Research And Implementation Of Dual-Band Infrared Target Passive Ranging Method Based On Elman Neural Network

Infrared target passive ranging technology uses the infrared radiation emitted by the target,and completes the estimation of the target distance by acquiring the radiation characteristics of the target.Passive infrared detection technology has higher detection accuracy,stronger stealth ability and better atmospheric and smoke permeability.Therefore,infrared passive ranging technology is widely used in the pre-war detection system of military weapons,and has become the main technology for realizing the detection of enemy targets by military equipment.It has played a vital role in quickly obtaining the position of a hostile target and guiding it to accurately strike the target.This paper mainly uses the principle of passive ranging of infrared targets as technical means,and studies the establishment and optimization of the dual-band infrared target passive ranging model from the perspective of artificial intelligence algorithms.A ranging module is designed to prove that the ranging model proposed in this paper can effectively estimate the target distance.The main contents include:1.The theoretical basis of infrared target passive ranging technology is introduced,and the main influencing factors that lead to the attenuation of infrared radiation energy transmission in the earth’s atmosphere are analyzed.In view of the attenuation of the atmosphere,the infrared radiation data of the band and the band were selected as the data source of the dual-band infrared target passive ranging model.Modtran software was used to calculate the transmittance data under different atmospheric modes,aerosol modes,and zenith angles.,And then calculate the irradiance data corresponding to the target transmission distance according to the basic law of radiation,and use these data as the original data source for subsequent research and testing.2.Carried out the distance estimation and research based on Elman neural network.Due to Elman’s own structural characteristics and gradient descent training algorithm,the initial randomly generated weights and thresholds of the network can easily cause the entire algorithm to fall into local extremes.In order to further improve the accuracy of ranging,this paper proposes an infrared passive ranging model based on the improved Elman neural network based on genetic algorithm.The model uses real-number coding to genetically code the random weights and thresholds originally generated by the Elman network,and introduces genetic algorithms.The algorithm is iteratively optimized,and the parameters of the genetic algorithm and the data combination input by the Elman neural network are optimized through multiple experiments.Finally,by comparing the test results of four ranging algorithms,it is proved that the ranging model proposed in this paper has higher ranging accuracy and smaller error fluctuation range.3.An infrared ranging module is designed and implemented,which is composed of a signal card with a built-in neural network ranging algorithm and a chassis with heat dissipation function.The irradiance data and zenith angle data are input to the internal TMS320F28335 chip of the module through the RS-422 serial port,and the predicted distance is output to the PC through the serial port after the calculation by the internal ranging algorithm of the chip.This paper uses irradiance,zenith angle,and weather mode parameters under various weather conditions to compose 41-bit input test data,and the output result is a distance estimate.The test results show that the relative error of the module’s ranging is within ±10%,the maximum time-consuming ranging is 12.037 ms,and the average timeconsuming is 11.900 ms,which proves that the module meets the performance requirements of the infrared ranging model in this article.