High Estimation Accuracy Research Articles

Accuracy and clinical effectiveness of risk prediction tools for pressure injury occurrence: An umbrella review.

Pressure injuries (PIs) pose a substantial healthcare burden and incur significant costs worldwide. Several risk prediction tools to allow timely implementation of preventive measures and a subsequent reduction in healthcare system burden are available and in use. The ability of risk prediction tools to correctly identify those at high risk of PI (prognostic accuracy) and to have a clinically significant impact on patient management and outcomes (effectiveness) is not clear. We aimed to evaluate the prognostic accuracy and clinical effectiveness of risk prediction tools for PI and to identify gaps in the literature. The umbrella review was conducted according to Cochrane guidance. Systematic reviews (SRs) evaluating the accuracy or clinical effectiveness of adult PI risk prediction tools in any clinical settings were eligible. Studies on paediatric tools, sensor-only tools, or staging/diagnosis of existing PIs were excluded. MEDLINE, Embase, CINAHL, and EPISTEMONIKOS were searched (inception to June 2024) to identify relevant SRs, as well as Google Scholar (2013 to 2024) and reference lists. Methodological quality was assessed using adapted AMSTAR-2 criteria. Results were described narratively. We identified 26 SRs meeting all eligibility criteria with 19 SRs assessing prognostic accuracy and 11 assessing clinical effectiveness of risk prediction tools for PI (4 SRs assessed both aspects). The 19 SRs of prognostic accuracy evaluated 70 tools (39 scales and 31 machine learning (ML) models), with the Braden, Norton, Waterlow, Cubbin-Jackson scales (and modifications thereof) the most evaluated tools. Meta-analyses from a focused set of included SRs showed that the scales had sensitivities and specificities ranging from 53% to 97% and 46% to 84%, respectively. Only 2/19 (11%) SRs performed appropriate statistical synthesis and quality assessment. Two SRs assessing machine learning-based algorithms reported high prognostic accuracy estimates, but some of which were sourced from the same data within which the models were developed, leading to potentially overoptimistic results. Two randomised trials assessing the effect of PI risk assessment tools (within the full test-intervention-outcome pathway) on the incidence of PIs were identified from the 11 SRs of clinical effectiveness; both were included in a Cochrane SR and assessed as high risk of bias. Both trials found no evidence of an effect on PI incidence. Limitations included the use of the AMSTAR-2 criteria, which may have overly focused on reporting quality rather than methodological quality, compounded by the poor reporting quality of included SRs and that SRs were not excluded based on low AMSTAR-2 ratings (in order to provide a comprehensive overview). Additionally, diagnostic test accuracy principles, rather than prognostic modelling approaches were heavily relied upon, which do not account for the temporal nature of prediction. Available systematic reviews suggest a lack of high-quality evidence for the accuracy of risk prediction tools for PI and limited reliable evidence for their use leading to a reduction in incidence of PI. Further research is needed to establish the clinical effectiveness of appropriately developed and validated risk prediction tools for PI.

Commercial vehicle state estimation based on Interactive Multi-Model Square Root Cubature Kalman Filtering

Abstract Accurate and reliable vehicle state information is essential for ensuring vehicle safety and stability. While Interactive Multiple Model Square Root Cubature Kalman Filtering (IMM-SRCKF) has shown promise in state estimation, this work specifically addresses the unique challenges of achieving high-accuracy and real-time state estimation for commercial vehicles within complex driving scenarios. A nonlinear three-degree-of-freedom vehicle dynamic model, encompassing longitudinal, lateral, and yaw motions, was developed as a foundation. State space and observation equations were then derived. The IMM-SRCKF algorithm was strategically utilized and adapted to enhance the selection of process and measurement noise covariance matrices, effectively integrating multiple model strategies with the inherent advantages of square root Kalman filtering. This approach enables real-time dynamic adjustment of each sub-model's weights. Co-simulation results using TruckSim and MATLAB/Simulink demonstrate that the proposed IMM-SRCKF method offers significant improvements over traditional techniques like the Extended Kalman Filter (EKF), Cubature Kalman Filter (CKF), and Square Root Cubature Kalman Filter (SRCKF), particularly in demanding double lane change and serpentining maneuvers. The notably lower Normalized Root Mean Square Error (NRMSE) values confirm the substantial enhancements in both accuracy and stability for commercial vehicle state estimation.

