多传感器融合目标跟踪

分析了基于成象和雷达两种传感器对目标状态的测量模型及其融合模型。针对两种传感器之间测量信息的不同步问题,给出了一种基于最小二乘法的不同步信息之间的时间配准和融合方法,并设计了跟踪滤波器。     

Flight path estimation using frequency measurements from a wideaperture acoustic array

A narrowband technique based on the acoustical Doppler effect is proposed for estimating the trajectory of a turbo-prop aircraft in level flight with constant velocity as it transits over a ground-based passive acoustic sensor array. The basic principle is to measure the temporal variation of the instantaneous frequency (IF) of the acoustic signal received by each sensor and then to minimize the sum of the squared deviations of the IF estimates from their predicted values over a sufficiently long period of time for all sensors. The technique provides estimates of the propeller blade rate and the five source motion parameters that describe the aircraft trajectory. The six dimensional minimization problem is reduced to a five dimensional maximization problem, which is solved numerically using the quasi-Newton method. A simple method is described that provides the initial parameter estimates required for the numerical maximization. The effectiveness of the motion parameter estimation technique is verified using real acoustic data recorded from a wide aperture microphone array during various transits of a turbo-prop aircraft     

Performance evaluation of multi-sensor classification systems

A common problem in classification is to use one/more sensors to observe repeated measurements of a target's features/attributes, and in turn update the targets' posterior classification probabilities to aid in target identification. This paper addresses the following questions: 1. How do we quantify the classification performance of a sensor? 2. What happens to the posterior probabilities as the number of measurements increase? 3. Will the targets be classified correctly? While the Kalman filter allows for off-line estimation of kinematic performance (covariance matrix), a comparable approach for studying classification accuracy has not been done previously. We develop a new analytical approach for computing the long-run classification performance of a sensor and also present recursive formulas for efficient calculation of the same. We show that, under a minimal condition, a sensor will eventually classify all targets perfectly. We also develop a methodology for evaluating the classification performance of multi-sensor fusion systems involving sensors of varying quality. The contributions of this paper are 1. A simple metric to quantify a sensor's ability to discriminate between the targets being identified, and its use in comparing multiple sensors, 2. An approximate formula based on this metric to compute off-line estimates of the rate of convergence toward perfect classification, and the number of measurements required to achieve a desired level of classification accuracy, and 3. The use of this metric to evaluate classification performance of multi-sensor fusion systems.     

Architectures and algorithms for track association and fusion

Target tracking using multiple sensors can provide better performance than using a single sensor. One approach to multiple target tracking with multiple sensors is to first perform single sensor tracking and then fuse the tracks from the different sensors. Two processing architectures for track fusion are presented: sensor to sensor track fusion, and sensor to system track fusion. Technical issues related to the statistical correlation between track estimation errors are discussed. Approaches for associating the tracks and combining the track state estimates of associated tracks that account for this correlation are described and compared by both theoretical analysis and Monte Carlo simulations     

Radar Measurement Noise Variance Estimation with Several Targets of Opportunity

A number of methods exist to track a target's uncertain motion through space using inherently inaccurate sensor measurements. A powerful method of adaptive estimation is the interacting multiple model (IMM) estimator. In order to carry out state estimation from the noisy measurements of a sensor, however, the filter should have knowledge of the statistical characteristics of the noise associated with that sensor. The statistical characteristics (accuracies) of real sensors, however, are not always available, in particular for legacy sensors. A method is presented of determining the measurement noise variances of a sensor, assumed to be constant, by using multiple IMM estimators while tracking targets whose motion is not known---targets of opportunity. Combining techniques outlined in [2] and [6], the likelihood functions are obtained for a number of IMM estimators, each with different assumptions on the measurement noise variances. Then a search is carried out over a varying grid of IMMs to bracket the variances of the sensor measurement noises. The end result consists of estimates of the measurement noise variances of the sensor in question.     

