几何代数及其在飞行力学中的应用

介绍了几何代数的基本知识,比较了几何代数与矢量代数、四元数的区别和联系,并推导了它们在表示旋转时的互相转换关系,展示了几何代数在描述空间旋转变换时的便利.最后通过相对姿态运动的分析,展示了几何代数在飞行力学中应用的可行性.     

基于几何代数理论的转子堆叠装配多目标优化

在航空发动机多级转子装配过程中,优化的转子堆叠装配方案对提高转子装配质量及安全运行有着重要的意义。为了提高转子安装相位优化效率和装配质量,基于几何代数理论并结合多目标优化方法提出了一种高效求解最优堆叠装配方案的方法。首先,对几何代数和齐次矩阵的计算效率进行对比验证,并基于几何代数理论改进多级转子堆叠装配误差传递模型;其次,针对航空发动机转子安装相位角度的装配要求,建立转子同心度和初始不平衡量的多目标优化模型;最后,采用非支配排序遗传算法求解该堆叠装配多目标优化问题,获得符合工艺要求的最优装配方案。算例结果表明,优化后的转子装配方案比随机装配方案同心度降低65.10%,初始不平衡量降低97.88%。     

Direction finding with a four-element Adcock-Butler matrix antennaarray

The conventional analog Adcock-Butler matrix (ABM) antenna array direction finder suffers from systemic errors, component matching problems, and bandwidth limitations. Three digital bearing estimators are developed as candidates to replace the analog signal processing portion of the ABM. Using the same antenna array, they perform all signal processing in the frequency domain, thereby benefitting from the computational efficiency of the fast Fourier transform (FFT) algorithm. The first estimator requires two analog-to-digital converters (A-D) and three antenna elements. It multiplies the difference between the discrete Fourier transforms (DFTs) of the output signals from two antenna elements with that from a third antenna element. At each frequency component, the phase of this product is a function of the bearing. A weighted least squares (LS) fit through all the phase components then gives a bearing estimate. The second estimator is similar to the first but uses three A-D and all four antenna elements. The output signal from the additional antenna element provides an independent estimate of the weights for the LS fit, giving an improvement in accuracy. The third estimator applies the physical constraint existing between the time-difference-of-arrival (TDOA) of a signal intercepted by two perpendicular sets of antenna elements. This yields a better estimator than simple averaging of the bearing from each set of antenna elements. The simulation studies used sinusoids and broadband signals to corroborate the theoretical treatment and demonstrate the accuracy achievable with these estimators. All three direction finders have superior performance in comparison with the analog ABM     

MAP estimation of target manoeuvre sequence with theexpectation-maximization algorithm

Two algorithms are derived for the problem of tracking a manoeuvring target based on a sequence of noisy measurements of the state. Manoeuvres are modeled as unknown input (acceleration) terms entering linearly into the state equation and chosen from a discrete set. The expectation maximization (EM) algorithm is first applied, resulting in a multi-pass estimator of the MAP sequence of inputs. The expectation step for each pass involves computation of state estimates in a bank of Kalman smoothers tuned to the possible manoeuvre sequences. The maximization computation is efficiently implemented using the Viterbi algorithm. A second, recursive estimator is then derived using a modified EM-type cost function. To obtain a dynamic programming recursion, the target state is assumed to satisfy a Markov property with respect to the manoeuvre sequence. This results in a recursive but suboptimal estimator implementable on a Viterbi trellis. The transition costs of the latter algorithm, which depend on filtered estimates of the state, are compared with the costs arising in a Viterbi-based manoeuvre estimator due to Averbuch, et al. (1991). It is shown that the two criteria differ only in the weighting matrix of the quadratic part of the cost function. Simulations are provided to demonstrate the performance of both the batch and recursive estimators compared with Averbuch's method and the interacting multiple model filter     

四元数最小均方误差算法及其在波束形成中的应用

 作为多维信号处理的一个重要工具,四元数代数已在各个领域有所应用。对四元数最小均方误差(QMMSE)算法进行了研究,首先推导了四元数实数形式的最小均方误差(QRMMSE)算法,进一步推导了四元数复数形式的最小均方误差(QCMMSE)算法,并且分析了两种算法的区别和计算量。最后将QMMSE算法应用到机载简化矢量传感器阵列的波束形成中,与复数长矢量最小均方误差(LVMMSE)算法相比较,QCMMSE算法的性能有所提高,计算量有所减少。计算机仿真结果验证了所提算法的有效性。     

Bearings-only and Doppler-bearing tracking using instrumentalvariables

In bearings-only tracking (BOT) or Doppler and bearing tracking (DBT), both common passive sonar problems, the measurement equations are nonlinear. To apply the Kalman filter, it is necessary either to linearize the equations or to embed the nonlinearities into the noise terms. The former sometimes leads to filter divergence, while the latter produces biased estimates. A formulation of BOT and DBT which has a constant state vector and simplifies the tracking problem to one of constant parameter estimation is given. The solution is by the instrumental variable method. The instrumental variables are obtained from predictions based on past measurements and are therefore independent of the present noisy measurements. The result is a recursive unbiased estimator. The theoretical developments are verified by simulation, which also shows that the formulation leads to near optimal estimators whose errors are close to the Cramer-Rao lower bound (CRLB)     

