Adaptive Estimation and Detection Techniques with Applications

Hybrid systems have been identified as one of the main directions in control theory and attracted increasing attention in recent years due to their huge diversity of engineering applications. Multiplemodel (MM) estimation is the state-of-the-art approach to many hybrid estimation problems. Existing MM methods with fixed structure usually perform well for problems that can be handled by a small set of models. However, their performance is limited when the required number of models to achieve a satisfactory accuracy is large due to time evolution of the true mode over a large continuous space. In this research, variable-structure multiple model (VSMM) estimation was investigated, further developed and evaluated. A fundamental solution for on-line adaptation of model sets was developed as well as several VSMM algorithms. These algorithms have been successfully applied to the fields of fault detection and identification as well as target tracking in this thesis. In particular, an integrated framework to detect, identify and estimate failures is developed based on the VSMM. It can handle sequential failures and multiple failures by sensors or actuators. Fault detection and target maneuver detection can be formulated as change-point detection problems in statistics. It is of great importance to have the quickest detection of such mode changes in a hybrid system. Traditional maneuver detectors based on simplistic models are not optimal and are computationally demanding due to the requirement of batch processing. In this presentation, a general sequential testing procedure is proposed for maneuver detection based on advanced sequential tests. It uses a likelihood marginalization technique to cope with the difficulty that the target accelerations are unknown. The approach essentially utilizes a priori information about the accelerations in typical tracking engagements and thus allows improved detection performance. The proposed approach is applicable to change-point detection problems under similar formulation, such as fault detection

Adaptive Estimation and Detection Techniques with Applications

Hybrid systems have been identified as one of the main directions in control theory and attracted increasing attention in recent years due to their huge diversity of engineering applications. Multiplemodel (MM) estimation is the state-of-the-art approach to many hybrid estimation problems. Existing MM methods with fixed structure usually perform well for problems that can be handled by a small set of models. However, their performance is limited when the required number of models to achieve a satisfactory accuracy is large due to time evolution of the true mode over a large continuous space. In this research, variable-structure multiple model (VSMM) estimation was investigated, further developed and evaluated. A fundamental solution for on-line adaptation of model sets was developed as well as several VSMM algorithms. These algorithms have been successfully applied to the fields of fault detection and identification as well as target tracking in this thesis. In particular, an integrated framework to detect, identify and estimate failures is developed based on the VSMM. It can handle sequential failures and multiple failures by sensors or actuators. Fault detection and target maneuver detection can be formulated as change-point detection problems in statistics. It is of great importance to have the quickest detection of such mode changes in a hybrid system. Traditional maneuver detectors based on simplistic models are not optimal and are computationally demanding due to the requirement of batch processing. In this presentation, a general sequential testing procedure is proposed for maneuver detection based on advanced sequential tests. It uses a likelihood marginalization technique to cope with the difficulty that the target accelerations are unknown. The approach essentially utilizes a priori information about the accelerations in typical tracking engagements and thus allows improved detection performance. The proposed approach is applicable to change-point detection problems under similar formulation, such as fault detection

Sequential Detection of Target Maneuvers

This paper addresses target maneuver onset detection based on sequential statistical tests. Cumulative sums (CUSUM) type and Shiryayev sequential probability ratio (SSPRT) tests are developed by using a likelihood marginalization technique to cope with the difficulty that the target maneuver accelerations are unknown. The approach essentially utilizes a priori information about the maneuver accelerations in typical tracking engagements and thus allows to improve detection performance as compared with traditional maneuver detectors. Simulation results are presented that demonstrate the capabilities of the maneuver detectors developed

Multiple-Model Estimation with Variable Structure - Part VI: Expected-Mode Augmentation

