Merging Strategy Research Articles

A new clearing method for cascade hydropower spot market

The natural hydraulic connections between cascade hydropower may make hydraulic and electric connections mismatch after the market clears. Aiming at relieving the contradiction between water surplus and water shortage after market clearing, this paper proposes a new market clearing method considering the coupling relationship between water and electricity. And the method is verified by the simulation of a virtual spot market consisting of eight stations in Sichuan Province, China. The main contributions of this paper lay on three aspects: firstly, a new index called water-electricity coupling degree (WCD) and its calculation method are proposed to measure the overall situation of water shortage and water abandonment in the clearing results. Secondly, a multi-objective clearing model for cascade hydropower stations is established with the goal of minimizing power purchase cost and maximizing WCD. Thirdly, a solution method combined with the characteristics of the clearing model is developed. The case results demonstrate that the built model increases the WCD value by 1.57 % and reduces the insufficient output and surplus water by 45.18 % without increasing the power purchase cost of the power grid; The proposed solution strategy lowers the complexity of the model and improves the solution efficiency, where the search space reduction and time period merging strategies can reduce the calculation time by about 3 % and 8 % respectively. The new clearing method provides a new idea for solving the uncoupling problem of water-electricity-price in the cascade hydropower spot market.

A New Strategy for Merging the Environmental and Commerce Challenges of the Caspian

Recent geopolitical events have thrust the Caspian region into a time of unparalleled opportunity. Specifically, the Middle Corridor (“MC”) has been presented as a potential savior for trans-Eurasian commerce. At the same time, its success relies on a sea that is in an ecologically precarious state. Saving the Caspian for all future uses, including trade, will require unprecedented engagement and coordination amongst all its bordering states. This paper proposes a strategy for using the newfound focus on the MC to highlight its inherent connection to the Sea’s ecological precarity – thereby linking strategies of developing Trans-Caspian commerce with the efforts to preserve the Sea’s ecology against both existing and emerging threats. The paper begins by summarizing recent geopolitical developments connected to the MC that have accentuated the Caspian’s global importance for commerce. It then explores a related challenge – the simultaneous efforts of several littoral states to pursue desalination of Caspian seawater at a time when the Sea’s level is already declining from pre-existing causes. While these are two separate issues, we argue that they are, in fact, interrelated and that the coordination and governance challenges for improving commerce are very similar to those necessary for regulating desalination. Next, we analyze the challenge of regulating desalination in the context of water geopolitics and the unique features of the current Caspian Treaty. Finally, we review the language of the existing Caspian Treaty to identify areas where enhancements are necessary to better reflect both emerging challenges.

Click processes orthogonal to CuAAC and SuFEx forge selectively modifiable fluorescent linkers.

The appeal of catalytic click chemistry is largely due to the copper-catalysed azide-alkyne cycloaddition (CuAAC) process, which is orthogonal to the more recently introduced sulfur-fluoride exchange (SuFEx). However, the triazole rings generated by CuAAC are not readily modifiable, and SuFEx connectors cannot be selectively functionalized, attributes that would be attractive in a click process. Here we introduce bisphosphine-copper-catalysed phenoxydiazaborinine formation (CuPDF), a link-and-in situ modify strategy for merging a nitrile, an allene, a diborane and a hydrazine. We also present copper- and palladium-catalysed quinoline formation (Cu/PdQNF), which is applicable in aqueous media, involving an aniline as the modifier. CuPDF and Cu/PdQNF are easy to perform and deliver robust, alterable and tunable fluorescent hubs. CuPDF and Cu/PdQNF are orthogonal to SuFEx and CuAAC, despite the latter and CuPDF also being catalysed by an organocopper species. These advantages were applied to protecting group-free syntheses of sequence-defined branched oligomers, a chemoselectively amendable polymer, three drug conjugates and a two-drug conjugate.

