# Advanced Intelligent English Translation Based on Multisensor Data Fusion Optimization

Authors: Cui Wang
Journal: Journal of Sensors
 (2022)

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## Abstract

English translation activity course is of great significance to cultivate students’ English translation level. In the context of multisensor data fusion, how to effectively carry out English translation activity course in colleges and universities has become an important topic. The educational value and intellectual property of advanced Intelligent English translation activity course are analyzed. From multisensor data fusion and the improvement of translation, translators psychological changes of the boot and prominent features, English translation activity and translation, the generality of the multisensor data fusion of multisensor data fusion personalization features this four aspects, which is under the background of the sensor data fusion of the practice for college English translation activity. Firstly, the theory of data fusion estimation is elaborated, and various data fusion structures in multisensor systems are summarized. Then, the data fusion estimation model based on Kalman filter is established, and the Kalman filtering algorithms of centralized, sequential, parallel, and joint structures are given, respectively. Simulation experiments are carried out on the algorithms. Experimental results show that the estimation accuracy of the system can be improved by multisensor data fusion. Then a rule-based lexical analyzer is designed. Combined with the system model, a rule-based lexical analyzer and a comprehensive dictionary, the linguistic knowledge source throughout the whole machine translation process, are researched and designed. A hashing algorithm for lexical retrieval is designed, and various rules and data structures related to the lexical analyzer are described in formal language. The analysis algorithms of morphological preprocessing, morphological analysis, unincluded word processing, phrase analysis, and part of speech tagging are introduced.


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## Body

## 1. Introduction

English translation activity course is a kind of liberal teaching course whose teaching value lies in enhancing students’ initiative and learning ability. For English translation skills, free dialogue and free communication will be arranged in the activity class, so that students can exchange some basic content with each other by using the English theoretical knowledge and communication skills they have learned, so as to experience the environmental value of English translation communication and experience the communication atmosphere [1].Transform-based is a rule-based approach, which takes source language analysis as the main body and logically divides into three stages: analysis, transformation, and generation. It uses comprehensive dictionaries and various rule libraries as the knowledge source. The instance-based pattern is based on the idea of instance-based method. We extend the instance library based on the instance method to the instance pattern library, that is, we abstract the structurally similar instances in the library into sentence patterns with arguments and store them in the library. In translation, the input sentence is matched with the source language part of the instance in the library as well as the source language part of the pattern in the library. The implementation of the system is based on the transformation method, and the instance pattern method is embedded in the syntactic analysis stage, while the output is based on the instance pattern method translation results. When there are no instances and sentence patterns similar to the input source language sentences in the instance pattern library, the system outputs the translation results based on the transformation generation method.The different fields of multisensor data fusion translation are studied by using bilingual conversion rules. Based on the analysis of bilingual conversion rules, the cognitive mechanism of translation comprehension and expression and the cognitive processing system of translation are studied. The free arrangement of specific English translation activities is proposed, and the latest teaching plan of English translation activities is mastered. The multisensor data fusion translation mode itself forms a new interaction mode through bilingual communication, and the specific problems and ideas are put forward through multisensor data fusion translation. The imagination of multisensor data fusion translation is essentially an image formed in the brain of the original text. To analyze the translation, elegant translation and style of the translator as the expression of the translator’s aesthetic psychology, and to study the creative translation, elegant translation and translator’s style need to study the rules of different forms of bilingual conversion. Based on the analysis of the translator’s unique bilingual conversion, this paper studies the influence of national cultural psychology on the translator.