Capacity estimation of lithium-ion battery through interpretation of electrochemical impedance spectroscopy combined with machine learning

Lithium-ion batteries inevitably undergo degradation over extended use, making precise capacity estimation essential for reliable state monitoring and health prognostics. However, conventional capacity estimation methods, which rely predominantly on time-domain information, offer limited insights due to the complex degradation mechanisms intrinsic to lithium-ion batteries. Accordingly, a capacity estimation approach leveraging partial frequency electrochemical impedance spectroscopy data integrated with machine learning is proposed. A reconstructed simplified fractional-order model characterized by a minimal set of parameters and superior fitting performance is introduced to extra health indicators from EIS measurement. Detailed equivalent circuit parameter extraction is performed, and subsequently incorporated into an enhanced Gaussian process regression model for capacity estimation. The proposed approach is validated using datasets derived from real-driving profiles including comprehensive comparisons, demonstrating its feasibility and effectiveness. Experimental results confirm that the framework achieves high estimation accuracy, strong generalization, and robust performance, even in scenarios with limited data availability and training noise presence, with a root mean square error not exceeding 0.81 %, a mean absolute error below 0.62 %, and a maximum absolute error of no more than 0.0653 Ah.

Continuous single-ended depth-of-interaction measurement using highly multiplexed signals and artificial neural networks

Objective. This study aims to enhance positron emission tomography (PET) imaging systems by developing a continuous depth-of-interaction (DOI) measurement technique using a single-ended readout. Our primary focus is on reducing the number of readout channels in the scintillation detectors while maintaining accurate DOI estimations, using a high-pass filter-based signal multiplexing technique combined with artificial neural networks (ANNs).Approach. Instead of reading out all 64 signals from an 8 × 8 silicon photomultiplier (SiPM) array for DOI estimation, the proposed method technique reduces the signals into just four channels by applying high-pass filters with different time constants. To recover the original signal amplitudes, an ANN is used to demultiplex the multiplexed signals. Specifically, the ANN processes the sampled waveforms of these four multiplexed signals and estimates the energy information of the original 8 × 8 SiPM channels. In this study, two DOI estimation strategies were explored for a continuous DOI (cDOI) PET detector utilizing triangular teeth-shaped reflectors: a 'single-step estimation' method directly estimating DOI from multiplexed signals, and a 'two-stage cascade estimation' method that first demultiplexes the signals and then estimates DOI. The performances of proposed strategies were validated using data irradiated at five steps (2, 6, 10, 14, and 18 mm).Results. The signal amplitude of row/column summed signals, which were recovered using the proposed ANN-based de-multiplexing, showed strong correlation with ground truth (e.g.R2= 0.98 for 125 MHz digitizer sampling rate). Moreover, both the single-step and two-stage estimation methods achieved high accuracy in DOI estimation, with an average DOI resolution of 4.86 and 5.11 mm respectively.Significance. This novel signal multiplexing technique significantly reduces the number of required readout channels, making cDOI PET more cost-effective.

