Optimum decoding based smoothing algorithm for dynamic systems with interference

This paper presents a new smoothing algorithm for discrete models with arbitrary random interference. For the disturbance noise, observation noise and interference, only independency is required. Moreover, the motion and observation models are not restricted to be linear functions of the disturbance noise and interference. This algorithm estimates the states by reducing the smoothing problem to a multiple composite hypothesis testing problem, and then using the Viterbi decoding algorithm. Simulation results have shown that the new algorithm performs very well.     

Non-linear smoothing algorithm for multi-dimensional dynamic systems

A new sub-optimum smoothing algorithm is presented for multi-dimensional dynamic systems. This algorithm is based upon quantization, multiple hypothesis testing, and the Viterbi decoding algorithm. The estimation of state vectors is carried out sequentially, component-by-component, and in parallel. A considerable memory reduction is achieved for state estimation implementation with the proposed algorithm. Simulation results, some of which are presented, show that the sub-optimum algorithm performs better than the extended Kalman filter algorithm for some non-linear multi-dimensional models with white gaussian disturbance and observation noises. In addition, the performance of the sub-optimum algorithm is almost as good as the Kalman filter algorithm for linear multi-dimensional models with white gaussian noise.     

A smoothing algorithm for systems with multiplicative noise

A fixed-point smoothing algorithm is derived for linear time-varying systems with multiplicative noise in the state channels of the plant and the measurements as well as additive noise. As in systems without multiplicative noise, the smoothed estimates depend on the filtered estimates. The steady-state behavior of the algorithm is examined. If the multiplicative part is absent, then the results coincide with known results for systems with additive noise     

An efficient fixed-lag smoothing algorithm for discrete linear systems

The modified Bryson-Frazier fixed interval smoothing algorithm [6], is an addendem to the Kalman filter. This algorithm when applied to the problem of fixed-lag smoothing is computationally more efficient than the algorithms recently reported in refs. [1–3]. Features of the algorithm are ease of implentation, computational efficiency, reduced storage requirements, and stability.     

Optimum quantization in dynamic systems

In this paper the problem of optimally designing a quantizer imbedded in a closed-loop dynamic system is considered. The criterion for the design is that the overall system performance as expressed by a variational criterion is optimized. The function of quantization is thus related to the functions of control and estimation that are performed in the system. First, a procedure is described for optimally designing a quantizer in a static open-loop system, where the design criterion is the expected value of a function of the instantaneous error between the input and output of the quantizer. This procedure reduces the search over all quantizer parameters to an iterative search over a single parameter. Next, the existing methods for finding the optimal design of a quantizer imbedded in a dynamic system are reviewed. The most general method found in the literature involves a combination of dynamic programming with an exhaustive search for all quantizer parameters. The computational requirements of this procedure are quite large even for low-order systems with few quantizer parameters. Finally, a new result is presented that leads to greatly reduced computational requirements for the dynamic system case. It is shown that under certain conditions an overall optimum system design is obtained by first optimizing the system with all quantizers removed and then applying the procedure for the static open-loop case mentioned above. This result is analogous to the separation of the functions of estimation and control that occurs under similar conditions. The computational savings over the existing procedures are very extensive, and the new procedure is computationally feasible for a large class of practical systems.     

Non-linear state smoothing for discrete dynamic systems with past histories

A state smoothing scheme is presented for dynamic systems with past histories. The state in the future is a given (linear or non-linear) function of the disturbance noise and both the present and N — I past discrete values of the state. An observation in the present is a given function of the observation noise and both the present and N — 1 past discrete values of the state. The proposed scheme is based upon multiple hypothesis testing and the Viterbi algorithm of information theory, Simulation results, some of which are presented, have shown that the proposed scheme performs well.     

Covariance factorization algorithms for fixed-interval smoothing oflinear discrete dynamic systems

Efficient factorized covariance smoothers designed to work with factorized covariance filters are derived for linear discrete dynamic systems. The approach to factorized covariance smoothers (either U -D or square root) uses outputs from factorized covariance filters and is closely derived from the G.J. Bierman's earlier algorithm (1974), the Dyer-McReynolds covariance smoother. These algorithms are more efficient than the Bierman's newer smoother (1983) based upon rank 1 process noise updates. The efficiency of the new algorithms increases significantly as the order of process noise increases. For full process noise, they can be implemented in a way that avoids the inverse of the transition matrix     

一般相关噪声下线性系统固定延迟平滑算法

针对机动目标跟踪中固定延迟平滑估计算法的精度问题,当具有一般相关过程噪声和量测噪声时,提出了离散线性系统最优固定延迟平滑估计算法.该算法通过将延迟区间内全部量测进行集中式扩维.并对误差传递进行分析,从而精确地给出了误差间的相关性.在线性无偏最小方差意义下对系统状态进行递推估计,新算法在噪声的高斯分布假设下是最优的.仿真实验结果表明了该算法的有效性.     

A survey of data smoothing for linear and nonlinear dynamic systems

A survey of the field of data smoothing for lumped-parameter, linear and nonlinear, dynamic systems is presented. The survey beings with the work of Kepler and Gauss, proceeds through that of Kolmogorov and Wiener, and concludes with the studies of numerous researchers during the past 10–12 years. The purpose of the survey is to place in perspective the development of the field of data smoothing relative to the broader area of estimation theory of which it is a part.     

An algorithm for inverting linear dynamic systems

An algorithm is presented for inverting transformations associated with linear dynamic systems. The induced inverse system, when it exists, operates on outputs and generates the corresponding inputs of the system to which it is inverse. The applications for inverse systems are found in such diverse areas as control, coding, filtering, and sensitivity analysis.     