Slicing-guided Skeleton Extraction Method for 3D Point Clouds of Human Body

<p>In the paper, a slicing-guided method is introduced to extract the curve skeleton from the point cloud body model. Firstly, the dominant eigenvector of body model as slicing direction is chosen adaptively, and the input body model is sliced accordingly. each slice is projected and classified into different regions, and the centroid of each region can be considered as initial skeleton point. Then, those skeleton points are removed outside models, and initial skeleton lines are generated by connecting points based on different region of body model. Finally, the two-step post-processing approach is proposed to improve the initial skeleton results for accurate topological analysis. With the branch point merging strategy, the initial skeleton of the model is optimized. Furthermore, the skeleton lines by interpolation optimization are refined and smoothed. Compared with similar skeleton extraction algorithms, the method proposed in the paper has relatively strong robustness and effectiveness, and can be applied to human body model in point cloud data.</p> <p> </p>

Density peaks algorithm based on information entropy and merging strategy for power load curve clustering

Density peaks algorithm based on information entropy and merging strategy for power load curve clustering

A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies' Intercomparison.

Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step 1: Translation and merging of the different datasets by employing InChIKeys for data integration, encompassing the translation of metabolite names (if needed). Followed by Step 2: Attributes' retrieval from the InChIkey, including descriptors of name (title name from PubChem and RefMet name from Metabolomics Workbench), and chemical properties (molecular weight and molecular formula), both systematic (InChI, InChIKey, SMILES) and non-systematic identifiers (PubChem, CheBI, HMDB, KEGG, LipidMaps, DrugBank, Bin ID and CAS number), and their ontology. Finally, a meticulous three-step curation process is used to rectify disparities for conjugated base/acid compounds (optional step), missing attributes, and synonym checking (duplicated information). The MMS procedure is exemplified through a case study of urinary asthma metabolites, where MMS facilitated the identification of significant pathways hidden when no dataset merging strategy was followed. This study highlights the need for standardized and unified metabolite datasets to enhance the reproducibility and comparability of metabolomics studies.

A multimodal prediction model for suicidal attempter in major depressive disorder.

Suicidal attempts in patients with major depressive disorder (MDD) have become an important challenge in global mental health affairs. To correctly distinguish MDD patients with and without suicidal attempts, a multimodal prediction model was developed in this study using multimodality data, including demographic, depressive symptoms, and brain structural imaging data. This model will be very helpful in the early intervention of MDD patients with suicidal attempts. Two feature selection methods, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms, were merged for feature selection in 208 MDD patients. SVM was then used as a classification model to distinguish MDD patients with suicidal attempts or not. The multimodal predictive model was found to correctly distinguish MDD patients with and without suicidal attempts using integrated features derived from SVM-RFE and RF, with a balanced accuracy of 77.78%, sensitivity of 83.33%, specificity of 70.37%, positive predictive value of 78.95%, and negative predictive value of 76.00%. The strategy of merging the features from two selection methods outperformed traditional methods in the prediction of suicidal attempts in MDD patients, with hippocampal volume, cerebellar vermis volume, and supracalcarine volume being the top three features in the prediction model. This study not only developed a new multimodal prediction model but also found three important brain structural phenotypes for the prediction of suicidal attempters in MDD patients. This prediction model is a powerful tool for early intervention in MDD patients, which offers neuroimaging biomarker targets for treatment in MDD patients with suicidal attempts.

Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation

AbstractRobot visual navigation is a relevant research topic. Current deep navigation models conveniently learn the navigation policies in simulation, given the large amount of experience they need to collect. Unfortunately, the resulting models show a limited generalization ability when deployed in the real world. In this work we explore solutions to facilitate the development of visual navigation policies trained in simulation that can be successfully transferred in the real world. We first propose an efficient evaluation tool to reproduce realistic navigation episodes in simulation. We then investigate a variety of deep fusion architectures to combine a set of mid-level representations, with the aim of finding the best merge strategy that maximize the real world performances. Our experiments, performed both in simulation and on a robotic platform, show the effectiveness of the considered mid-level representations-based models and confirm the reliability of the evaluation tool. The 3D models of the environment and the code of the validation tool are publicly available at the following link: https://iplab.dmi.unict.it/EmbodiedVN/.

