SUMMARY: Multi-sensor fusion (or) multi-sensor information fusion is an emerging technology that is applied in the field of robotics, image and signal processing and medical diagnosis. The main objective of this article is to give an idea of ​​different sensor fusion performances and technical characteristics obtained from different techniques. It is based on the principle of integrating data from different sensors which could enable better understanding of data from different varieties of sources to achieve better performance in many individual sources such as weather forecasting, analysis and the estimation of statistical data. In systems engineering, fusion methods are important because the system could provide capabilities to systems with different sensors, especially beyond that individual system of sensors. Multi-sensor data fusion enables the integration of data from various sensors to improvise perception of the environment and facilitates decision-making, planning, execution and control of automation. From this article, an idea has been proposed about the smart home system using various sensors that help in framing the house, which helps people in framing the elderly at home. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essay KEYWORDS: Multi-sensor data fusion, image fusion, neural network image fusion, decision-level fusion, data integration, smart home.INTRODUCTION: Multi-sensor fusion is like an animal who evaluates his environment using environmental sounds (signals) that help him find whether his environment is suitable for him or not. Multi-sensor data fusion is like the human brain that helps us recognize the meaning of different tastes, it is also like the animal that detects environmental sounds. Multi-sensor fusions also play a major role in combining sensor data from various sources to derive reliable and accurate results that are unachievable from the individual sensor. The sensor fusion process integrates data in such a way that results in the best performance that could be achieved if each piece of information was used alone. From this proposal, it will give an idea of ​​smart home from the multi-sensor process in homes. The smart home is based on motion detections and contributes to theft protection which indicates the detection to the user. Some of the factors that improve system performance.Improved system reliability.Extended coverage.Improved confidence.Shorter response time.Improved resolution.Some of the key issues are the nature of sensor types available with resolution.Sensor scanning capability .Capacity to implement the algorithm in the sensors and in the center where it is controlled. MULTI-SENSOR FUSION CONCEPT The concept of multi-sensor fusion is simple and has the basic four-level process as follows. INFORMATION SOURCE: This is a source of sensor information and other database that helps locate sensors at the required location. SOURCE PREPROCESSING: This step helps in data pre-selection and data allocation for the sensors that are going to be used. This helps the merging process to be verified before time. LEVEL 1 PROCESSING (Object Refinement): At this level, entities such as position, speed and identity are obtained, which helps the military totarget their enemies. This process involves four basic elements: data alignment (transforming data into a consistent framework and units of reference), association (using correlation methods), tracking actual and future positions of objects and identification. [1] LEVEL 2 PROCESSING (situation refinement): At this level it is useful to analyze some prior information that needs to be framed and wants to immediately inform the user, such as objects, events and information contextual. LEVEL 3 PROCESSING (Threat Refinement): Based on prior knowledge of Level 2 and predictions about the future situation help assess the current situation of the location. This is quite a difficult level of processing because it is not based on the database calculated in the system but on things that happen other than these, such as strategies, environmental threats, etc. . LEVEL 4 PROCESSING (process refinement): This process is a concerned meta-process. with another process. [2] It helps to control the other system by monitoring the performance, its ability to operate and identifying information that is feedback from the system that needed to be improved. In this process, it results in the multisensory data fusion goal we are aiming for. DATABASE MANAGEMENT: It is the action of the brain for the process in which it helps in storing all the data in the system. It helps to store, retrieve, archive, compress, query and protect data. It's complex because we can't predict what will happen in the process, so it's a complex process. HUMAN-MACHINE INTERACTION: This process provides the interface between the system and human through input and communication of fusion results to the operator. and the user. This interaction helps the user know the information coming from the entry and the incident that took place. Some human and user interaction models are JDL, Waterfall Fusion Process Model, Boyd Model, The LAAS Architecture, The Omnibus Model. INPUT-OUTPUT MODES: If input is given to the system, output must be obtained from the system. Thus, the process of merging input and output is carried out in six different modes. DATA IN - DATA OUT: In this method, the input and output are in the form of data. This type of method is mainly used in the fusion process. This method is used at the front of the processing flow and primarily works in signal and image processing systems. FEATURE IN – FEATURE OUT: In this method, it is based on the features obtained at both the input and output. This happens in the middle of the processing flow. Here, the information obtained in raw measurement form is then combined into qualitative and quantitative data. ENTRY DECISION – EXIT DECISION: It is based on the entry and exit decision. This mainly happens at the end of the processing flow. This is the process of integrating decisions from different sensors and the data can be raw or extracted from functionality. This method is adopted in the case where the sensors used are compatible. DATA IN – FEATURE OUT: In this method, the input is based on the raw data from sensors or other input facilities and the output is in feature format (representing it visually) with the environment or phenomenon that we consider. FEATURE IN – DECISION OUT: Here the input is accepted as the feature format and generates the desired decision output. Here the input comes from the sensors and the output which is generated when the decision is displayed to the user. This type is mainly used in.