## 2. Related Work

Multisensor data fusion is put in different position; more similar or not similar sensor provided by the local environment of integrated incomplete data, eliminate redundancy and possible conflict between sensor data, to complement each other and reduce the uncertainty, in order to form a relatively complete and consistent perception description of the system environment [2]. In order to improve the intelligent system, decision-making, planning, reflecting the rapidity and correctness, and reducing the risk of decision-making, the definition of data fusion is given as follows: Data fusion is a multilevel and multifaceted process, which detects, combines, correlates, estimates, and combines multisource data to achieve accurate state estimation and identity estimation as well as complete and timely situation assessment and threat estimation. In addition, there are many researches on fusion algorithms at present, but there are few researches on performance testing and evaluation of data fusion algorithms, and no universally recognized evaluation standard has been formed [3]. Research on multisensor data fusion methods has been mature, but how to evaluate the performance of the fusion system composed of these various algorithms is still in the exploratory stage, and there are few relevant research results. Make the blindness of the design with a certain information fusion system, in order for the multisensor data fusion technology to get better development, military command automation system more mature, developed for use with data fusion algorithm to develop user an open, high-performance, and strong scalability, and to evaluate the fusion; algorithm and system is very necessary [4].The fusion algorithm is the core of the data fusion system, and the current mainstream fusion algorithm is mainly to study the fusion of similar information, but the multisensor fusion of heterogeneous information lacks effective fusion algorithm and fusion model. The study found that the heterogeneous information data fusion in the complex problem can be overcome with the help of modern statistics theory [5], such as rough set theory, the Bayes network and evidence theory, support vector machine (SVM), and the theory of random set theory of the development of technology for different sources of multisensor data fusion algorithm brings new thinking method and innovation space [6]. The research work of data fusion is still in its infancy. Compared with developed countries, the technology of information fusion is not perfect and mature, and there is still a lot of room for exploration in basic theoretical research and unified framework specification. However, with the development of neural network technology, sensor data fusion optimization, parallel computing, microelectronics technology, information communication, sensor technology, statistical theory, fuzzy mathematics, and other theoretical technologies, it is bound to greatly promote the development of multisensor data fusion technology [7]. Our country in the field of civil general technology and military high-tech sector also will certainly to large-scale application of multisensor data fusion technology, in this case is related to data fusion of the domestic researchers put forward higher requirements, including the formation of a unified definition of data fusion model, continue to develop and perfect the basic theory of multisensor data fusion and technical requirements. In existing data fusion algorithms, it is often necessary to set the fusion upper limit in the face of multiple information fusion. There is no objective calculation method for setting the upper limit, which is often based on subjective experience. Different upper limits selected will have different fusion results, affecting the accuracy and robustness of the fusion results [8]. And in the process of multiple information fusion, fuzzy logic reasoning can be directly in the process of reasoning to put the uncertainty of multisensor [9], so the observation information fusion with fuzzy set credibility objectively calculated this topic will be discussed and become one of the research question, is one aspect of this research paper. As an important branch of statistical theory, D-S evidence theory has been widely used in many fields such as multisensor data fusion, target recognition, and uncertain reasoning due to its practicality and operability in engineering applications and its ability to clearly represent “uncertain and unknown” data information. Dempster’s combination rule, as the core cornerstone of evidence theory, has the advantages of simple form and easy calculation [10, 11]. However, when the evidence is highly conflicting, the inference result obtained by applying Dempster’s combination rule is often contrary