The set of suspected targets detected in the scene captured by the photoelectric theodolite must contain many interference targets with complex grayscale/shape/dynamic characteristics, resulting in the failure of target capture. Aiming at the problem of anti-interference stable capture scenario, it is mainly classified and identified from the aspects of the continuity of the change in the time domain and airspace of the target and the interference characteristics, as well the characteristics differences. This paper proposes a Target anti-jamming acquisition method based on decision tree classification technology, which is aimed at the problem of stable acquisition. First, it analysis of target/interference characteristics. based on the analysis of shape/ratiation/motion characteristics of historical images and scenes, the machine offline semi -supervised learning method of the decision tree method is adopted to form reference feature set by selecting multiple features of the target/interference. Secondly, on the basis of the target/interference feature extraction, binary tree classifier is used in online processing. According to a reference feature layer by layer, the relationship between the feature and the statistical feature of the image is associated, the change of the target feature is perceived, and the suspected target set to be detected is discriminated and classified one by one, so as to identify the target and eliminate the interference target.
In the field of conventional weapon test and identification, optical measurement is often used to measure the exterior ballistic parameters of targets. With the variety of measuring equipment and measuring bands such as visible light/medium-wave/long-wave, the profile/shape and radiation/luminance distribution of the exhaust plume target imaging in the optical measurement system are great different, so that the target interpretation position changes greatly with the change of the exhaust plume .The variation of the plume depends on the flight state of the weapon system, inmost cases there is no prior data support, we judge by the experience of the surveyors, so this processing mode affects the credibility of the measurement data, especially in the case of high precision requirements such as characteristic parameter and miss distance processing. Aiming at the Consistency problem of multispectral imaging data of the exhaust plume target, using the inversion imaging method of fluid numerical simulation to characterize the multispectral imaging of the weapon system under various flight states, and theoretically evaluate the imaging deviation. Whether this method is feasible depends on accuracy of the wake simulation calculation meets the actual needs. For this reason, the wake combustion products and their components of a certain type of rocket are studied. Based on the plume component analysis, establish a plume directional radiation calculation model. Through the close acquisition of the visible light/ medium-wave/long-wave images in the initial stage of the rocket, the profile characteristic parameters are compared with the simulated radiation images obtained by numerical simulation. The results show that ,the main body of the exhaust plume edge error is located within ± 20%, and the comparison result in the core area of the exhaust plume is better than ±10%,so the profile data of the core area can be used as the reference for consistency analysis. This method breaks through the current situation of systematic error estimation based on experience in theory, and has high guiding significance for the evaluation of the processing accuracy of external ballistic test data.
With the development of infrared technology, the cost of detector is decreasing and the application of staring imaging is becoming more and more extensive, which promotes the improvement of the antimissile warning level of the weapon platform. In the paper, a method for the antitank missile warning and precise recognition is presented based on the infrared characteristics of missile exhaust plume and the motion features of ballistic target. Firstly, the current status of missle warning technology is summarized. A hemispherical infrared staring detector of actual application is designed for ground weapon platform. The key technical indicators are given, and the technical routine of point target imaging warning is illustrated. Based on the above warning devices and typical observation scenarios, the relationship between target infrared radiation, observation distance and motion velocity is established, and a detection warning model is established. Combining with the actual application scene, the infrared warning distance window optimization, velocity calculation precision and point target recognition threshold are synthetically verified, and the analysis and quantification of critical data are gived. It showes that in typical scene detection and warning, the modeling of infrared characteristics and motion features can effectively achieve the antitank missile warning; under the control of observation conditions, the uncertainty of infrared radiation and motion velocity are less than 10%, which can be used to precise recognition the target under threshold of 30%.
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