On the exact solutions of pseudorange equations

Three formulations of exact solution algorithms to the system of determined pseudorange equations are derived. It is demonstrated that pseudorange equations are hyperbolic in nature and may have two solutions, even when the emitter configuration is nonsingular. Conditions for uniqueness and for the existence of multiple solutions are derived in terms of the Lorentz inner product. The bifurcation parameter for systems of pseudorange equations is also expressed in term of the Lorentz functional. The solution is expressed as a product of the geometric dilution of precision (GDOP) matrix, representing the linear part of the solution, and a vector of nonlinear term. Using this formulation stability of solutions is discussed     

State estimation using an approximate reduced statistics algorithm

The problem of state estimation using nonlinear additive Gaussian noise measurements is addressed. A geometric model for the posterior state density is assumed based on a multidimensional Haar basis representation. An approximate reduced statistics (ARS) algorithm, suggested by the parameter estimator of Kulhavy is then developed, using successive minimization of relative entropy between model densities and an approximate posterior density. The state estimator thus derived is applied to a bearings-only target tracking problem in a multiple sensor scenario     

EM-ML algorithm for track initialization using possibly noninformative data

Initializing and maintaining a track for a low observable (LO) (low SNR, low target detection probability and high false alarm rate) target can be very challenging because of the low information content of measurements. In addition, in some scenarios, target-originated measurements might not be present in many consecutive scans because of mispointing, target maneuvers, or erroneous preprocessing. That is, one might have a set of noninformative scans that could result in poor track initialization and maintenance. In this paper an algorithm based on the expectation-maximization (EM) algorithm combined with maximum likelihood (ML) estimation is presented for tracking slowly maneuvering targets in heavy clutter and possibly noninformative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down noninformative scans using the Q-function in the M-step of the EM algorithm. It is shown that target features in the form of, for example, amplitude information (AI), can also be used to improve the estimates. In addition, performance bounds based on the supplemented EM (SEM) technique are also presented. The effectiveness of new algorithm is first demonstrated on a 78-frame long wave infrared (LWIR) data sequence consisting of an Fl Mirage fighter jet in heavy clutter. Previously, this scenario has been used as a benchmark for evaluating the performance of other track initialization algorithms. The new EM-ML estimator confirms the track by frame 20 while the ML-PDA (maximum likelihood estimator combined with probabilistic data association) algorithm, the IMM-MHT (interacting multiple model estimator combined with multiple hypothesis tracking) and the EVIM-PDA estimator previously required 28, 38, and 39 frames, respectively. The benefits of the new algorithm in terms of accuracy, early detection, and computational load are illustrated using simulated scenarios as well.     

Application of Maximum Likelihood Estimation of Persymmetric Covariance Matrices to Adaptive Processing

The optimum weights for an adaptive processor are determined by solving a particular matrix equation. When, as is usually true in practice, the covariance matrix is unknown, a matrix estimator is required. Estimating the matrix can be computationally burden some. Methods of decreasing the computational burden by exploiting persymmetric symmetries are discussed. It is shown that the number of independent vector measurements required for the estimator can be decreased by up to a factor of two.     

Bearings-only tracking of maneuvering targets using abatch-recursive estimator

We present a new batch-recursive estimator for tracking maneuvering targets from bearings-only measurements in clutter (i.e., for low signal-to-noise ratio (SNR) targets), Standard recursive estimators like the extended Kalman Iter (EKF) suffer from poor convergence and erratic behavior due to the lack of initial target range information, On the other hand, batch estimators cannot handle target maneuvers. In order to rectify these shortcomings, we combine the batch maximum likelihood-probabilistic data association (ML-PDA) estimator with the recursive interacting multiple model (IMM) estimator with probabilistic data association (PDA) to result in better track initialization as well as track maintenance results in the presence of clutter. It is also demonstrated how the batch-recursive estimator can be used for adaptive decisions for ownship maneuvers based on the target state estimation to enhance the target observability. The tracking algorithm is shown to be effective for targets with 8 dB SNR     

Acousto-optic spectrum analyzer techniques for monitoring CW

The issues associated with processing the outputs of an integrating acoustooptical spectrum analyzer (AOSA) to detect and identify the frequency and power of continuous wave (CW) signals are considered. The performance of the optimum detection strategy is presented, along with the Cramer-Rao bound on the performance of estimators of the frequency, and the performance of a simple peak-based frequency estimator. The performance of the maximum likelihood (ML) estimator of the power level is also presented. The results provide the behavior of the system as a function of the product of the aperture time and photodetector spacing amongst other parameters     

A New Method of Position Estimation Using Bearing Measurements

A method of locating radio emitters anywhere on the surface of the Earth, by using bearing measurements, is presented. The method associates planes of bearing with the measured bearings, and characterizes these planes by their unit normals. The emitter is located by finding the unit vector from the Earth's center that minimizes the sum of squares of its inner product with the normal vectors. The solution is found in terms of the eigenvalues and eigenvectors of a 3 × 3 matrix.     