to the actual situation.So far, the theory instruction in the country with more in the college of foreign language teaching research is based on college English teaching of a non-English major; in English, especially in senior English, teaching research gains almost no attention, so it is necessary to explore to enrich teaching [12], and the practice prove that this kind of research is feasible. Speech/language intelligence is related to spoken or written language. Intelligent people of this type are good at reading, writing, telling stories, and memorizing words. They have a strong understanding of meaning and syntactic structure and have good memory and recall. They tend to learn knowledge through reading, reading, writing, discussion, or debate [13]. In high English teaching, every teaching is closely related to this intelligent development, that is, to effectively improve students’ comprehensive English language skills such as written expression, oral expression, and translation [14]. There are many teaching methods to improve written expression, including discourse style and genre analysis, abstract writing, structure analysis of complex sentences, contextual meaning analysis of words, use and appreciation of rhetoric devices, “foreground” stylistic expression effect analysis, etc. The comprehensive use of these methods has indirectly or directly improved students’ writing ability from different perspectives [15]. The teaching methods to improve oral expression include reading the text aloud, retelling the text, summarizing paragraphs or text themes, explaining difficult sentences, discussing problems, students’ interaction, and thinking exercises after class reading comprehension. The teaching methods to improve translation ability include translating difficult sentences and practicing translation exercises after class. Multisensor data fusion is related to logical reasoning, abstract generalization, and scientific research. This type of intelligent person usually has a high IQ in the traditional sense and has the ability to analyze, sort out, organize, critical thinking, and apply it in mathematics [16], which are the abilities proposed in the English teaching syllabus for high English. “The outline” clearly states that “to cultivate students’ ability of analyzing and appreciating famous texts, logical thinking, and independent thinking” [17]. Therefore, teachers should think and practice more in this aspect. In teaching related to this teaching method is very common for chapter structure and plot clues to the analysis of the macro language level [18], including using logic and reasoning and contextual meaning of words or sentences the pragmatic meaning between the micro level of linguistic analysis; these are units of study that need basic intelligence. In high English teaching, the improvement of critical thinking or other logical thinking ability is only related to a part of the teaching content, which requires teachers to consciously and actively think and discover this in teaching and creating opportunities for learners to develop this intelligence as much as possible [19, 20]. For example, the thinking process of discussing the relevance between the content of an article and its title and raising objections to the viewpoints or conclusions of an author, is the teaching practice of applying critical thinking.In fact, there are related contents in the existing literature on teaching research. Although the theoretical basis of the research is different, the essence of the research is similar to this research. Based on task-based teaching method, the title, structure, quoted materials, and classical degree of one of the articles are questioned in three task stages of preclass, class, and after-class, respectively, to analyze and improve learners’ critical thinking [21]. As for the research on the cultivation of logical thinking ability, it not only draws relevant conclusions based on case analysis, that is, the cultivation of comprehensive language ability is closely related to the improvement of logical thinking ability but also further discusses how to internalize the logical knowledge that students lack in normal teaching through high English teaching [22, 23].Fusion algorithm is the core of the whole fusion system, but at present, most of the research is on the fusion of similar data, while the modeling, collaboration, and fusion of heterogeneous information still need the support of algorithm. Introducing modern statistical inference methods into the research of information fusion algorithms will help to deal with complex problems. Bayes network and other intelligent computing techniques into the research of data fusion algorithm will bring new ideas and methods to the research of heterogeneous data fusion algorithm.