A Robust Method for Real Time Intraoperative 2D and Preoperative 3D X-Ray Image Registration Based on an Enhanced Swin Transformer Framework

In image-guided surgery (IGS) practice, combining intraoperative 2D X-ray images with preoperative 3D X-ray images from computed tomography (CT) enables the rapid and accurate localization of lesions, which allows for a more minimally invasive and efficient surgery, and also reduces the risk of secondary injuries to nerves and vessels. Conventional optimization-based methods for 2D X-ray and 3D CT matching are limited in speed and precision due to non-convex optimization spaces and a constrained searching range. Recently, deep learning (DL) approaches have demonstrated remarkable proficiency in solving complex nonlinear 2D–3D registration. In this paper, a fast and robust DL-based registration method is proposed that takes an intraoperative 2D X-ray image as input, compares it with the preoperative 3D CT, and outputs their relative pose in x, y, z and pitch, yaw, roll. The method employs a dual-channel Swin transformer feature extractor equipped with attention mechanisms and feature pyramid to facilitate the correlation between features of the 2D X-ray and anatomical pose of CT. Tests on three different regions of interest acquired from open-source datasets show that our method can achieve high pose estimation accuracy (mean rotation and translation error of 0.142° and 0.362 mm, respectively) in a short time (0.02 s). Robustness tests indicate that our proposed method can maintain zero registration failures across varying levels of noise. This generalizable learning-based 2D (X-ray) and 3D (CT) registration algorithm owns promising applications in surgical navigation, targeted radiotherapy, and other clinical operations, with substantial potential for enhancing the accuracy and efficiency of image-guided surgery.

Advanced computational strategy for damage identification of offshore jacket platforms

AbstractIn this study, an efficient surrogate‐assisted grey wolf optimizer (GWO) is presented by combining Kriging‐based active learning to identify damages in jacketed platforms based on modal analysis. The use of active learning in parallel with GWO significantly reduced the number of calls to the objective function and increased the accuracy of the algorithm's search in the problem space. The proposed approach was first evaluated on four benchmark problems, and its performance was validated against original GWO, particle swarm optimization (PSO), and genetic algorithm (GA) techniques. Then, by generating artificial damage scenarios on a real jacket platform in ABAQUS software, it was evaluated for the identification of damaged members. The results indicated high accuracy in estimation and an appropriate convergence rate in solving the high‐dimensional and complicated problem of damage detection of jacketed platforms. In such a way that the error rate of damage severity estimation in scenarios 1 and 2 was, on average, 3% and 5%, respectively. Meanwhile, the damage position was correctly estimated, and the call rate of the function was reduced by 50%. The efficiency of the proposed approach shows that it can be used for further works on the reliability‐based design of jacket structures.

Effects of Sampling Design on Population Abundance Estimation of Ichthyoplankton in Coastal Waters

The abundance, spatial distribution of and dynamic changes in ichthyoplankton species affect the recruitment and fish population dynamics, which are fundamental for stock assessment and fisheries management. An evaluation of alternative sampling designs needs to be carried out to determine the optimal scheme that is cost-effective in collecting high-quality ichthyoplankton data. A simulation study was conducted to evaluate the performances and consistency of six potential sampling designs for an ichthyoplankton survey in the coastal waters of the central and southern Yellow Sea. Relative estimation error (REE) and relative bias (RB) were used to measure the performances in estimating the population abundances of five target ichthyoplankton species in different sampling designs. In general, the two systematic sampling (SYS) designs had high precision and accuracy estimation and remained stable over years for estimating the population abundance of ichthyoplankton species compared with the other four sampling designs. SYS did not result in the overestimation or underestimation of the mean population abundance. Most sampling designs showed relatively high accuracy in abundance estimation when sample sizes were higher than medium levels. This study could improve the performances of sampling designs of ichthyoplankton surveys and provide a reference for the further optimization of sampling designs.

Accurate Joint Estimation of Position and Orientation Based on Angle of Arrival and Two-Way Ranging of Ultra-Wideband Technology

In wireless sensor networks (WSNs), ultra-wideband (UWB) technology is essential for robot localization systems, especially for methods of the simultaneous estimation of position and orientation. However, current approaches frequently depend on rigid body models, which require multiple base stations and lead to substantial equipment costs. This paper presents a cost-effective UWB SL model utilizing the angle of arrival (AOA) and double-sided two-way ranging (DS-TWR). To improve localization accuracy, we propose a self-localization algorithm based on constrained weighted least squares (SL-CWLS), integrating a weighted matrix derived from a measured noise model. Additionally, we derive the constrained Cramér–Rao lower bound (CCRLB) to analyze the performance of the proposed algorithm. Simulation results indicate that the proposed method achieves high estimation accuracy, while real-world experiments validate the simulation results.