Non-linear state smoothing and filtering in blocks for dynamic systems with missing observations

State smoothing and filtering are treated for non-linear dynamic systems in the absence of observations in a considered observation interval. Models are taken to be non-linear functions of the states, disturbance noises, and observation noises, whereas the state estimation of these models cannot, in general, be performed by classical estimation schemes, such as the extended Kalman filter. State estimation is performed in blocks by estimating missing observations with interpolating functions. The resulting estimation scheme requires a constant memory for its implementation.     

An efficient dynamic scheduling algorithm for multiprocessorreal-time systems

Many time-critical applications require predictable performance and tasks in these applications have deadlines to be met. In this paper, we propose an efficient algorithm for nonpreemptive scheduling of dynamically arriving real-time tasks (aperiodic tasks) in multiprocessor systems. A real-time task is characterized by its deadline, resource requirements, and worst case computation time on p processors, where p is the degree of parallelization of the task. We use this parallelism in tasks to meet their deadlines and, thus, obtain better schedulability compared to nonparallelizable task scheduling algorithms. To study the effectiveness of the proposed scheduling algorithm, we have conducted extensive simulation studies and compared its performance with the myopic scheduling algorithm. The simulation studies show that the schedulability of the proposed algorithm is always higher than that of the myopic algorithm for a wide variety of task parameters     

A new algorithm for integration of dynamic systems

A high precision unconditionally stable algorithm for computation of linear dynamic structural systems is described. It shares the advantageous property (typical for lower order algorithms) of the amplification matrix preserving a banded form due to discretization in space. Hence in contrast to the known high precision algorithms employing full matrices, less computer space and fewer operations are needed. The new algorithm is free of artificial damping in the undamped case as well as of artificial oscillation in the postcritical damping region. Local truncation error analysis is made and rate of convergence is proved. Applications to problems allowing comparison with existing results are presented. Excellent agreement with exact solutions is achieved and engineering accuracy is attained with relatively few time steps.     

Optimal smoothing of non-linear dynamic systems via Monte Carlo Markov chains

We consider the smoothing problem of estimating a sequence of state vectors given a nonlinear state space model with additive white Gaussian noise, and measurements of the system output. The system output may also be nonlinearly related to the system state. Often, obtaining the minimum variance state estimates conditioned on output data is not analytically intractable. To tackle this difficulty, a Markov chain Monte Carlo technique is presented. The proposal density for this method efficiently draws samples from the Laplace approximation of the posterior distribution of the state sequence given the measurement sequence. This proposal density is combined with the Metropolis-Hastings algorithm to generate realizations of the state sequence that converges to the proper posterior distribution. The minimum variance estimate and confidence intervals are approximated using these realizations. Simulations of a fed-batch bioreactor model are used to demonstrate that the proposed method can obtain significantly better estimates than the iterated Kalman-Bucy smoother.     

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.     

A new dynamic voting algorithm for distributed database systems

We consider the problem of keeping a distributed database system that has been partitioned because of site or communication link failures partially operable while ensuring data consistency. A dynamic-voting-consistency algorithm is proposed, and its correctness is demonstrated. The proposed algorithm results in improved efficiency in executing read requests by not requiring a read quorum. This algorithm is effective in environments where the majority of user requests are “read” types of requests. Furthermore, the proposed algorithm results in efficient recovery by avoiding updating those data objects that are still current. Under the proposed algorithm, the majority partition would be available even if changes in the network topology take place at a higher rate than the update rate, as long as only simple partitioning takes place     

Decomposition algorithm of dynamic programming for large-scale time-delay systems

This paper addresses the development of a dynamic programming decomposition-coordination algorithm for the optimal control of discrete-time large-scale systems with multiple time delays. The systems under study can be converted into the corresponding augmented systems satisfying the Markov processes denning the augmented state vectors. The resulting dynamic programming can then be further decomposed into a series of subproblems which might be solved in parallel by combining the dynamic programming techniques with approaches to matrix partition. The entire system's solution is finally given by the iterative computation between the subproblems being the local control level and the coordinator. The major advantages of the proposed algorithm over others are to avoid two-point boundary-value problems of the systems with multiple time delays and to significantly reduce the computer memory and time required for such large-scale systems optimal control problems.     

Comments on "Optimum quantization in dynamic systems"

A proposed algorithm for determining the optimal parameters of a quantizer imbedded in a closed-loop dynamical system is shown to yield incorrect results. A counterexample to the algorithm is presented, as well as a correct interpretation of this result.     

Optimum experimental designs for dynamic systems in the presence of correlated errors

The problem of determining an optimal measurement time schedule for identification of unknown parameters in multiresponse systems when correlations between observations occur is considered. The measurement process is performed by collecting data at discrete time instants from several outputs. An observation plan is proposed based on a scalar measure of the Fisher information matrix as the design criterion quantifying the accuracy of parameter estimators. A numerical procedure is proposed to determine approximations of optimum designs in the case of correlated measurement errors. The approach is illustrated with an example of the multi-output system of equations describing a chemical kinetic reaction.     

基于启发式动态分解算法的矩形件优化排样

针对二维矩形件优化排样问题,提出了一种启发式动态分解算法,其可扩展用于三维及多容器全局排样求解。根据排放矩形件对容器进行正交动态分解,计算放置耦合度选择最佳子容器,通过干涉关系实现所有容器状态更新,实现大规模复杂排样问题的快速高效求解。对国际上公认Bench-mark多个问题例的计算结果表明,所提算法与同类算法相比优势明显,布局利用率提高达9.4%,计算效率提升达95.7%,并且已在商业化排样软件AutoCUT中应用,应用前景良好。