## 3. Intelligent Recognition of English Translation Based on Sensor Data Fusion Optimization

### 3.1. Sensor Data Fusion Translation Technology Based on English Translation Activity Class Translation

The training of applied talents in the English translation activity class based on sensor data fusion and optimization has brought new and innovative experience to students while improving students’ learning ability and school effectiveness through a series of teaching methods. The management method of application-oriented talent training in the sensor data fusion optimization of English translation activity class should also be innovated on the basis of this new teaching concept. The management method of application-oriented talent training in the sensor data fusion optimization of English translation activity class should be improved from the two roles of school and teacher, and the management method should be innovated. The idea of management innovation comes from a new understanding of the “theory+practice” integrated teaching method for talent cultivation in the sensor data fusion and optimization of English translation activity class. It takes the sensor data fusion and optimization of English translation activity class as the system. Construct new teaching materials for sensor data fusion and optimization of English translation activity class, create a new curriculum system, construct new curriculum management requirements, set up new courses, simulate basic cases, establish teaching planning, and establish a new management system for sensor data fusion and optimization of English translation activity class. Through this series of sensor data fusion to optimize the management of applied talents in English translation activities, the overall teaching ideas are constantly optimized; the teaching scheme is reformed; the teaching system is improved, and the teaching strategies are put forward. The basic principle of learning is shown in Figure1.Figure 1
Overall architecture of intelligent recognition of English translation based on multisensor data fusion optimization.From the perspective of the school, the experimental teaching or platform teaching of sensor data fusion and optimization of English translation activity class innovative talent training is a two-way communication process. On the one hand, teachers need to cooperate with students; On the other hand, students need to actively cooperate with teachers to form a systematic cycle and maintain the transmission of information. At the same time, it also opens up a new learning channel for students. From the point of view of specific communication and specific learning content, it is of great help for students to master an independent learning method for their future social practice and exploration of new ideas and methods. Students take advantage of the improvement of this method and the improvement of learning effect, constantly promote learning, to achieve their learning goals. The study of English translation activity courses will continue to be divided into different areas of specialization after a certain stage. Sensor data fusion to optimize English translation activity, such as economy and commercial sensors data fusion to optimize English translation activity, environmental protection, sensor data fusion to optimize English translation activity, administrative sensor data fusion to optimize English translation activity, litigation sensor data fusion to optimize English translation activity, and so on; these different dimensions and directions let the students have a new self-challenge and improvement space. Therefore, the school’s innovative sensor data fusion and optimization of English translation activity class application talent training strategy has improved the teaching environment, let students have their own direction and recognize the important value and significance of English translation activity class. The learning process of intelligent English translation rules is described in Table1:Table 1
Advanced intelligent English translation rules for multisensor data fusion.
Step 1: apply a simple basic tagging process (such as random tagging) to the training corpus to generate the working corpus with incorrect taggingStep 2: learn an optimal ruleStep 2.l: annotate each error in the working corpus and generate the candidate rule set according to its context characteristics and each rule templateStep 2.2: for each candidate rule, try to apply to the working corpus and calculate the net number of corrected error labels (the number of corrected error labels minus the number of newly generated error labels)Step 2.3: select the previous optimal rule (the maximum number of net corrected error marks) and insert the tail of the learned ruleStep 2.4: apply the currently learned optimal rules to the working corpus and update the working corpusStep 3: repeat step2 until the improvement in the performance of the working corpus is negligible, or a specified number of rules is reachedThe part of speech tagging process is described as follows:Step 1: use the same process as the training to label the slogan materials and generate the working corpus.Step 2: label = l as corpus by applying each rule in the rule set sequentially, and output the annotation resultsFor convenience, if the estimate a priori mean and a priori covariance (as well as the correlations between it and the measurement error of the v) is known, is that the linear minimum variance estimate fusion with full of prior information, the reason is that the linear minimum variance estimate the be estimator of the estimator is to use only a priori information is it in the first two moments. The following theorem gives the fusion rule of linear minimum variance estimation with complete prior information.(1)Q=Dx−KDyKtIf there is no prior information about the estimator, or the information is incomplete (for example, prior covariance is unknown or non-existent), or the estimator is non-random, then the linear minimum variance estimation formula is:(2)y=Ey+covx,ycovy¯According to the classical Kalman filtering algorithm and the optimal fusion estimation theorem, we can obtain the multisensor centralized Kalman filtering algorithm, as shown in Figure2.Figure 2
Structure of multisensor integrated data fusion algorithm.