Business model innovation: Integrative review, framework, and agenda for future innovation management research

AbstractThe business model innovation (BMI) concept has become a well‐established phenomenon of current academic research. While Foss and Saebi's (Journal of Management, 2017, 43, 200–227) seminal literature review on BMI revealed 349 articles on BMI published between 1972 and 2015, an additional number of 1727 articles on the topic have been published since 2016. In contrast to this overall interest in the BMI phenomenon, innovation‐focused journals include only a limited number of publications on BMI. Further, besides the valuable insights and fruitful research directions of extant literature reviews, they tend also to overlook the linkages between traditional innovation management and the majority of BMI research. Given this underrepresentation of BMI research in the innovation management literature, we conduct an integrative literature review to bring the disconnected literature closer together and offer directions for future research. Our literature review applies the review strategy of blending and merging the literature across domains. First, we blend the knowledge base of the BMI domain by applying the Product Development and Management Association (PDMA) Body of Knowledge categories. Second, we merge the literature across domains by developing an integrative framework. As recent BMI literature increasingly converged to two lines of research, our framework differentiates between the relatedness of the BMI and BMI openness. Thereby, we offer new avenues for future research that can enrich the dialogue on BMI research in the innovation management community. These results demonstrate how the BMI domain covers aspects that fit, contribute to, and extend classical innovation‐focused research, how both research domains can be merged to enrich each other, and how future research can foster the dialogue across disconnected domains.

Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.

Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with reproducible prediction performance on independent datasets. We investigated the best approaches to integrate different studies of the same type of omics data under a variety of different heterogeneities. We developed a comprehensive workflow to simulate a variety of different types of heterogeneity and evaluate the performances of different integration methods together with batch normalization by using ComBat. We also demonstrated the results through realistic applications on six colorectal cancer (CRC) metagenomic studies and six tuberculosis (TB) gene expression studies, respectively. We showed that heterogeneity in different genomic studies can markedly negatively impact the machine learning classifier's reproducibility. ComBat normalization improved the prediction performance of machine learning classifier when heterogeneous populations are present, and could successfully remove batch effects within the same population. We also showed that the machine learning classifier's prediction accuracy can be markedly decreased as the underlying disease model became more different in training and test populations. Comparing different merging and integration methods, we found that merging and integration methods can outperform each other in different scenarios. In the realistic applications, we observed that the prediction accuracy improved when applying ComBat normalization with merging or integration methods in both CRC and TB studies. We illustrated that batch normalization is essential for mitigating both population differences of different studies and batch effects. We also showed that both merging strategy and integration methods can achieve good performances when combined with batch normalization. In addition, we explored the potential of boosting phenotype prediction performance by rank aggregation methods and showed that rank aggregation methods had similar performance as other ensemble learning approaches.

Cooperative Merging Strategy in Mixed Traffic Based on Optimal Final-State Phase Diagram With Flexible Highway Merging Points

The cooperation between connected and automated vehicles (CAVs) has emerged as a promising way to improve traffic efficiency and safety for ramp merging on highways. Existing research mostly focused on the collaboration of individual CAVs, while the cooperative merging strategy in mixed traffic considering vehicle platoons has been less explored. To address the above problem, this study aims to build connections between sequence scheduling and motion planning in mixed traffic, where individual CAVs, CAVs platoons, and mixed platoons coexist. First, optimal control strategies are presented for vehicles with flexible merging points based on Pontryagin’s minimum principle (PMP), and the final vehicle states in variable conditions are summarized in a phase diagram. Subsequently, the optimal final-state phase diagram is introduced into the passing sequence tree search process, which is designed for different vehicle groups in mixed traffic. Heuristic pruning rules are added to the depth-first search strategy to facilitate finding the optimal solution. Finally, an event-triggered receding horizon optimization algorithm is developed for continuous implementation. The numerical simulations are conducted in multiple traffic volumes, and the simulation results reveal that our proposed algorithm significantly improves the overall traffic efficiency and reduces vehicle-passing delays compared with the traditional FIFO-based cooperation method.