Estimation of Dendrocalamus giganteus leaf area index by combining multi-source remote sensing data and machine learning optimization model.

The Leaf Area Index (LAI) is an essential parameter that affects the exchange of energy and materials between the vegetative canopy and the surrounding environment. Estimating LAI using machine learning models with remote sensing data has become a prevalent method for large-scale LAI estimation. However, existing machine learning models have exhibited various flaws, hindering the accurate estimation of LAI. Thus, a new method for large-scale estimation of Dendrocalamus giganteus LAI was proposed, which integrates ICESat-2/ATLAS, and Sentinel-1/-2 data, and refines machine learning models through the application of Bayesian Optimization (BO), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Simulated Annealing (SA). First, spatial interpolation was performed using the Sequential Gaussian Conditional Simulation (SGCS) method. Then, multi-source remote sensing data were leveraged to optimize feature variables through the Pearson correlation coefficient approach. Subsequently, optimization algorithms were applied to Random Forest Regression (RFR), Gradient Boosting Regression Tree (GBRT), and Support Vector Machine Regression (SVR) models, leading to efficient large-scale LAI estimation. The results showed that the BO-GBRT model achieved high accuracy in LAI estimation, with a coefficient of determination (R 2) of 0.922, a root mean square error (RMSE) of 0.263, a mean absolute error (MAE) of 0.187, and an overall estimation accuracy (P 1) of 92.38%. Compared to existing machine learning methods, the proposed approach demonstrated superior performance. This method holds significant potential for large-scale forest LAI inversion and can facilitate further research on other forest structure parameters.

A Joint Prediction of the State of Health and Remaining Useful Life of Lithium-Ion Batteries Based on Gaussian Process Regression and Long Short-Term Memory

To comprehensively evaluate the current and future aging states of lithium-ion batteries, namely their State of Health (SOH) and Remaining Useful Life (RUL), this paper proposes a joint prediction method based on Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) networks. First, health features (HFs) are extracted from partial charging data. Subsequently, these features are fed into the GPR model for SOH estimation, generating SOH predictions. Finally, the estimated SOH values from the initial cycle to the prediction start point (SP) are input into the LSTM network in order to predict the future SOH trajectory, identify the End of Life (EOL), and infer the RUL. Validation on the Oxford Battery Degradation Dataset demonstrates that this method achieves high accuracy in both SOH estimation and RUL prediction. Furthermore, the proposed approach can directly utilize one or more health features without requiring dimensionality reduction or feature fusion. It also enables RUL prediction at the early stages of a battery’s lifecycle, providing an efficient and reliable solution for battery health management. However, this study is based on data from small-capacity batteries and does not yet encompass applications in large-capacity or high-temperature scenarios. Future work will focus on expanding the data scope and validating the model’s performance in real-world systems, driving its application in practical engineering scenarios.

Aircraft Load Estimation Using Linear Parameter-Varying System-Based Hybrid Observers

The hybrid loads observer has demonstrated its precision in estimating structural loads and wind disturbances in prior applications. This level of precision is attained by combining a high-fidelity physical, nonlinear flight dynamics model with a data-driven correction model. To mitigate the typically high computational effort, the nonlinear model is substituted in this work with a linear parameter-varying (LPV) system. Therefore, the nonlinear model is approximated by scheduled Linear Time-Invariant models derived from Jacobian linearization. The robustness of this novel approach against parameter uncertainties is evaluated through simulated flight test studies using a subscale test aircraft as an example. It is demonstrated that increased model uncertainties lead to wind estimation accuracy reduction. Additionally, increased parameter uncertainties adversely affect the accuracy of structural load estimation within the model-based (physical) part of the observer. Nonetheless, this loss of accuracy can be compensated by the correction model, leading to the typical high load estimation accuracy of the hybrid loads observer. Finally, experimental data from wind-tunnel tests, utilizing a 1-degree-of-freedom representative test wing, confirms the high estimation accuracy of the LPV-based hybrid loads observer. Despite employing low-fidelity models, achieving high accuracy is feasible while maintaining the characteristic low complexity of the correction model.