### 3.2. Intelligent Recognition Model for English Translation

The purpose of English translation activity itself is to improve students’ learning motivation and improve their learning shortcomings by using learning motivation. The way to highlight the value of English translation activity class is to use sensor data fusion and optimization technology to combine the specific content of the combined needs to maintain a healthy and correct way of communication, which is an important education method. There is a kind of influence education combining with the new method of sensor data fusion to optimize AI (Artificial Intelligence) education, through the scene teaching combined with the students’ hands-on approach, deal with the specific content of communication, should handle to the normal social relationship reflected in the norms of behavior and life style of dealing with specific types of communication, and improve the practical value of the English translation. The combination of professional talent training and probation education can achieve better educational results. The innovation of education and teaching methods can effectively improve the details of specific teaching characteristics and teaching requirements, so as to better provide a benign interaction for students. By using probation education to improve students’ comprehensive quality of sensor data fusion and optimization of English translation activity class, lay a good ideological foundation for talent training, and shape the talent self-growth path of sensor data fusion and optimization of English translation activity class.According to the required functions of English translation intelligent recognition model, the overall model design is planned. Figure3 shows the model design process. The model can realize data collection, output and processing. The voice signal is collected through the data acquisition device, and then the English signal is input into the processing system through the audio input device to process the data signal. After processing, the results are output to the corresponding client and displayed. Users can view the automatic identification results of English translation through the display or client.Figure 3
Flow chart of advanced intelligent English translation model.The digital filter is used to realize the speech signal weighting processing and improve the stress detection system. The specific method is: the symbols F1 and F2 are used to represent the first and second resonance peaks of the vowel spectrum characteristics, respectively, which are related to the front and back of the tongue position and the height. Based on this, the classification system can be used to output position confidence after the neural network is used to distinguish the unstressed and the stressed, so as to measure the vowel intonation and select the best speech signal. In this paper, a high frequency signal lifting preweighting digital filter with SNR of 6 dB is used to process the speech signal in the frequency band higher than 800 Hz. The weighting coefficient is between 0.9 and 1.0, and the weighting signal can be calculated as follows:(3)yt=Txt+bAfter the frame processing is realized, the voice signal is windowing. In order to display the voice effect clearly, the rectangular window is selected, and the calculation formula is as follows:(4)wt=wn×ztThe continuous spectrum of aperiodic continuous time signal is calculated by Fourier transform, but the discrete sampling value of continuous signal is obtained in the actual control system, so the signal spectrum is calculated by discrete sampling value. Fast Fourier transform FFT is derived from the algorithm improvement of discrete Fourier transform based on the odd-even, imaginary and real properties of discrete Fourier transform. A finite length discrete speech signal is improved to obtain the following formula:(5)YT=∑xtFinally, different fields of sensor data fusion optimization translation are studied by using bilingual conversion rules. The research on the cognitive mechanisms of translation understanding and expression and the cognitive processing system of translation are carried out on the basis of analyzing the laws of bilingual conversion. Enhance the bilingual language learning motivation, characteristic as well as specific targets by syntax conversion rules, work schedules in accordance with the translator, and the characteristics of the target language, to translate the original and the choreography, deal with specific translation work, master the sensor data fusion optimization way of translation, puts forward specific English translation activity of free arrangement, and master the latest English translation activity class teaching plan. The sensor data fusion optimization translation mode itself forms a new way of interaction through bilingual communication and puts forward specific problems and ideas through the sensor data fusion optimization translation. The imagination of the sensor data fusion optimization translation is essentially the image formed in the brain of the original text. These images require the translator to express them in the target language. This inevitably involves the conversion law between bilinguals. The study of translators’ aesthetic psychological factors requires the analysis of translators’ translation, elegant translation and style as the expression of translators’ aesthetic psychological, while the study of creative translation, elegant translation and translator’s style requires the study of different forms of bilingual conversion laws. On the basis of analyzing the translator’s unique bilingual conversion, the author studies the influence of national cultural psychology on the translator.The personalization of sensor data fusion optimization translation refers to the customization of personal and interest of reading materials. In the context of mobile Internet, everyone wants to have their own unique and customized sensor data fusion optimization translation. Therefore, the current reading design can take into account the electronic upload and design of all categories of content, so that users can read and watch from their favorite parts and fully enjoy the fun of free learning and reading. Sensor data fusion optimization of wrong translation of personalized reader limit content and direction, gives the reader a comfortable environment; the use of information transmission platform can translate personal interest to optimize the sensor data fusion content automatically pushed to the readers’ mobile port, through the personalized video and audio content meet the needs of modern young people. In the era of new media, the diversified and fragmented functions of reading materials will be enlarged to satisfy users’ reading interests and improve their reading pleasure and self-identity. Based on the platform to spread, strengthen cultural communication, the traditional books to storage in the form of electronic books and information for the transformation direction, carry out personalized custom services; the spread of the diversification of new media meets the diverse needs of the active audience, under the background of the development of information interaction; in the perspective of development to promote the transformation of the traditional media, we should constantly optimize the quality of traditional reading materials, innovate the contents and channels of communication, and improve the existing deficiencies and defects by various forms of promotion and diversified ways of information interaction. Through websites, blogs, and other modern Internet information channels and platforms for sensor data fusion optimization, translation, promotion, and information interaction, to build modern communication channels with The Times.In the tagging method based on multisensor data fusion optimization, attributes are the features of the word to be tagged and its context. The part of speech features of words to be annotated are classified attributes, while other features of words to be annotated, such as vocabulary, facultative ambiguity type, whether there are uppercase letters, digits, hyphens, prefixes, and suffixes as well as the part of speech or facultative ambiguity type of context words, are nonclassified attributes. We also divided the words in the corpus into common words and unusual words according to the comprehensive dictionary, set feature templates for them, respectively, and established multisensor data fusion optimization T known and T Unknown. During labeling, different multisensor data fusion optimization was searched according to whether it was a common term, and corresponding labeling results were obtained, as shown in Table2.Table 2
Optimization of advanced Intelligent English translation process by multisensor data fusion.
Step 1: read the statement to be annotated and store it in internal formStep 2: cycle each word W in annotation sentencesStep 2.1: check the dictionary. If W is a common word, tree = Tse known; otherwise, tree = T-unknownStep 2.2: optimize the tree root node feature type according to multisensor data fusion, and extract the current word W to be annotated. The context eigenvalue value ofStep 2.3: if multisensor data fusion optimization tree root node has branches with value, then the tree binary value is the subtree of value, go to step2.2Step 2.4: the current multisensor data fusion optimization tree is a leaf node or there is no sub-tree with valueMark the current word W to be marked. Optimize the default part of speech of tree root node for current multisensor data fusionIf step 2.5 does not reach the end of the sentence, go to step 2. l to mark the next word, otherwise the sentence is marked.