Multisensor multitarget bias estimation for general asynchronous sensors

A novel solution is provided for the bias estimation problem in multiple asynchronous sensors using common targets of opportunity. The decoupling between the target state estimation and the sensor bias estimation is achieved without ignoring or approximating the crosscovariance between the state estimate and the bias estimate. The target data reported by the sensors are usually not time-coincident or synchronous due to the different data rates. Since the bias estimation requires time-coincident target data from different sensors, a novel scheme is used to transform the measurements from the different times of the sensors into pseudomeasurements of the sensor biases with additive noises that are zero-mean, white, and with easily calculated covariances. These results allow bias estimation as well as the evaluation of the Cramer-Rao lower bound (CRLB) on the covariance of the bias estimate, i.e., the quantification of the available information about the biases in any scenario. Monte Carlo simulation results show that the new method is statistically efficient, i.e., it meets the CRLB. The use of this technique for scale and sensor location biases in addition to the usual additive biases is also presented.     

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     

Exact multisensor dynamic bias estimation with local tracks

An exact solution is provided for the multiple sensor bias estimation problem based on local tracks. It is shown that the sensor bias estimates can be obtained dynamically using the outputs of the local (biased) state estimators. This is accomplished by manipulating the local state estimates such that they yield pseudomeasurements of the sensor biases with additive noises that are zero-mean, white, and with easily calculated covariances. These results allow evaluation of the Cramer-Rao lower bound (CRLB) on the covariance of the sensor bias estimates, i.e., a quantification of the available information about the sensor biases in any scenario. Monte Carlo simulations show that this method has significant improvement in performance with reduced rms errors of 70% compared with commonly used decoupled Kalman filter. Furthermore, the new method is shown to be statistically efficient, i.e., it meets the CRLB. The extension of the new technique for dynamically varying sensor biases is also presented.     

Sensor Fault Detection in a System with Random Disturbances

Sensor faults are detected in an operating automatic system by a simplified version of the dedicated observer scheme. Control inputs are augmented by a random disturbance of moderate intensity. The dedicated observer in this case is a Kalman filter, driven by a single sensor. This filter provides estimates of the outputs from the other, nonredundant, sensors. A logical combination of these functionally redundant signals with the actual sensor signals provides prompt detection of incipient faults on all instruments without false alarms. The scheme is applied to a simulation of the lateral axis control system of a hydrofoil boat in which four sensors are to be covered by the fault detection scheme. Tests indicate that the scheme is robust with respect to variations in the intensity of the random disturbance.     

飞机着陆时的容错导航

研究了飞机在微波着陆环境中的传感器容错系统。该系统的主要目的是检测辅助导航设备和机载传感器的故障,并在这些传感器可能发生故障时提供可靠的飞机状态估计值。自动导航和控制系统利用这些状态估计控制和引导飞机按预定路线降落。     

Analysis of Min-Norm and MUSIC with arbitrary array geometry

A nonasymptotic performance comparison is presented between the Min-Norm and MUSIC algorithms for estimating the directions of arrival of narrowband plane waves impinging on an array of sensors. The analysis is based on a finite amount of sensor data. The analysis makes the assumption of high signal-to-noise ratio (SNR), and it applies to arrays of arbitrary geometry. It is shown that Min-Norm can be expressed as a certain data-dependent weighted MUSIC algorithm, and that this relationship allows a unified performance comparison. It is also shown that the variances of the estimated directions-of-arrival from the MUSIC algorithm are always smaller than those of the Min-Norm algorithm at high SNR when both algorithms employ a numerical search procedure to obtain the estimates     

均值检验方法及其在冗余惯性导航系统中的应用

采用了非线性滤波的方法对影响故障检测及分离（FDI）性能的传感器误差进行了补偿,同时提出了故障检测与分离的均值检验方法（MVT）,该方法可以对2个传感器同时出现故障的情况进行检测与隔离。5个陀螺冗余惯性导航系统仿真结果验证了本文算法的有效性。     

A Smart Low-Power Meteorological System

A new low-power instrument to measure meteorological parameters has been developed. The instrument is based on an intelligent data cruncher concept: Fast sensor data rates are stored and process to yield a variety of answers for each parameter, at slower data rates, as appropriate. Special methods are used to achieve these results with an average current drain of under one mA, including sensors. Sampling rates and processing algorithms are designed to correct for swaying ocean-deployed buoys. A modular approach to design allows many types of sensors to be accommodated and permits data dissemination to a variety of destinations; data is available for real-time transmission or for internal archiving. The Weather Station's high-capacity internal data storage system, coupled with its fast data acquisition rates, enable the instrument to be used for air turbulence measurements.     