Identification and High-Accuracy Range Estimation With Doppler Tags in Radar Applications

Identification and High-Accuracy Range Estimation With Doppler Tags in Radar Applications

Online Learning With Joint State and Model Estimation

ABSTRACTModel‐based state observers require high‐quality models to deliver accurate state estimates. However, due to time or cost shortage, modeling simplifications or numerical issues, models often have severe inaccuracies that may lead to insufficient and deficient control. Instead of attempting to iteratively model these deviations, we address the challenge by the concept of joint estimation. Thus, we assume a linear combination of suitable functions to approximate the inaccuracies. The parameters of the linear combination are supposed to be time invariant and augment the model's state. Subsequently, the parameters can be identified simultaneously to the states within the observer. Referring to the principle of Occam's razor, the parameters are claimed to be sparse. Our former work shows that estimating states and model inaccuracies simultaneously by a sparsity promoting unscented Kalman filter yields not only high accuracy but also provides interpretable representations of underlying inaccuracies. Based on this work, our contribution is twofold: First, we apply our approach finally on a real‐world test bench, namely a golf robot. Within the experimental setting, we investigate closed loop behavior as well as how suitable functions need to be chosen to approximate the inaccuracies in a physically interpretable way. Results do not only provide high state estimation accuracy but also meaningful insights into the system's inaccuracies. Second, we discuss and establish a method to automatically adapt and update the model based on collected data of the linear combination during operation. Examining past parameter estimates by principal component analysis, a moving window is utilized to extract the most dominant functions. These are kept characterizing the model inaccuracies, while nondominant functions are automatically neglected and refilled with novel function candidates. After analysis and rebuilding, this updated function set is subsequently fed back into the joint estimation loop and deployed for further estimation. Hence, we give a holistic paradigm of how to analyze and combat model inaccuracies while ensuring high state estimation accuracy. Within this setting, we once more investigate closed loop behavior and yield promising results. In conclusion, we show that the proposed observer provides a helpful tool to guarantee high estimation accuracy for models with severe inaccuracies or for situations with occurring deviations during operation, for example, due to mechanical wear or temperature changes.

An FDA-MIMO radar 2-D parameter estimation algorithm based on graph signal processing

ABSTRACT To improve the range-angle estimation accuracy of frequency diverse array multiple Input multiple output (FDA-MIMO) radar at low SNR, this letter proposes a joint estimation algorithm of FDA-MIMO radar target range-angle based on graph signal processing (GSP). Firstly, the adjacency matrix is constructed according to the FDA-MIMO radar signal model, and then the eigenvector of the target is extracted by the eigen-decomposition of the adjacency matrix. Secondly, the echo signal is processed by graph Fourier transform (GFT). Finally, the spectral peak search response function is established to obtain the range-angle information of the target. The experimental results show that the proposed algorithm has higher estimation accuracy than the MUSIC algorithm when the SNR is lower than 0 dB, and it has certain advantages and application potential for the parameter estimation of weak targets.