## 3.1. Sensor Data Fusion Translation Technology Based on English Translation Activity Class Translation

The training of applied talents in the English translation activity class based on sensor data fusion and optimization has brought new and innovative experience to students while improving students’ learning ability and school effectiveness through a series of teaching methods. The management method of application-oriented talent training in the sensor data fusion optimization of English translation activity class should also be innovated on the basis of this new teaching concept. The management method of application-oriented talent training in the sensor data fusion optimization of English translation activity class should be improved from the two roles of school and teacher, and the management method should be innovated. The idea of management innovation comes from a new understanding of the “theory+practice” integrated teaching method for talent cultivation in the sensor data fusion and optimization of English translation activity class. It takes the sensor data fusion and optimization of English translation activity class as the system. Construct new teaching materials for sensor data fusion and optimization of English translation activity class, create a new curriculum system, construct new curriculum management requirements, set up new courses, simulate basic cases, establish teaching planning, and establish a new management system for sensor data fusion and optimization of English translation activity class. Through this series of sensor data fusion to optimize the management of applied talents in English translation activities, the overall teaching ideas are constantly optimized; the teaching scheme is reformed; the teaching system is improved, and the teaching strategies are put forward. The basic principle of learning is shown in Figure1.Figure 1
Overall architecture of intelligent recognition of English translation based on multisensor data fusion optimization.From the perspective of the school, the experimental teaching or platform teaching of sensor data fusion and optimization of English translation activity class innovative talent training is a two-way communication process. On the one hand, teachers need to cooperate with students; On the other hand, students need to actively cooperate with teachers to form a systematic cycle and maintain the transmission of information. At the same time, it also opens up a new learning channel for students. From the point of view of specific communication and specific learning content, it is of great help for students to master an independent learning method for their future social practice and exploration of new ideas and methods. Students take advantage of the improvement of this method and the improvement of learning effect, constantly promote learning, to achieve their learning goals. The study of English translation activity courses will continue to be divided into different areas of specialization after a certain stage. Sensor data fusion to optimize English translation activity, such as economy and commercial sensors data fusion to optimize English translation activity, environmental protection, sensor data fusion to optimize English translation activity, administrative sensor data fusion to optimize English translation activity, litigation sensor data fusion to optimize English translation activity, and so on; these different dimensions and directions let the students have a new self-challenge and improvement space. Therefore, the school’s innovative sensor data fusion and optimization of English translation activity class application talent training strategy has improved the teaching environment, let students have their own direction and recognize the important value and significance of English translation activity class. The learning process of intelligent English translation rules is described in Table1:Table 1
Advanced intelligent English translation rules for multisensor data fusion.
Step 1: apply a simple basic tagging process (such as random tagging) to the training corpus to generate the working corpus with incorrect taggingStep 2: learn an optimal ruleStep 2.l: annotate each error in the working corpus and generate the candidate rule set according to its context characteristics and each rule templateStep 2.2: for each candidate rule, try to apply to the working corpus and calculate the net number of corrected error labels (the number of corrected error labels minus the number of newly generated error labels)Step 2.3: select the previous optimal rule (the maximum number of net corrected error marks) and insert the tail of the learned ruleStep 2.4: apply the currently learned optimal rules to the working corpus and update the working corpusStep 3: repeat step2 until the improvement in the performance of the working corpus is negligible, or a specified number of rules is reachedThe part of speech tagging process is described as follows:Step 1: use the same process as the training to label the slogan materials and generate the working corpus.Step 2: label = l as corpus by applying each rule in the rule set sequentially, and output the annotation resultsFor convenience, if the estimate a priori mean and a priori covariance (as well as the correlations between it and the measurement error of the v) is known, is that the linear minimum variance estimate fusion with full of prior information, the reason is that the linear minimum variance estimate the be estimator of the estimator is to use only a priori information is it in the first two moments. The following theorem gives the fusion rule of linear minimum variance estimation with complete prior information.(1)Q=Dx−KDyKtIf there is no prior information about the estimator, or the information is incomplete (for example, prior covariance is unknown or non-existent), or the estimator is non-random, then the linear minimum variance estimation formula is:(2)y=Ey+covx,ycovy¯According to the classical Kalman filtering algorithm and the optimal fusion estimation theorem, we can obtain the multisensor centralized Kalman filtering algorithm, as shown in Figure2.Figure 2
Structure of multisensor integrated data fusion algorithm.