Emitter localization using clustering-based bearing association

A closed-form emitter location estimator using time difference of arrival (TDOA) measurements is developed based on triangulation of hyperbolic asymptotes. The problem of associating the asymptotes with the emitter is solved by clustering the bearing angles of the linear asymptotes using a kernel density estimate. A closed-form estimate of the emitter location is obtained from triangulation of the clustered bearings using a weighted version of the pseudolinear estimator. By way of simulation examples, the proposed closed-form estimator is shown to outperform the computationally demanding and divergence-prone maximum likelihood (ML) estimator at moderate TDOA noise levels.     

On Attitude Estimation Schemes for Fine-Pointing Control

This paper studies single-axis equations of motion that are applicable to a spacecraft or to a space experiment pointing assembly whose motion has been perfectly isolated from the carrier vehicle. It considers four state estimators for implementation in the control loop for a stellar observation experiment. The first three estimators are very general and do not make use of input torque in their prediction models, while the proposed fourth estimator utilizes this information, It is shown via closed-loop covariance analysis that the best achievable pointing performance with the best of the first three estimators is limited to about 0.125 are-sec (rms) with the given rate-gyro and star-tracker inaccuracies. It is also shown that the fourth estimator has the capability of achieving a pointing performance far superior to the performance achievable using the first three estimators. The fourth estimator relies on the ability to accurately generate the desired control torque (i.e., low input noise).     

MAV Attitude Determination by Vector Matching

An attitude determination algorithm suitable for micro aerial vehicle (MAV) applications is developed. The algorithm uses Earth's magnetic and gravity field vectors as observations. The magnetic field vector measurements are obtained from a magnetometer triad. The gravity field vector is measured by fusing information from an accelerometer triad with GPS/WAAS (wide area augmentation system) velocity measurements. Two linearization and estimator designs for implementing the algorithm are discussed. Simulation and experimental flight test results validating the algorithm are presented.     

Error Covariance Analysis of a Karhunen-Loeve Random Field Estimator

An error covariance analysis of a two-dimensional Karhunen-Loeve random field estimator (KLE) for gridded gravity data is presented without actually using the data. Attention is focused particularly on the north-south deflection component of the disturbance vector so that the rms value of its residual error may not exceed 3 percent of the signal rms for grid spacing of not less than 5 nm, when signal-to-noise ratio in the data is varied within reasonable limits. To achieve this rather stringent goal, a discrete KLE derived entirely from a two-dimensional grid configuration is needed. The KLE for geodesy applications was developed initially by Bose. Using continuous measurements and the orthogonality relations of the Karhunen-Loeve (KL) eigenvectors in a continuous domain, continuous KL gain coefficients ?mnj and consequently the continuous KL gain vector Kmn were obtained; thus a satisfactory continuous KLE was achieved. However the discrete version of the KLE was then given by keeping the old (continuous) ?mnj, and simply computing the KL eigenvectors on the grid coordinates to evaluate the discrete KL gain vector Kmn. Clearly this is not a satisfactory discrete KLE since , ?mnj are not constants but rather depend on the measurement noise variances. So if the measurements are now going to be discrete, one must rederive ?mnj for this situation.     

U(sl(■)_((+)))的生成元及生成关系

设sl(2,F)是三维单代数,sl(■)是相应于sl(2,F)的无扭仿型Kac-Moody代数的导代数。讨论了sl(■)的子代数sl(■)(+)的普遍包络代数U(sl(■)_((+)))的生成元及生成关系问题。     

Maximum likelihood angle extractor for two closely spaced targets

In a scenario of closely spaced targets special attention has to be paid to radar signal processing. We present an advanced processing technique, which uses the maximum likelihood (ML) criterion to extract from a monopulse radar separate angle measurements for unresolved targets. This processing results in a significant improvement, in terms of measurement error standard deviations, over angle estimators using the monopulse ratio. Algorithms are developed for Swerling I as well as Swerling III models of radar cross section (RCS) fluctuations. The accuracy of the results is compared with the Cramer Rao lower bound (CRLB) and also to the monopulse ratio technique. A novel technique to detect the presence of two unresolved targets is also discussed. The performance of the ML estimator was evaluated in a benchmark scenario of closely spaced targets - closer than half power beamwidth of a monopulse radar. The interacting multiple model probabilistic data association (IMMPDA) track estimator was used in conjunction with the ML angle extractor