Development of an Extended State Observer for Monitoring of a PWR Based on a Two-Point Kinetic Model

Accurate estimation of unmeasured system states and disturbances in a pressurized water reactor (PWR) is essential for effective control, operation optimization, and safety monitoring. To this end, this paper investigates the estimation of unmeasured system states and disturbances of the PWR system during load-following operation. First, a mathematical model for the PWR system is established based on the two-point kinetics equations with one equivalent delayed neutron precursor group. Subsequently, an extended state observer (ESO) integration scheme, incorporating two coupled ESOs, is constructed to estimate unmeasured system states, including relative density of delayed neutron precursor, average fuel temperature, total reactivity, xenon concentration, and iodine concentration, along with time-varying disturbances, with the use of measurements of the PWR system only. According to the Lyapunov stability theorem, it is proved that the estimation error dynamic of the proposed ESO integration scheme is uniformly ultimately bounded stable. Finally, simulation results confirm that the proposed ESO integration scheme provides higher estimation accuracy and stronger robustness against measurement noises, model uncertainties, and external disturbances compared to both a high-gain observer and a high-order sliding mode observer.

Digital Twin-Assisted Lightpath Provisioning and Nonlinear Mitigation in C+L+S Multiband Optical Networks.

Multiband (MB) optical transmission targets increasing the capacity of operators' optical transport networks. However, nonlinear impairments (NLI) affect each optical channel in the C+L+S bands differently, and, therefore, the routing and spectrum assignment (RSA) problem needs to be complemented with fast and accurate tools to consider the quality of transmission (QoT) within the provisioning process. This paper proposes a digital twin-assisted approach for lightpath provisioning to provide a complete solution for the RSA problem that ensures the required QoT in MB optical networks. The OCATA time domain digital twin is proposed, not only to estimate the QoT of a selected path but also to support the QoT-based channel assignment process. OCATA is based on a Deep Neural Network (DNN) to model the propagation of the optical signal. However, because of the different impacts of nonlinear noise on each channel and the large number of channels that need to be considered in C+L+S MB scenarios, OCATA needs to be adapted to make it scalable, while keeping its high accuracy and fast QoT estimation characteristics. In consequence, a complete methodology is proposed in this work that limits the number of channels being modeled to just a few. Moreover, OCATA-MB helps to mitigate NLI noise by programming the receiver at the provisioning time and thus with very little complexity compared to its equivalent implemented during the operation. NLI noise mitigation can be applied in the case when a lightpath cannot be provisioned because none of the available channels can provide the required QoT, making it an advantageous tool for reducing connection blocking. Exhaustive simulation results demonstrate the remarkable accuracy of OCATA-MB in estimating the QoT for any channel. Interestingly, by utilizing the proposed OCATA-MB-assisted lightpath provisioning approach, a reduction of the blocking ratio exceeding 50% when compared to traditional approaches is shown when NLI noise mitigation is not applied. If NLI mitigation is implemented, an additional over 50% blocking reduction is achieved.

IGBT Junction Temperature Estimation Based on External Parameters of Multiple Drive Devices in Practical Industrial Scenario

ABSTRACTThe insulated gate bipolar transistor (IGBT) is one of the most important power semiconductor devices in power electronics and is also prone to failure. High junction temperature and junction temperature fluctuation of IGBT are the main causes of IGBT module aging failure. The high‐precision monitoring of the junction temperature of the IGBT module is a prerequisite for IGBT life prediction, which is crucial for reducing maintenance costs and improving equipment reliability. Therefore, an IGBT junction temperature estimation method based on long short‐term memory (LSTM) neural network and sliding window estimation model is proposed and applied in practical industrial scenarios. This method uses the operating data of the motor drive device in the actual industrial application scenario as the training and test data set and uses the external operating parameters of the IGBT module to estimate the junction temperature of the IGBT module. Compared with the internal operating parameters of the IGBT module based on switching transient, the external operating parameters are easier to collect and process, and more suitable for practical application scenes. A sliding window estimation model is proposed to estimate the junction temperature of the IGBT module. Compared with the point‐to‐point estimation method, the sliding window estimation method can capture the influence of historical operation data better and has a higher capability of time series data estimation. The IGBT junction temperature estimation of sliding windows is realized by the LSTM neural network, which is more suitable for time series estimation in real industrial scenarios. The experimental results show that the estimation accuracy of the sliding window estimation method is better than that of the point‐to‐point estimation method, and the accuracy of the sliding window estimation method based on LSTM is better than that of the sliding window estimation method based on other machine learning models. It proves that the proposed method can better capture the dynamic process of the system and has higher estimation accuracy.

