Human presence detection based on wireless signals in the millimeter wave band was an emerging technology proposed in recent years. The principle was that respiratory vital sign activity when the human body was at rest can cause micro-movements in the thoracic and abdominal cavities or changes in human walking, hand waving, arm swinging and other movements, which in turn had an impact on the reflected signals. Based on this, the analysis of wireless signals could be used to mine the micro-doppler information of the detection target, so as to achieve the detection of human presence. With the popularization and promotion of communication technology, the characteristics of low power consumption, no personal privacy, and "all-weather" make millimeter wave radar more and more popular in smart home application scenarios. This paper focuses on the analysis of Acconeer's PCR10P development board human presence detection for high inverse static object false alarm and proposes a high inverse suppression algorithm based on the target phase. By comparing the original presence detection algorithm of Acconeer's PCR10P development board and its XM/XB112 development board after adding PCA algorithm on this basis and the human presence detection test results of the proposed phase tracking presence detection algorithm and analyzing the advantages and disadvantages of the two optimized algorithms.
A multi-objective optimal design scheme for tolerance combination based on improved NSGA-II algorithm is established to reduce the production cost, standardize the tolerance design and improve the qualification rate of the rear cover of a product. The production cost function and quality loss function are used as the objective function, and the genetic algorithm with elite strategy and improved genetic variation factor is used to optimize the simulation of the assembly tolerance combination and obtain the optimal Pareto solution set. The results of the tolerance combination show that the optimized assembly tolerance combination is used to reduce both production cost and quality loss cost.
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