Adaptive Interacting Multiple Model Research Articles

An enhanced mobile localization algorithm integrating multiple AUKF models for mixed indoor environments

Abstract The positioning technology based on ultra-wideband ranging has been widely applied in the field of indoor positioning due to its excellent localization capabilities. However, mixed line-of-sight (LOS) and non-LOS (NLOS) indoor environments severely constrain positioning accuracy. To address this issue, we propose an innovative algorithm based on the adaptive unscented Kalman filter (AUKF) and interactive multiple model (IMM), designed to significantly enhance positioning accuracy in mixed indoor environments by mitigating the impact of NLOS errors and inaccurate process noise. Firstly, recognizing the distinct characteristics of ranging errors in indoor environments, we develop LOS and NLOS ranging models separately. Based on these models, the unscented Kalman filters are constructed for LOS and NLOS environments to accurately simulate the mixed LOS/NLOS indoor environments. Secondly, determining the statistical characteristics of process noise is challenging, often leading to degraded filter performance. We address this issue by proposing an environment-based AUKF algorithm, which significantly enhances the robustness and accuracy of the positioning system. Finally, the environment-based AUKFs are integrated into the IMM framework to constrain NLOS errors and achieve precise positioning effectively. Simulations, open-source dataset validation and experimental results demonstrate that the proposed algorithm significantly enhances the accuracy and stability of mobile target positioning in mixed LOS/NLOS indoor environments.

Adaptive IMM Smoothing Algorithms for Jumping Markov System With Mismatched Measurement Noise Covariance Matrix

Adaptive IMM Smoothing Algorithms for Jumping Markov System With Mismatched Measurement Noise Covariance Matrix

A Fusion Strategy for Vehicle Positioning at Intersections Utilizing UWB and Onboard Sensors.

For vehicle positioning applications in Intelligent Transportation Systems (ITS), lane-level or even more precise localization is desired in some typical urban scenarios. With the rapid development of wireless positioning technologies, ultrawide bandwidth (UWB) has stood out and become a prominent approach for high-precision positioning. However, in traffic scenarios, the UWB-based positioning method may deteriorate because of not-line-of-sight (NLOS) propagation, multipath effect and other external interference. To overcome these problems, in this paper, a fusion strategy utilizing UWB and onboard sensors is developed to achieve reliable and precise vehicle positioning. It is a two-step approach, which includes the preprocessing of UWB raw measurements and the global estimation of vehicle position. Firstly, an ARIMA-GARCH model to address the NLOS problem of UWB at vehicular traffic scenarios is developed, and then the NLOS of UWB can be detected and corrected efficiently. Further, an adaptive IMM algorithm is developed to realize global fusion. Compared with traditional IMM, the proposed AIMM is capable of adjusting the model probabilities to make them better matching for current driving conditions, then positioning accuracy can be improved. Finally, the method is validated through experiments. Field test results verify the effectiveness and feasibility of the proposed strategy.

Adaptive Variable Structure Interacting Multiple Model Tracking Algorithm for Hypersonic Glide Vehicle

Adaptive Variable Structure Interacting Multiple Model Tracking Algorithm for Hypersonic Glide Vehicle

Adaptive filtering and smoothing algorithm based on variable structure interactive multiple model

For maneuvering target tracking, a novel adaptive variable structure interactive multiple model filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. Firstly, an accurate model of the variable structure interactive multiple model algorithm is established. Secondly, by constructing a new model subset based on the original model subsets, the matching accuracy between the model subset and the actual maneuvering mode of the target is improved. Then, the AVSIMMFS algorithm is obtained by smoothing the filtered data of the new model subset. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Finally, in order to verify the effectiveness of the algorithm, the simulation is carried out on two cases. The simulation results show that the tracking performance of AVSIMMFS algorithm is better than other methods and has lower calculation cost.

A Pearson-Type VII Distribution With Adaptive Parameters Selection-Based Interacting Multiple Model Kalman Filter

Existing robust filters under generalized nonstationary noises conditions are difficult to choose suitable prior parameters, this brief proposed a Pearson-type VII distribution with adaptive parameters selection based interacting multiple model (IMM) Kalman filter (IMMPVII KF). In the model conditional filtering process, both the one-step prediction and the measurement likelihood are modeled as Pearson-type VII distributions. They are decomposed into Gaussian-Gamma Hierarchies (GGH), which are then matched to the time-varying heavy-tailed properties of the noises by pre-selecting the sets of shape and rate parameters and the variational Bayesian (VB) technique. Finally, a new model probability update method for filter under non-Gaussian conditions is derived. Simulation results show that the filter proposed in this brief has better robustness and adaptability to generalized non-stationary noises than the existing filters.