## 3.2. Intelligent Recognition Model for English Translation

The purpose of English translation activity itself is to improve students’ learning motivation and improve their learning shortcomings by using learning motivation. The way to highlight the value of English translation activity class is to use sensor data fusion and optimization technology to combine the specific content of the combined needs to maintain a healthy and correct way of communication, which is an important education method. There is a kind of influence education combining with the new method of sensor data fusion to optimize AI (Artificial Intelligence) education, through the scene teaching combined with the students’ hands-on approach, deal with the specific content of communication, should handle to the normal social relationship reflected in the norms of behavior and life style of dealing with specific types of communication, and improve the practical value of the English translation. The combination of professional talent training and probation education can achieve better educational results. The innovation of education and teaching methods can effectively improve the details of specific teaching characteristics and teaching requirements, so as to better provide a benign interaction for students. By using probation education to improve students’ comprehensive quality of sensor data fusion and optimization of English translation activity class, lay a good ideological foundation for talent training, and shape the talent self-growth path of sensor data fusion and optimization of English translation activity class.According to the required functions of English translation intelligent recognition model, the overall model design is planned. Figure3 shows the model design process. The model can realize data collection, output and processing. The voice signal is collected through the data acquisition device, and then the English signal is input into the processing system through the audio input device to process the data signal. After processing, the results are output to the corresponding client and displayed. Users can view the automatic identification results of English translation through the display or client.Figure 3
Flow chart of advanced intelligent English translation model.The digital filter is used to realize the speech signal weighting processing and improve the stress detection system. The specific method is: the symbols F1 and F2 are used to represent the first and second resonance peaks of the vowel spectrum characteristics, respectively, which are related to the front and back of the tongue position and the height. Based on this, the classification system can be used to output position confidence after the neural network is used to distinguish the unstressed and the stressed, so as to measure the vowel intonation and select the best speech signal. In this paper, a high frequency signal lifting preweighting digital filter with SNR of 6 dB is used to process the speech signal in the frequency band higher than 800 Hz. The weighting coefficient is between 0.9 and 1.0, and the weighting signal can be calculated as follows:(3)yt=Txt+bAfter the frame processing is realized, the voice signal is windowing. In order to display the voice effect clearly, the rectangular window is selected, and the calculation formula is as follows:(4)wt=wn×ztThe continuous spectrum of aperiodic continuous time signal is calculated by Fourier transform, but the discrete sampling value of continuous signal is obtained in the actual control system, so the signal spectrum is calculated by discrete sampling value. Fast Fourier transform FFT is derived from the algorithm improvement of discrete Fourier transform based on the odd-even, imaginary and real properties of discrete Fourier transform. A finite length discrete speech signal is improved to obtain the following formula:(5)YT=∑xtFinally, different fields of sensor data fusion optimization translation are studied by using bilingual conversion rules. The research on the cognitive mechanisms of translation understanding and expression and the cognitive processing system of translation are carried out on the basis of analyzing the laws of bilingual conversion. Enhance the bilingual language learning motivation, characteristic as well as specific targets by syntax conversion rules, work schedules in accordance with the translator, and the characteristics of the target language, to translate the original and the choreography, deal with specific translation work, master the sensor data fusion optimization way of translation, puts forward specific English translation activity of free arrangement, and master the latest English translation activity class teaching plan. The sensor data fusion optimization translation mode itself forms a new way of interaction through bilingual communication and puts forward specific problems and ideas through the sensor data fusion optimization translation. The imagination of the sensor data fusion optimization translation is essentially the image formed in the brain of the original text. These images require the translator to express them in the target language. This inevitably involves the conversion law between bilinguals. The study of translators’ aesthetic psychological factors requires the analysis of translators’ translation, elegant translation and style as the expression of translators’ aesthetic psychological, while the study of creative translation, elegant translation and translator’s style requires the study of different forms of bilingual conversion laws. On the basis of analyzing the translator’s unique bilingual conversion, the author studies the influence of national cultural psychology on the translator.The personalization of sensor data fusion optimization translation refers to the customization of personal and interest of reading materials. In the context of mobile Internet, everyone wants to have their own unique and customized sensor data fusion optimization translation. Therefore, the current reading design can take into account the electronic upload and design of all categories of content, so that users can read and watch from their favorite parts and fully enjoy the fun of free learning and reading. Sensor data fusion optimization of wrong translation of personalized reader limit content and direction, gives the reader a comfortable environment; the use of information transmission platform can translate personal interest to optimize the sensor data fusion content automatically pushed to the readers’ mobile port, through the personalized video and audio content meet the needs of modern young people. In the era of new media, the diversified and fragmented functions of reading materials will be enlarged to satisfy users’ reading interests and improve their reading pleasure and self-identity. Based on the platform to spread, strengthen cultural communication, the traditional books to storage in the form of electronic books and information for the transformation direction, carry out personalized custom services; the spread of the diversification of new media meets the diverse needs of the active audience, under the background of the development of information interaction; in the perspective of development to promote the transformation of the traditional media, we should constantly optimize the quality of traditional reading materials, innovate the contents and channels of communication, and improve the existing deficiencies and defects by various forms of promotion and diversified ways of information interaction. Through websites, blogs, and other modern Internet information channels and platforms for sensor data fusion optimization, translation, promotion, and information interaction, to build modern communication channels with The Times.In the tagging method based on multisensor data fusion optimization, attributes are the features of the word to be tagged and its context. The part of speech features of words to be annotated are classified attributes, while other features of words to be annotated, such as vocabulary, facultative ambiguity type, whether there are uppercase letters, digits, hyphens, prefixes, and suffixes as well as the part of speech or facultative ambiguity type of context words, are nonclassified attributes. We also divided the words in the corpus into common words and unusual words according to the comprehensive dictionary, set feature templates for them, respectively, and established multisensor data fusion optimization T known and T Unknown. During labeling, different multisensor data fusion optimization was searched according to whether it was a common term, and corresponding labeling results were obtained, as shown in Table2.Table 2
Optimization of advanced Intelligent English translation process by multisensor data fusion.
Step 1: read the statement to be annotated and store it in internal formStep 2: cycle each word W in annotation sentencesStep 2.1: check the dictionary. If W is a common word, tree = Tse known; otherwise, tree = T-unknownStep 2.2: optimize the tree root node feature type according to multisensor data fusion, and extract the current word W to be annotated. The context eigenvalue value ofStep 2.3: if multisensor data fusion optimization tree root node has branches with value, then the tree binary value is the subtree of value, go to step2.2Step 2.4: the current multisensor data fusion optimization tree is a leaf node or there is no sub-tree with valueMark the current word W to be marked. Optimize the default part of speech of tree root node for current multisensor data fusionIf step 2.5 does not reach the end of the sentence, go to step 2. l to mark the next word, otherwise the sentence is marked.


