared and other Line-of-Sight (LOS) surveillance systems. However, these systems cannot tell what is happening on the other side of a wall, behind bushes, around the corner, in the dark or through a dense fog. A new sensor has been developed that uses technology developed by the DoD for missile warhead fuzing. This small, light weight, low power “Radar” is based upon the fact that radio waves can penetrate nonmetallic materials. This new surveillance capability can help provide information about what is in a wall, ceiling or floor or on the other side of a door or concrete wall. Real field scenarios are used in this paper to show how this radar works and how field users can tell if someone is moving inside a building, even from remote locations     

Obstacle detection by recognizing binary expansion patterns

A technique is described for obstacle detection, based on the expansion of the image-plane projection of a textured object, as its distance from the sensor decreases. Information is conveyed by vectors whose components represent first-order temporal and spatial derivatives of the image intensity, which are related to the time to collision through the local divergence. Such vectors may be characterized as patterns corresponding to “safe” or “dangerous” situations. We show that the essential information is conveyed by single-bit vector components, representing the signs of the relevant derivatives. We use two recently developed, high capacity classifiers, employing neural learning techniques, to recognize the imminence of collision from such patterns     

Sensor/actuator failure detection in the Vista F-16 by multiplemodel adaptive estimation

Multiple model adaptive estimation (MMAE) is applied to the Variable-In-flight Stability Test Aircraft (VISTA) F-16 flight control system at a low dynamic pressure flight condition (0.4 M at 20000 ft). Single actuator and sensor failures are addressed first, followed by dual actuator and sensor failures. The system is evaluated for complete or “hard” failures, partial or, “soft” failures, and combinations of hard and soft actuator and sensor failures. Residual monitoring is discussed for single and dual failure scenarios. Performance is enhanced by the application of a modified Bayesian form of MMAE, scalar residual monitoring to reduce ambiguities, automatic dithering where advantageous, and purposeful commands