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  • Ескіз
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    Accurate detection of multiple targets by uniform rectangular array radar with threshold soft update and area rescanning
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2022) Romanuke, Vadim
    Background. If the intensity of moving targets within a surveyed area is low, an optimal number of uniform rectangular array (URA) radar sensors is in either the minimally-sized URA (or close to it) or maximally-sized URA (or close to it), where the URA size is regulated by (symmetrically) turning off vertical and horizontal sensors. However, this does not guarantee detection of any target because sometimes the threshold detection, by which the main parameters of a pair of two targets are estimated, fails even by using the soft threshold approach when the threshold is gradually decreased while the detection fails. Objective. In order to improve detection of multiple ground-surface targets by a URA radar, the goal is to decrease a number of detection fails, when targets are just missed. For this, the approach of threshold soft update and a set of quasioptimal URA sizes included 20 25  and 35 35  URAs are to be used by rescanning the area if the detection fails. Methods. To achieve the goal, the functioning of the URA radar is simulated for a set of randomly generated targets, where roughly a half of the set is to be of single targets, and the other half is to be of pairs of targets. The simulation is configured and carried out by using MATLAB® R2021b Phased Array System ToolboxTM functions based on a model of the monostatic radar. Results. Neither the soft threshold approach, nor the rescanning increase the detection accuracy. However, when either the soft threshold or rescanning is applied, or they both are applied, the number of detections is increased. The increment can be evaluated in about 2.7 %, but the expected high-accurate detection performance slightly drops. This is caused by that the soft thresholding and rescanning attempt at retrieving at least some information about the target instead of the detection fail. Conclusions. Using the threshold soft update approach along with a more frequent rescanning decreases a number of detection fails. Besides, the soft thresholding and rescanning allow slightly decreasing the number of URA sensors sufficient to maintain the same detection accuracy by increasing the averaged number of single-target and two-target detections at least by 2.5 %. The increment in a number of detected targets on average is equivalent to increasing the probability of detection.
  • Ескіз
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    Optimal construction of the pattern matrix for probabilistic neural networks in technical diagnostics based on expert estimations
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2021) Romanuke, Vadim
    In the field of technical diagnostics, many tasks are solved by using automated classification. For this, such classifiers like probabilistic neural networks fit best owing to their simplicity. To obtain a probabilistic neural network pattern matrix for technical diagnostics, expert estimations or measurements are commonly involved. The pattern matrix can be deduced straightforwardly by just averaging over those estimations. However, averages are not always the best way to process expert estimations. The goal is to suggest a method of optimally deducing the pattern matrix for technical diagnostics based on expert estimations. The main criterion of the optimality is maximization of the performance, in which the subcriterion of maximization of the operation speed is included. First of all, the maximal width of the pattern matrix is determined. The width does not exceed the number of experts. Then, for every state of an object, the expert estimations are clustered. The clustering can be done by using the k-means method or similar. The centroids of these clusters successively form the pattern matrix. The optimal number of clusters determines the probabilistic neural network optimality by its performance maximization. In general, most results of the error rate percentage of probabilistic neural networks appear to be near-exponentially decreasing as the number of clustered expert estimations is increased. Therefore, if the optimal number of clusters defines a too “wide” pattern matrix whose operation speed is intolerably slow, the performance maximization implies a tradeoff between the error rate percentage minimum and maximally tolerable slowness in the probabilistic neural network operation speed. The optimal number of clusters is found at an asymptotically minimal error rate percentage, or at an acceptable error rate percentage which corresponds to maximally tolerable slowness in operation speed. The optimality is practically referred to the simultaneous acceptability of error rate and operation speed.
  • Ескіз
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    Uniform rectangular array radar optimization for efficient and accurate estimation of target parameters
    (National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 2022) Romanuke, Vadim
    Background. If the intensity of moving targets within a surveyed area is low, some sensors of the uniform rectangular array (URA) radar can be (symmetrically) turned off. However, this does not guarantee detection of any target because sometimes the threshold detection, by which the main parameters of the target are estimated, fails. Objective. In order to improve detection of ground-surface targets, the goal is to find an optimal number of URA radar sensors along with improving the stage of threshold detection. The criterion is to determine such a minimum of these sensors at which the main parameters of the target are accurately estimated. In addition, the threshold detection is to be modified so that a number of detection fails would be lesser. Methods. To achieve the said goal, the URA radar is simulated to detect a single target. The simulation is configured and carried out by using MATLAB® R2021b Phased Array System ToolboxTM functions based on a model of the monostatic radar. Results. There is a set of quasioptimal URA sizes included minimally-sized and maximally-sized URAs. The best decision is to use, at the first stage, the minimally-sized URA (by turning off the maximal number of vertical and horizontal sensors). If the detection fails, then the maximally-sized URA radar is tried. If the detection fails again, the next minimally-sized URA is tried, in which one horizontal sensor is additionally turned on. Additional horizontal sensors must be enabled while the detection fails but the number of vertical sensors should not be greater by about a third of their minimal number. Conclusions. An optimal number of URA radar sensors is in either the minimally-sized URA (or close to it) or maximallysized U RA ( or c lose t o i t). The U RA s ize i s r egulated b y ( symmetrically) turning off vertical and horizontal sensors. The threshold detection stage is modified so that the threshold is gradually decreased while the detection fails. This allows increasing a number of detected targets on average, which is equivalent to increasing the probability of detection.