Finite data performance of MUSIC and minimum norm methods

In the direction of arrival (DOA) estimation problem, we encounter both finite data and insufficient knowledge of array characterization. It is therefore important to study how subspace-based methods perform in such conditions. We analyze the finite data performance of the multiple signal classification (MUSIC) and minimum norm (min. norm) methods in the presence of sensor gain and phase errors, and derive expressions for the mean square error (MSE) in the DOA estimates. These expressions are first derived assuming an arbitrary array and then simplified for the special case of an uniform linear array with isotropic sensors. When they are further simplified for the case of finite data only and sensor errors only, they reduce to the recent results given previously (1989, 1991). Computer simulations are used to verify the closeness between the predicted and simulated values of the MSE     

Fusion of sensors with dissimilar measurement/tracking accuracies

The case of data fusion of sensors dissimilar in their measurement/tracking errors is considered. It is shown that the fused track performance is similar whether the sensor data are fused at the track level or at the measurement level. The case of a cluster of targets, resolved by one sensor but not the other, is also considered. Under certain conditions the fused track may perform worse than the worst of the sensors. A remedy to this problem through modifications of the association algorithm is presented     

Censoring sensors: a low-communication-rate scheme for distributeddetection

We consider a new scheme for distributed detection based on a “censoring” or “send/no-send” idea. The sensors are assumed to “censor” their observations so that each sensor sends to the fusion center only “informative” observations, and leaves those deemed “uninformative” untransmitted. The main result of this work is that with conditionally independent sensor data and under a communication rate constraint, in order to minimize the probability of error, transmission should occur if and only if the local likelihood ratio value observed by the sensor does not fall in a certain single interval. Similar results are derived from Neymarr-Pearson and distance-measure viewpoints. We also discuss simplifications for the most interesting case that the fusion center threshold is high and the communication constraint is severe. We compare censoring with the more common binary-transmission framework and observe its considerable decrease in communication needs. Finally, we explore the use of feedback to achieve optimal performance with very little communication     

跟踪弹道导弹时预警卫星姿态前馈协同控制

基于角动量守恒定理,推导了跟踪弹道导弹时,具有活动部件的预警卫星姿态前馈协同控制。该控制算法基于角动量守恒方程估计红外相机运动产生的角动量扰动以及为抑制该扰动所需的期望飞轮转速,将期望飞轮转速与实际转速的偏差作为前馈信号加入到传统控制系统中,推导出预警卫星姿态前馈控制模型。最后通过数值仿真验证了控制方法,仿真结果与理论分析具有很好的一致性。并且该控制方法简单、可靠,能够适应卫星在轨实时控制。     

基于Ansoft的同面电容传感器的研究与仿真

阐述了利用同面电容传感器对复合材料进行无损检测的原理,运用AnsoftMaxwell有限元分析软件对不同极板间距和屏蔽电极形式的传感器进行仿真计算,比较分析了不同极板间距的传感器产生的电场强度变化趋势,研究了不同屏蔽电极对传感器检测特性的影响。仿真结果表明：传感器极板间距越大,检测深度越大,检测特性越好;在3种屏蔽电极中,环形屏蔽电极的检测特性最好。     

Multiple target angle tracking using sensor array outputs

The use of the output of an array of sensors to track multiple independently moving targets is reported. The output of each sensor in the array is the sum of signals received from each of the targets. The results of direction-of-arrival estimation by eigenvalue analysis are extended to derive a recursive procedure based on a matrix quadratic equation. The solution of this matrix quadratic equation is used to provide updated target positions. A linear approximation method for estimating the solution of the matrix equation is presented. The algorithm is demonstrated by the simulated tracking of two targets. The main advantage of the algorithm is that a closed-form solution for updating the target angle estimates has been obtained. Also, its application is straightforward, and the data association problem due to uncertainty in the origin of the measurements is avoided. However, it requires the inversion of an N×N as well as other linear operations, so that the computational burden becomes substantial as N becomes very large