Full stage networks with auxiliary focal loss and multi-attention module for submarine garbage object detection

Submarine garbage is constantly destroying the marine ecological environment and polluting the ocean. It is critical to use detection methods to quickly locate and identify submarine garbage. The background of submarine garbage images is much more complex than that of natural scene images, with object deformation and missing contours putting higher demands on the detection network. To solve the problem of low accuracy under complex backgrounds, full stage networks with auxiliary focal loss and multi-attention module are proposed for submarine garbage object detection based on YOLO. To maximize the gradient combination, a hierarchical fusion feature mechanism and a segmentation and merging strategy are used in this paper to optimize the difference in gradient combination to obtain full-stage features. Then the criss-cross attention module is used to precisely extract multi-scale features of small object dense regions while removing noise information from complex backgrounds. Finally, the auxiliary focal loss function addresses the issue of unbalanced positive and negative samples, focusing on the learning of difficult samples while improving overall detection precision. Based on comparative experiments and ablation experiments, the FSA networks achieved state-of-the-art performance, and is applicable to the real-time object detection of submarine garbage in complex backgrounds.

Pioneering first-in-class FAAH-HDAC inhibitors as potential multitarget neuroprotective agents.

Aiming to simultaneously modulate the endocannabinoid system (ECS) functions and the epigenetic machinery, we selected the fatty acid amide hydrolase (FAAH) and histone deacetylase (HDAC) enzymes as desired targets to develop potential neuroprotective multitarget-directed ligands (MTDLs), expecting to achieve an additive or synergistic therapeutic effect in oxidative stress-related conditions. We herein report the design, synthesis, and biological evaluation of the first-in-class FAAH-HDAC multitarget inhibitors. A pharmacophore merging strategy was applied, yielding 1-phenylpyrrole-based compounds 4a-j. The best-performing compounds (4c, 4f, and 4h) were tested for their neuroprotective properties in oxidative stress models, employing 1321N1 human astrocytoma cells and SHSY5 human neuronal cells. In our preliminary studies, compound 4h stood out, showing a balanced nanomolar inhibitory activity against the selected targets and outperforming the standard antioxidant N-acetylcysteine in vitro. Together with 4f, 4h was also able to protect 1321N1 cells from tert-butyl hydroperoxide or glutamate insult. Our study may provide the basis for the development of novel MTDLs targeting the ECS and epigenetic enzymes.

Developing Novel Coumarin-Containing Azoles Antifungal Agents by the Scaffold Merging Strategy for Treating Azole-Resistant Candidiasis.

The extensive use of antifungal drugs has resulted in severe drug resistance, making clinical treatment of fungal infections more difficult. Biofilm inhibitors can overcome drug resistance by inhibiting fungal biofilm formation. In this study, some coumarins with antibiofilm activity were merged into CYP51 inhibitors to produce novel molecules possessing potent antiresistance activity. As expected, most compounds exhibited excellent in vitro antifungal activity against pathogenic fungi, especially fluconazole-resistant candidiasis. Then, their mechanism was confirmed by sterol composition analysis and morphological observation. Biofilm inhibition and down-regulation of resistance-related genes were employed to confirm the compounds' antiresistance mechanisms. Significantly, compound A32 demonstrated fungicidal activity against fluconazole-resistant strain 904. Most importantly, compound A32 showed potent in vivo antifungal activity against pathogenic fungi and fluconazole-resistant strains. Preliminary pharmacokinetic and toxicity tests demonstrated that the compounds possessed favorable druggability. Taken together, compound A32 represents a promising lead to develop novel antifungal agents for treating azole-resistant candidiasis.

A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM

Satellite products have mediocre performance in precipitation estimation, while rain gauges are incapable of describing continuous spatial precipitation distributions. To obtain spatially continuous and accurate precipitation data, this paper proposes a two-step scheme incorporating environmental variables, satellite precipitation estimations, and rain gauge observations for the calibration of satellite precipitation data. First, the GPM data are downscaled from 0.1° to 0.01° based on the seasonal RF models to minimize the spatial differences between the satellite estimations and the rain gauge observations. Secondly, the fusion model combining ConvLSTM and CBAM explores the spatiotemporal correlation of downscaled satellite precipitation data with environmental co-variables and ground-based observations to correct GPM precipitation. The integrated scheme (CBAM-ConvLSTM) is applied to acquire monthly precipitation at a spatial resolution of 0.01° over Hanjiang River Basin from 2014 to 2018. Comparative analyses of model-based satellite products with in situ observations show that model-based precipitation products have a high-resolution spatial distribution along with high accuracy, which combines the advantages of in situ observations and satellite products. Compared to the original GPM product, the evaluation metric values of the merged precipitation products all improved: the RMSE decreased by 31% while the CC increased from 0.55 to 0.69, the bias decreased from about 25% to less than 1.8%, and the MAE decreased by 27.8% while the KGE increased from 0.28 to 0.52. This two-step scheme provides an effective way to derive a high-resolution and accurate monthly precipitation product for humid regions.