this paper. Assume for simplicity of presentation that the IMM mechanism is used for model-conditioned reinitialization [23]. The proposed EMA algorithms involve the following main functional modules: 1) EMA M k := M ): expected-mode augmentation. 2) VSIMM[M k , M k1 ]: recursion for variablestructure IMM (VSIMM) estimation that uses This is the case whenever the mode space is continuous, although there are problems in which different m j represent different physical quantities and thus their weighted sum is not necessarily meaningful. TABLE I One Cycle of EMA Algorithm A S2. For M k = E k ), run VSIMM[M , M k1 ] to obtain the overall estimates, error covariances, and model probabilities TABLE I I One Cycle of EMA Algorithm B k1 ,runVSIMM[M f , M k1 ] to obtain S2. Obtain E k = E(M ; M S3. Run VSIMM[E , M ] to obtain S4. Run EF[M in the set M k =M TABLE III One Cycle of EMA Algorithm C kjk1 g S2. For M 0 k = E 0 k k1 ), run VSIMM[M k , M k1 ] S3. Obtain E k = E(M 0 k ; M 2M k S4. Run VSIMM[E k , M k1 ] k1 ,runEF[M in the set M =M model sets M k1 and M k at time k 1andk, respectively. 3) EF[M 0 k , M 00 k ; M k1 ]: estimation fusion of two estimates resulting from VSIMM[M 0 k , M k1 ]and VSIMM[M 00 k , M k1 ] recursions, respectively, where M 0 k and M 00 k are discussed later. The VSIMM and EF functions have been developed, utilized, and documented in several publications on VSMM estimation [17, 29, 26, 25]. For the EMA procedure, a general description has been given above; a more detailed discussion is given nex

Performance Comparison of Target Maneuver Onset Detection Algorithms

This paper compares six di#erent algorithms for target maneuver detection in a number of typical maneuvering target tracking scenarios. Measurement residual based chi-square test, input estimate based chi-square test, input estimate based significance test, generalized likelihood ratio, cumulative sum, and marginalized likelihood ratio detectors are examined. Maneuver onset detection times and ROC curves are presented and performance measures are discussed through simulations. Further, the e#ect of di#erent window sizes on detection performance is evaluated

Predicting Internet End-To-End Delay: A Multiple-Model Approach

This paper presents a novel approach to predict the Internet end-to-end delay using multiple-model (MM) methods. The basic idea of the MM method is to assume the system dynamics can be described by a set of models rather than a single one; by running a bank of filters (each corresponds to a certain model in the set) in parallel at the same time, the MM output is given by a combination of the estimates from these filters. Based on collected end-to-end delay data and preliminary data analysis, we propose an off-line model set design procedure using vector quantization (VQ) and short-term time series analysis so that MM methods can be applied to predict on-line measurement data. Numerical results show that the proposed MM predictor outperforms two widely used adaptive filters in terms of prediction accuracy and robustness

Multiple-Model Detection of Target Maneuvers

This paper proposes a multiple-model (MM) hypothesis testing approach for detection of unknown target maneuvers that may have several possible prior distributions. An MM maneuver detector based on sequential hypothesis testing is developed

A Range Rate Based Detection Technique for Tracking a Maneuvering Target

In this paper a novel approach for detecting unknown target maneuver using range rate information is proposed based on the generalized Page's test with the estimated target acceleration magnitude. Due to the high nonlinearity between the range rate measurement and the target state, a measurement conversion technique is used to treat range rate as a linear measurement in Cartesian coordinates so that a standard Kalman filter can be applied. The detection performance of the proposed algorithm is compared with that of existing maneuver detectors over various target maneuver motions. In addition, a model switching tracker based on the proposed maneuver detector is compared with the state-of-the-art IMM estimator. The results indicate the e#ectiveness of the maneuver detection scheme which simplifies the tracker design. The tracking performance is also evaluated using a steady state analysis

Expected-Mode Augmentation Algorithms for Variable-Structure Multiple-Model Estimation

This paper presents a new class of variable-structure algorithms, referred to as expected-mode augmentation (EMA), for multiple-model estimation. In this approach, the original model set is augmented by a variable set of models intended to match the expected value of the unknown true mode. These models are generated adaptively in real time as (globally or locally) probabilistically weighted sums of modal states over the model set. This makes it possible to cover a large continuous mode space by a relatively small number of models at a given accuracy level. Performance of the proposed EMA algorithms is evaluated via a simulated example of a maneuvering target tracking problem

Additional file 1: Table S1. of Rituximab plus chemotherapy as first-line treatment in Chinese patients with diffuse large B-cell lymphoma in routine practice: a prospective, multicentre, non-interventional study

Baseline characteristics. Table S2. Comparisons of baseline characteristics between patients with history of heart or liver diseases and patients without heart or liver diseases. Table S3. Summary of AEs. Table S4. Summary of hepatic AEs. Table S5. Summary of cardiovascular AEs. Table S6. Summary of screening results of DLBCL patients prior to DLBCL treatment. Table S7 Comparisons of baseline characteristics between HBsAg-pos or HBsAg-neg/HBcAb-pos patients with double-neg patients. Figure S1. HBV DNA testing prior to R-chemo. Figure S2. HBV infection monitoring in R-chemo treated DLBCL patients. (DOCX 276 kb