## 4. Example Verification

In order to fully verify the validity of the intelligent recognition model for English translation, the model is tested for English translation proofreading through experiments, and the data in the experiment process are recorded to analyze the system performance. In the experiment, there are 400 character proofreading vocabulary, 500 short text proofreading number and 25 kB/s word recognition speed. The accuracy of English translation before and after proofreading was compared. Table3 shows the accuracy of English translation before and after proofreading.Table 3
Accuracy of English translation before and after proofreading.
Serial numberTranslation accuracyProofread before (%)After proofreading (%)158.399.2272.298.5367.498.3472.299.2575.298.4Precision mean69.0498.64It can be seen from Table3 that the highest accuracy of English translation results before proofreading is 75.2%, and the accuracy is as high as 99.2% after using the intelligent recognition of the modules in the paper. There is a big difference in accuracy between the two, which verifies the effectiveness of the intelligent recognition model of English translation in the system.Now we compare the situation of multiple sensors with that of single sensor A, and other data remain unchanged. The experimental results are shown in Figure4:Figure 4
Estimation error of multiple sensors and single sensor.In order to fully display the advantages of the design model, the intelligent recognition model based on syntax and phrase is used to realize the comparative experiment. The number of node control points of each system was recorded during the experiment and the distribution of node control was analyzed. The distribution of loci can describe the semantic and contextual relevance of English translation, and the dense distribution of loci indicates that the system has a high accuracy in English translation recognition. Figure5 shows the distribution of the node control points identified by the system. The compact distribution of the node control points in Figure 5 indicates that the system has high recognition performance; the proofreading result is more accurate, and the problem of contextual incoherence in English translation is solved.Figure 5
Distribution of node control points of recognition system in this paper.In the multisensor data fusion optimization model, the constant is the proportional coefficient used to adjust the positive and negative factors relative to the method’s own voting value (marked accuracy). The two values have a significant influence on the result of the final vote. Figure6 shows that the annotation accuracy of multisensor data fusion optimization reaches the optimal value of 96. 32% with 1.6 as step size, respectively. It is 1.493% higher than the average of single annotation result, 0.86 higher than the best single annotation result, thus reducing the error rate by 27.8% and 18.2%, respectively.Figure 6
Further experiment of multisensor data fusion optimization.Figure7 shows the root mean square error of the target on X and Y axes based on multisensor data fusion optimization. Figure 8 is the root mean square error on the target X and Y axes based on advanced Intelligent English translation.Figure 7
Optimized fusion algorithm based on multisensor data fusion.Figure 8
Advanced English intelligent translation fusion algorithm.According to the quality evaluation level of machine translation, the translation rated as A or B is the correct translation in our general sense. According to statistics, out of 360 English sentences submitted for translation, 309 sentences were considered as grade A or B. Accordingly, the system achieves 89% translation accuracy under the condition of perfect knowledge. Among them, 49 sentences were translated by instance mode mechanism (13.6%), and the translation accuracy rate reached 100%. 301 sentences were translated based on conversion mechanism (86.1%), and the translation accuracy rate was 86.2%. According to the experimental results, although the system has achieved a high translation accuracy, there are still many errors in some translations. After analysis and induction, there are 12 types of translation errors, and their respective proportions are shown in Table4:Table 4
Analysis of translation error types.
No.Wrong typeProportionInstructions1Improper translation35. 29%2Parallel composition14. 71%Identification and translation, unclear concept3Adverbials modify the object error11. 31%4Semantic error7. 43%Semantics are poorly defined5Rear modification5. 57%Infinitive6Passive voice4. 23%Translation of “by”7Insert the ingredients4. 05%8Gerund and present participle3. 86%Misjudgment of part of speech9Position of the question word is wrong3. 73%Structural adjustment problem10Structure of3. 54%Conversion rules are not detailed enough11Subject-predicate agreement2. 88%12Present participle acts as an accompanying adverbial2. 34%


## 5. Conclusion

Based on the above analysis, it can be concluded that the multisensor data fusion optimization translation based on the English translation activity course is a new translation method created based on the combination of information data and multisensor data fusion optimization, which represents the development direction of translation studies in the future. Using multisensor data fusion and optimization of translation to continuously strengthen the translation foundation of English translation activities in the future and improve the development of translation studies is of strong vitality and a good driving force for the development of education and culture in the future. In view of the proposed multisensor data fusion algorithm based on optimal fusion set and the proposed new conflict evidence synthesis algorithm based on D-S evidence theory, the accuracy and anti-interference ability of the algorithm need to be further studied and the experimental results are verified.



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