This paper proposes an adaptive method of detecting objects on the image of an optoelectronic device. The method is based on reconstructing a reference signal-image and forming a statistic in the form of the maximum eigenvalue of the selective correlation matrix for making a decision concerning the detection of an object, using the Neyman-Pearson criterion. The information contained in the images recorded is used when there are no a priori data concerning the background-target situation. A block diagram of the algorithm is given, along with the results of estimating the efficiency index for detecting objects under various conditions.
A criterion and an algorithm of detecting dynamic objects (DOs) in a complex background formed by an intense cumulus and high-altitude cumulus are proposed. The object image has a small size (point image) and low contrast. The principle of DO detection is fractal-correlation: it is based on the use of sampling as a relationship of likelihood functions of similar alternative conditions: either "only complex background within the sight of an optoelectron device (OED)" or "DO on the complex background within the sight of an OED." The DO detection algorithm is designed as a binary accumulator according to the most powerful local criterion. The critical limit of decision making is defined by the Neumann -Pearson lemma for the acceptable possibility of false detection of a DO. Simulation proves the algorithm to be highly effective.
The problem of the test signal recovery is urgent during the monitoring of the current state of a dynamical system. Such a signal is usually wideband with respect to frequency, has steep leading and trailing edges, and a short duration in the time domain. Under actual conditions, it should be recovered from a small sample of readouts (measurements) at the system output. In this paper, we propose a method of the signal recovery by the generalized Kotel'nikov series for the subsequent estimation of its parameters in the time and frequency domains. The method is more efficient than the existing signal-recovery methods. Its efficiency is confirmed by the quantitative-analysis results.At present, recovery of a signal whose frequency spectrum is in the interval from −α to α is usually performed using the basis of Kotel'nikov functions. For this, the signal in the time intervalHere, f (kπ/α) are the values of the function f (t) measured at the points kπ/α, where k = 0, ±1, ±2, . . . , ±N . The exact representation of the signal is written asHowever, such a series has the following disadvantages: (i) slow convergence to the approximated function;(ii) the interval between the discrete readout times should not exceed π/α; (iii) for the finite number of the series terms (−N ≤ k ≤ N ), the high-frequency harmonics are predominant in the spectrum of the function f (t) if the values of f (kπ/α) near the boundary points of the interval [−T = −Nπ/α, Nπ/α = T ] significantly exceed the values of f (kπ/α) near the interval center. As a result, the Kotel'nikov-series spectrum becomes strongly oscillatory and, therefore, an analog-digital device and an algorithm for recovery of the function f (t) become more complicated.If the readouts can be performed only with the period ∆ = π/α for α < β, where β is the boundary frequency of the recovered-signal spectrum, then the conditions of the Kotel'nikov-Shannon sampling theorem are not satisfied and use of the Kotel'nikov series becomes unjustified.
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