Maneuvering target tracking based on an adaptive variable structure interactive multiple model filtering and smoothing algorithm

For maneuvering target tracking, the interactive multiple model (IMM) algorithm and its variants have shown good performance, among which the variable structure IMM (VSIMM) algorithm is the most widely studied one. The approximation degree of the algorithm and the matching degree of the model subset will affect the performance of the VSIMM. In addition, considering that smoothing can provide better estimation of the target state, a new adaptive VSIMM filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. First, an accurate model of the VSIMM algorithm is established, in which the IMM is run in parallel with and independently from the different model subsets, and the state estimation of the model subset with the highest probability is selected as the final estimation result. Then, an adaptive VSIMM (AVSIMM) algorithm was designed based on the VSIMM algorithm. The adaptation is reflected in the construction of a new model subset from the original model subsets, which improves the matching degree between the model subset and the actual maneuvering model of the target. Finally, by smoothing the filtering data of the AVSIMM algorithm, the AVSIMMFS algorithm is obtained. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Simulation results show that the tracking performance of the AVSIMMFS algorithm is better than that of other methods.

Road slope estimation based on acceleration adaptive interactive multiple model algorithm for commercial vehicles

Road slope is an important external variable in vehicle dynamic control systems. However, it is a challenging problem to estimate road slope accurately for commercial vehicles due to the coupling problem among the mass, road slope and pitch angle. To solve this problem, a novel road slope estimation scheme with the correction of pitch angle is proposed. First, the coupling problem between road slope and body pitch is analyzed from the perspective of sensor signals. Next, the driving conditions are divided into gentle and strong scenarios, abstracted to the smooth driving model (SDM) and intensive driving model (IDM), respectively. SDM alone cannot guarantee accuracy under intensive scenarios, while IDM alone converges slowly under smooth driving scenarios. The acceleration adaptive interactive multiple model (AAIMM) algorithm is then designed to combine the models and determine which model is the most appropriate under different driving intensities. At last, the sensor-less pitch angle correction strategy based on the suspension deformation model is presented and the particle swarm optimization (PSO) algorithm is used to optimize the suspension stiffness off-line. The simulations and road tests indicate the effectiveness and accuracy of the proposed road slope estimation scheme.

Adaptive Hybrid Robust Filter for Multi-Sensor Relative Navigation System

This paper provides an adaptive hybrid robust filter (AHRF) for multi-sensor relative navigation systems that can be used to support cooperative intelligent transport systems. It is known that Huber’s M-estimation based robust filter and the fault detection and exclusion (FDE) based RAIM filter each has its own drawbacks, depending on the nature of the observation error biases. Based on the interactive multiple model (IMM) framework, our proposed AHRF in this paper can take advantage of both filters in a complementary sense. A new adaptive IMM (AIMM) algorithm with Markov transition probability prediction is proposed to allow AHRF to switch efficiently between the two filters. We consider the relative navigation system with Global Navigation Satellite System (GNSS) and ultra-wideband (UWB) as observations to verify AHRF in three cases of possible failure modes and multipath-induced errors. Our results show that AHRF outperforms both the FDE and robust filter in all cases. AHRF framework can be further adapted to include many other fault-tolerant filters to improve the robustness of multi-sensor relative navigation system even further.

An adaptive IMM filter for jump Markov systems with inaccurate noise covariances in the presence of missing measurements

This paper derives a Kullback-Leibler average (KLA) based adaptive interacting multiple model (IMM) filter for jump Markov systems with inaccurate noise covariances in the presence of missing measurements. Firstly, a switching error model (SEM) is constructed to model the probability density function of measurement likelihood. The SEM can automatically choose appropriate error model for the real measurement or pure measurement noise by estimating a binary indicator. Secondly, by assigning conjugate priors on inaccurate noise covariances and binary indicator, the state, noise covariances and binary indicator are jointly estimated based on variational Bayesian (VB) technique. Finally, by using the KLA fusion scheme to fuse the conditioned estimates from every mode in the mixing and output phase, an adaptive IMM filter is derived. Simulation results demonstrate that the proposed filter achieves better performance than existing typical algorithms.

