manysensorThe basic principle of information fusion technology is similar to the process of the human brain's comprehensive processing of information, which involves the multi-level and multi spatial complementary and optimized combination processing of various sensors, ultimately resulting in a consistent interpretation of the observed environment. Today we will mainly introduce the four basic characteristics of multi-sensor fusion systems, hoping to help everyone.

The multi-sensor fusion system has four significant characteristics:

1. Redundancy of Information: For a certain feature of the environment, multiple sets of information can be obtained through multiple sensors (or multiple different moments of a single sensor), which are redundant and have different levels of reliability. Through fusion processing, more accurate and reliable information can be extracted from them. In addition, the redundancy of information can improve the stability of the system, thereby avoiding the impact on the entire system caused by the failure of a single sensor.

2. The complementarity of information: Different types of sensors can provide systems with information of different properties, which describe different environmental characteristics and are complementary to each other. If a coordinate space is defined consisting of all features, then the information provided by each sensor belongs only to a subspace of the entire space, and is independent of the space formed by other sensors.

3. Timeliness of information processing: The processing of each sensor is independent of each other, and the entire processing process can adopt a parallel thermal processing mechanism, thereby enabling the system to have faster processing speed and provide more timely processing results.

4. Low cost of information processing: Multiple sensors can obtain the same amount of information as a single sensor at a lower cost. On the other hand, if the information provided by a single sensor is not used to achieve other functions, the cost of a single sensor and the sum of the costs of multiple sensors are equivalent.

Information fusion, as a comprehensive processing process for multi-sensor information, has inherent complexity. In the process of information fusion processing, there are three main architectures for information fusion systems based on different methods of processing raw data: centralized, distributed, and hybrid.

1. Centralized: Centralized processing directly sends the raw data obtained by each sensor to the central processor for fusion processing, which can achieve real-time fusion. Its data processing accuracy is high and the algorithm is flexible. The disadvantage is that it requires high processor requirements, low reliability, and large data volume, making it difficult to implement.

2. Distributed: Each sensor first performs local processing on the raw data obtained, including preprocessing, classification, and feature extraction of the raw data, and makes decisions based on their respective decision criteria. Then, the results are sent to the fusion center for fusion to obtain the final decision. Distributed systems have low communication bandwidth requirements, fast computing speed, good reliability and continuity, but their tracking accuracy is not as high as centralized systems.

3. Hybrid: In most cases, the above two are combined in different ways to form a hybrid structure. It retains the advantages of the two types of systems mentioned above, but comes at a relatively high cost in terms of communication and computation. However, such systems also have the advantages of the two types of systems mentioned above, and in practical situations, such structures are often adopted.