SPPUSM: An MS/MS spectra merging strategy for improved low-input and single-cell proteome identification

Single and rare cell analysis provides unique insights into the investigation of biological processes and disease progress by resolving the cellular heterogeneity that is masked by bulk measurements. Although many efforts have been made, the techniques used to measure the proteome in trace amounts of samples or in single cells still lag behind those for DNA and RNA due to the inherent non-amplifiable nature of proteins and the sensitivity limitation of current mass spectrometry. Here, we report an MS/MS spectra merging strategy termed SPPUSM (same precursor-produced unidentified spectra merging) for improved low-input and single-cell proteome data analysis. In this method, all the unidentified MS/MS spectra from multiple test files are first extracted. Then, the corresponding MS/MS spectra produced by the same precursor ion from different files are matched according to their precursor mass and retention time (RT) and are merged into one new spectrum. The newly merged spectra with more fragment ions are next searched against the database to increase the MS/MS spectra identification and proteome coverage. Further improvement can be achieved by increasing the number of test files and spectra to be merged. Up to 18.2% improvement in protein identification was achieved for 1 ng HeLa peptides by SPPUSM. Reliability evaluation by the “entrapment database” strategy using merged spectra from human and E. coli revealed a marginal error rate for the proposed method. For application in single cell proteome (SCP) study, identification enhancement of 28%–61% was achieved for proteins for different SCP data. Furthermore, a lower abundance was found for the SPPUSM-identified peptides, indicating its potential for more sensitive low sample input and SCP studies.

Nash double Q-based multi-agent deep reinforcement learning for interactive merging strategy in mixed traffic

The interaction between ramp and mainline vehicles plays a crucial role in merging areas, especially in the mixed-traffic environment. The driving behaviours of human drivers are uncertain and diverse, and the uncertainty makes it more complex for connected automated vehicles (CAV) to plan trajectories and merge into the mainline. To overcome this problem, a interactive merging strategy based on multi-agent deep reinforcement learning (MADRL) is designed, enabling the ramp vehicle (CAV) to consider the dynamic reaction of mainline vehicles. There are two agents in our interactive strategy, one of which is to predict and analyse the behaviour of mainline vehicles (human-driven vehicles, HDV, or non-connected vehicles). The other is created for exploring optimal merging actions of ramp vehicles. Firstly, game theory is used to model the competitive behaviours between ramp and mainline vehicles, and the Nash equilibrium of joint actions guides the ramp vehicle to learn best response to the mainline vehicle. Secondly, the Nash double Q algorithm is developed to ensure the outputs of Q networks are trained to efficiently converge to the Nash equilibrium point. The trained Q networks are then used for online control. Finally, our strategy is compared with single RL and existing MADRL algorithms in real on-ramp scenarios. Simulations show our strategy to be successful in coordinating both vehicles via analysis of human drivers, resulting in improved driving performance in terms of global safety, efficiency, and comfort.

Dynamic event‐triggered control for delayed switched neural networks: A merging signal scheme

AbstractThis paper investigates the asynchronous control of delayed switched neural networks via a dynamic event‐triggering mechanism (DETM) and a merging signal scheme. Firstly, an improved DETM is proposed through adding an exponential decaying term into the triggering condition, which is proved to have a larger lower bound by comparison with the static event‐triggering mechanism (SETM) and the existing DETM. Next, a merging signal is constructed by introducing a time‐varying delay to combine the system switching signal with the controller switching signal, which is convenient to deal with the synchronous and asynchronous switching in a unified framework. Then, the closed‐loop system which integrates the proposed DETM with the merging signal is formulated. Moreover, by constructing multiple time‐dependent Lyapunov functionals, sufficient conditions which ensure the asymptotical stability of the resulting closed‐loop system are derived, and a design algorithm for the feedback controller is designed consequently. Finally, the validity and superiority of the proposed results are demonstrated by numerical examples.