Technical Support System for Power System Load Modeling

Background: In order to better establish accurate load models and meet the practical demand of current power system load modeling, it is necessary to establish related technical support systems for power system load modeling. Objective: The purpose of the paper was to construct the overall scheme of power system load modeling technology support system and complete the development of the system. Methods: Based on the modular design idea, the system adopts a multi-level architecture combining B/S and C/S modes, covering the key technologies of substation classification based on selforganizing neural network algorithm, load dynamic characteristic classification based on lifting wavelet packet algorithm, load model parameter identification and load modeling based on adaptive interactive multiple model (AIMM) algorithm. Results: After actual operation verification, the built technology support system can well solve the related problems of substation classification, load dynamic characteristic classification, load model parameter identification and load modeling. It has the characteristics of a friendly man-machine interface, simple operation and strong extensibility. Conclusion: The built technology support system provides powerful technical support for improving the load data management level of the power system and establishing an accurate load model, and promotes the practical process of load modeling theory.

Cooperative detection based on the adaptive interacting multiple model-information filtering algorithm

This paper develops a multiaircraft cooperative detection scheme for effectively improving the accuracy when detecting the highly maneuvering target, where the cooperative estimate and observation trajectory planning of multiaircraft are addressed simultaneously. Based on the error compression ratio of the Markov matrix, an adaptive IMM algorithm is proposed for single detection, the convergence characteristic of which is theoretically proven meanwhile. Then, an adaptive IMM-information filtering algorithm is derived by embedding information filtering into the adaptive IMM algorithm for the fusion of the detection information. Next, the coordinated planning of observation trajectories is achieved for multiaircrafts by relying on receding horizon optimization (RHO) and the detection information. The cooperative detection and trajectory planning scheme is illustrated by the simulation results.

GPS/INS integration based on adaptive interacting multiple model

The extended Kalman filter (EKF) had widely been used in the inertial navigation system (INS) and global positioning system (GPS) integrated navigation system. Nevertheless, the EKF contains instability caused by linearisation and numerous calculations of the Jacobian matrix. To solve the problem, the adaptive interacting multiple model (AIMM) filter algorithm is proposed in order to achieve a better navigation solution. The soft-switching feature, which is offered by interacting multiple model, allows conversion of process noise between lower and upper limits, meanwhile the measurement covariance is adjusted online by Sage adaptive filter. Considering the need to update the pseudo-range and Doppler data, the updating strategy of classification measurement is proposed. The results of the GPS/INS integrated navigation are estimated in the form of data of real ship, and experimental results indicate that the higher position accuracy can be obtained in AIMM filter.

Multiple Model Kalman and Particle Filters and Applications: A Survey

Abstract Kalman Filters (KF) is a recursive estimation algorithm, a special case of Bayesian estimators under Gaussian, linear and quadratic conditions. For non-linear systems, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) provide first and higher order linearization approximations. Particle Filters (PF), on the other hand, are sequential Monte Carlo methods to provide estimations for non-linear non-Gaussian problems. For complex systems, Kalman or Particle Filter based single model filters may not be sufficient to model the system behaviour. Multiple Model (MM) Filters achieve more reliable estimates by using more than one filter with different models in parallel and the outputs of each filter are fused by assigning a probability to each filter. The most common methods used in the literature for multiple model estimation are Multiple Model Adaptive Estimation (MMAE) and Interacting Multiple Model (IMM). This paper presents an overview of the recent research on multiple model filters.

Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System

The extended Kalman filter (EKF) as a primary integration scheme has been applied in the Global Positioning System (GPS) and inertial navigation system (INS) integrated system. Nevertheless, the inherent drawbacks of EKF contain not only instability caused by linearization, but also massive calculation of Jacobian matrix. To cope with this problem, the adaptive interacting multiple model (AIMM) filter method is proposed to enhance navigation performance. The soft-switching characteristic, which is provided by interacting multiple model algorithm, permits process noise to be converted between upper and lower limits, and the measurement covariance is regulated by Sage adaptive filtering on-line Moreover, since the pseudo-range and Doppler observations need to be updated, an updating policy for classified measurement is considered. Finally, the performance of the GPS/INS integration method on the basis of AIMM is evaluated by a real ship, and comparison results demonstrate that AIMM could achieve a more position accuracy.

Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking.

Networked multiple sensors are used to solve the problem of maneuvering target tracking. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information-weighted consensus filter for maneuvering target tracking is proposed. The pseudo measurement matrix is computed with unscented transform to utilize the information form of measurements, which is necessary for consensus iterations. To improve the maneuvering target tracking accuracy and get a unified estimation in each sensor node across the entire network, the adaptive current statistical model is exploited to update the estimate, and the information-weighted consensus protocol is applied among neighboring nodes for each dynamic model. Based on posterior probabilities of multiple models, the final estimate of each sensor is acquired with weighted combination of model-conditioned estimates. Experimental results illustrate the superior performance of the proposed algorithm with respect tracking accuracy and agreement of estimates in the whole network.

Maneuvering Acceleration Estimation Algorithm Using Doppler Radar Measurement

An algorithm to estimate the tangential and normal accelerations directly using the Doppler radar measurement in an online closed loop form is proposed. Specific works are as follows: first, the tangential acceleration and normal acceleration are taken as the state variables to establish a linear state transition equation; secondly, the decorrelation unbiased conversion measurement Kalman filter (DUCMKF) algorithm is proposed to deal with the strongly nonlinear measurement equation; thirdly, the geometric relationship between the range rate and the velocity direction angle is used to obtain two estimators of the velocity direction angle; finally, the interactive multiple model (IMM) algorithm is used to fuse the estimators of the velocity direction angle and then the adaptive IMM of current statistical model based DUCMKF (AIMM-CS-DUCMKF) is proposed. The simulation experiment results show that the accuracy and stability of DUCMKF are better than the sequential extended Kalman filter algorithm, the sequential unscented Kalman filter algorithm, and converted measurement Kalman filter algorithms; on the other hand they show that the AIMM-CS-DUCMKF can obtain the high accuracy of the tangential and normal accelerations estimation algorithm.

GPS/BDS/INS tightly coupled integration accuracy improvement using an improved adaptive interacting multiple model with classified measurement update

An Extended Kalman Filter (EKF) is commonly used to fuse raw Global Navigation Satellite System (GNSS) measurements and Inertial Navigation System (INS) derived measurements. However, the Conventional EKF (CEKF) suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance. In this research, an Adaptive Interacting Multiple Model (AIMM) filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model (IMM) algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS) were post-processed in differential mode. The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpart for GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.

Kurtosis-based IMM filter for multiple MEMS gyroscopes fusion

PurposeThe purpose of this study is to explore a signal processing method to improve the angular rate accuracy of micro-electro-mechanical system (MEMS) gyroscope by combining numerous gyroscopes.Design/methodology/approachTo improve the dynamic performance of the signal processing method, the interacting multiple model (IMM) can be applied to the fusion of gyroscope array. However, the standard IMM has constant Markov parameter, which may reduce the model switching speed. To overcome this problem, an adaptive IMM filter is developed based on the kurtosis of the gyroscope output, in which the transition probabilities are adjusted online by utilizing the dynamic information of the rate signal.FindingsThe experimental results indicate that the precision of the gyroscope array composed of six gyroscopes increases significantly and the kurtosis-based adaptive Markov parameter IMM filter (K-IMM) performs better than the baseline methods, especially under dynamic conditions. These experiments prove the validity of the proposed fusion method.Practical implicationsThe proposed method can improve the accuracy of MEMS gyroscopes without breakthrough on hardware, which is necessary to extend their utility while not restricting the overwhelming advantages.Original/valueA K-IMM algorithm is proposed in this paper, which is used to improve the angular rate accuracy of MEMS gyroscope by combining numerous gyroscopes.

Stochastic Hybrid System Actuator Fault Diagnosis by Adaptive Estimation

Based on the interacting multiple model (IMM) estimator for hybrid system state estimation and on the adaptive Kalman filter for time varying system joint state-parameter estimation, a new algorithm, the adaptive IMM estimator, is proposed in this paper for actuator fault diagnosis in stochastic hybrid systems. The working modes of the considered hybrid systems are described by stochastic state-space models, and the mode transitions are characterized by a Markov model. Actuator faults are modeled as parameter changes, and the related fault diagnosis problem is solved by the proposed adaptive IMM estimator through joint state-parameter estimation. Numerical examples are presented to illustrate the performance of the proposed method.