Estimation of Non-Photosynthetic Vegetation Cover Using the NDVI–DFI Model in a Typical Dry–Hot Valley, Southwest China

Non-photosynthetic vegetation (NPV) significantly impacts ecosystem degradation, drought, and wildfire risk due to its flammable and persistent litter. Yet, the accurate estimation of NPV in heterogeneous landscapes, such as dry–hot valleys, has been limited. This study utilized multi-source time-series remote sensing data from Sentinel-2 and GF-2, along with field surveys, to develop an NDVI-DFI ternary linear mixed model for quantifying NPV coverage (fNPV) in a typical dry–hot valley region in 2023. The results indicated the following: (1) The NDVI-DFI ternary linear mixed model effectively estimates photosynthetic vegetation coverage (fPV) and fNPV, aligning well with the conceptual framework and meeting key assumptions, demonstrating its applicability and reliability. (2) The RGB color composite image derived using the minimum inclusion endmember feature method (MVE) exhibited darker tones, suggesting that MVE tends to overestimate the vegetation fraction when distinguishing vegetation types from bare soil. On the other hand, the pure pixel index (PPI) method showed higher accuracy in estimation due to its higher spectral purity and better recognition of endmembers, making it more suitable for studying dry–hot valley areas. (3) Estimates based on the NDVI-DFI ternary linear mixed model revealed significant seasonal shifts between PV and NPV, especially in valleys and lowlands. From the rainy to the dry season, the proportion of NPV increased from 23.37% to 35.52%, covering an additional 502.96 km². In summary, these findings underscore the substantial seasonal variations in fPV and fNPV, particularly in low-altitude regions along the valley, highlighting the dynamic nature of vegetation in dry–hot environments.

An IMM Algorithm for Tracking Maneuvering Space Target under Low-Rate Data

Under low data rates, the observational data is sparse, and the noise have a significant impact. Under such circumstance, filtering algorithms tend to rely more heavily on predictions. However, the absence of maneuver information usually results in significant model errors, ultimately affecting the accuracy of states estimation and potentially leading to filter divergence. To address this issue, we have constructed a model set specific to satellite maneuvers and proposed an Improved Interacting Multiple Model (IMM) algorithm based on a designed maneuver detection model and estimation model. We have developed a maneuver estimation model to estimate the maneuvers, and to perform pruning on the maneuver models before estimation. The algorithm dynamically controls the introduction of maneuver models via the detection model and estimation model. When a maneuver is detected, the algorithm introduces the maneuver model set for fusion estimation; otherwise, it degrades into a Kalman filter based on a non-maneuver model. Simulation results indicate that the proposed algorithm effectively tracks targets in low data rate scenarios while maintaining high estimation accuracy.

A robust distributed interaction multiple model filter for jump Markov systems with heavy-tailed measurement noise

When heavy-tailed measurement noises (HTMNs) are introduced by outliers, the estimation accuracy of traditional distributed interaction multiple model (IMM) algorithms will be severely reduced in some target tracking scenarios. To solve the problem, a new distributed robust filter algorithm using cubature information filtering and weighted average consensus approach in the framework of IMM filtering is proposed. Firstly, the HTMNs are modeled as Student’s t-distributions, and as conjugate prior distributions for the degree of freedom parameters and the scale matrices, the Gamma distributions and the inverse-Wishart distributions have been chosen. Secondly, the system state vectors, noise corresponding parameters, and model probabilities are inferred utilizing the variational Bayesian method. What’s more, in order to increase the stability of sensor networks and improve the estimation accuracy of the proposed filtering algorithm, the weighted average consensus approach is used to update information pairs and model probabilities. Finally, a target tracking simulation experiment is used to verify that the proposed algorithm has higher estimation accuracy and robustness than existing advanced filtering algorithms in dealing with HTMN.