Leveraging Landsat-8/-9 underfly observations to evaluate consistency in reflectance products over aquatic environments

With an identical design and build, the Operational Land Imager-2 (OLI2) aboard Landsat-9 (L9) complements OLI observations by reducing the global revisit rate of Landsat to 8 days. This study takes advantage of near-coincident OLI2 and OLI observations obtained on 11–17 November 2021 to assess the relative quality of the standard United States Geological Survey (USGS) top-of-atmosphere (TOA) reflectance (ρt) and atmospherically corrected reflectance (aquatic reflectance; ρwAR) products over bodies of water. The TOA assessment was carried out for all the visible bands, including the panchromatic band, as well as the near-infrared (NIR) and shortwave infrared (SWIR) bands, whereas ρwAR products were analyzed in the 443, 482, 561, and 655 nm bands. The overlapping areas of OLI-OLI2 ρw product pairs were further analyzed for the rigor in corrections for the viewing geometry implemented in selected atmospheric correction (AC) processors, including SeaDAS, ACOLITE, and POLYMER, with products denoted as ρwAR, ρwac, and ρwpol, respectively. Overall, we found the OLI2-OLI ρt products to be consistent within 0.4% in the visible-near-infrared (VNIR) bands except in the green band (561 nm), where OLI2 records ∼0.8% larger values than OLI. The two SWIR bands (1610 and 2200 nm) were also found to agree within ∼2.2% and 2.1%, respectively, with OLI2 being lower in magnitude. The median differences in the standard ρw (ρwAR) were estimated to be ∼2.4%, 1.5%, 2.3%, and 2.5% in the 443, 482, 561, and 655 nm bands, respectively, which are well within the accepted differences in cross-mission data merging schemes. Further, we show that OLI2's signal-to-noise ratio (SNR) is 7–30% higher than that of OLI in all the bands, likely due to its 14-bit digitization rate, as compared to OLI's 12-bit digitization rate. Our self-consistency assessment of the AC processors for handling the differences in the view zenith angles (ΔVZA) and relative azimuth angles (ΔRAA) of OLI2 and OLI observations suggests that, overall, the processors account for the Sun-sensor geometry differently at different spectral bands. These differences (inconsistencies) amount to average median absolute percentage differences (MAPD) in ρw from 0.3 to 5.3% (e.g., ∆ρwAR443nmΔVZAΔRAA=ρwAR,OLI443+6.310−ρwAR,OLI2443+3.9−13<0.5%).More specifically, we found that SeaDAS provides the most optimal corrections for the angular variability as a function of VZA, ρwac are most consistent for different ranges of RAAs, and POLYMER performs best in the 443 nm band. We surmise that future (and other existing) AC methods should be tested with Landsat-8/-9 underfly imagery to quantify their performance for tackling the variability in imaging geometry and minimize associated uncertainties. With several missions planned for launch by the end of this decade, it is further emphasized that post-launch tandem maneuvers are essential to creating harmonized multi-mission data products and, therefore, similar operations should be considered and extended to ensure a broad range of environmental conditions are captured for comprehensive cross-mission analyses.

Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets

AbstractThis paper addresses the integrated tracking and identification problem of a manoeuvring reentry target that performs intentional lateral manoeuvres to disrupt ground radars. Unlike previous approaches, prior knowledge of the lift‐induced drag is incorporated into a new manoeuvring model to describe the reentry target dynamics more explicitly. This model can account for the constraint between lift and drag, which is beneficial in ensuring the reliability of target state estimation. Noticing that the lift‐induced drag is an inherent characteristics of a reentry target that distinguishes the target's identity from others belonging to the same class, the integrated target tracking and identification problem is formulated within the framework of the multiple hypothesis testing about a set of manoeuvring models constructed by different prior knowledge. The proposed approach enables the authors to derive the optimal solution to the given problem in a mathematically rigorous manner. To cope with the real‐time implementation issue, a hypothesis merging strategy is also devised in view of maintaining the target identification performance. Simulation results demonstrate that the proposed scheme provides superior performance and reliability both in target tracking and identification compared to the existing method, despite imperfectness of prior knowledge.