Diss / 10

target track. Further, taking into consideration the coordinates of the new target pip, we make more exact the parameters of target track (block 4). For this purpose, one of the algorithms discussed in the this chapter is used. As a rule, this is the algorithm ensuring a filtering of maneuvering target track parameters, since the output information is used in the interests of the user (block 5). After completing the operations carried out by block 5, the processing of the next target pip is finished and the algorithm goes to choose a new target pip from the buffer.

•If a new target pip does not fall into the gate of any trajectory of a tracking target, in this case, this target pip is checked for a target track belonging to any detected trajectory (block 6). For this purpose, the target pip belonging to future gates formed by results of beginnings of new target tracks is checked. If the target pip belongs to the periodical

gate selected from the data array Dtgdecision of detected trajectories, then this target pip is considered as an extension of this target track. Taking into consideration the coordinates of the new target pip, we make more exact the parameters of the detected tracking target trajectory (block 7). After that, we check a detection criterion and the target track is recorded into the target track array and the algorithm goes to process the next target pip.

•If a new target pip belongs to no one gates of detected trajectories of tracking targets, then this target pip is checked to belong to the gates of initial lock-in formed around the individual target pips considered at the previous stages as beginnings of possible new trajectories of tracking targets (block 9). If a new target pip belongs to a regular initial lock-in gate

from the data array Dtginitial, then we compute initial values of target track parameters by two target pips, that is, the algorithm of beginning a new trajectory of tracking target is realized

(block 10). This beginning is transferred to the data array Dtgdecision, and the algorithm starts to process the next target pip.

•If a new target pip does not belong to any initial lock-in gates, then it is recorded into the data array Dtginitial as a possible beginning of a new tracking target trajectory.

Thus, under an adopted structure to process new target pips (to form beginnings of tracking target trajectory) a cycle of single realization of the considered algorithm is the random variable, statistical characteristics of which are discussed in the next chapters. In conclusion, we note that the discussed flowchart of the unified complex radar signal reprocessing algorithm is not typical, that is, acceptable for all practical situations. This is only an example allowing us to make some calculations during further analysis.

5.8  SUMMARY AND DISCUSSION

There are many sources of error in radar-tracking performance. Fortunately, most are insignificant except for very high-precision tracking-radar applications such as range instrumentation, where the angle precision required may be on the order 0.05 mrad (mrad, or milliradian, is one thousandth of a radian, or the angle subtended by 1-m cross range at 1000-m range). Many sources of error can be avoided or reduced by radar design or modification of the tracking geometry. Cost is a major factor in providing high-precision tracking capability. Therefore, it is important to know how much error can be tolerated, which sources of error affect the application, and what are the most cost-effective means to satisfy the accuracy requirements. Because tracking radar subsystems track targets not only by angle but also in range and sometimes in Doppler, the errors in each of these target parameters must be considered in most error budgets. It is important to recognize the actual CRS information output. For a mechanically moved antenna, the angle-tracking output is usually obtained from the shaft position of the elevation and azimuth antenna axes. Absolute target location relative to their coordinates will include the accuracy of the survey of the antenna pedestal site.

The motions of a target with respect to the CRS causes the total echo signal to change with time, resulting in random fluctuations in the radar measurements of the parameters of the target. These fluctuations caused by the target only, excluding atmospheric effects and radar receiver noise contributions, are called the “target noise.” This discussion of target noise is based largely on aircraft, but it is generally applicable to any target, including land targets of complex shape that are large with respect to a wavelength. The major difference is in the target motion, but the discussions are sufficiently general to apply to any target situation. The target track model given by (5.1) must take into consideration, first of all, the target track parameter disturbance caused by a nonuniform environment, in which the target moves, atmospheric conditions, and, also, inaccuracy and inertness of control and target parameter stabilization system in the course of target moving. We may term this target track parameter disturbance as the control noise, that is, the noise generated by the target control system. As a rule, the control noise is presented as the discrete white noise with zero mean and the variance σ2n. In addition to the control noise, the target track model must take into consideration specific disturbances caused by unpredictable changes in target track parameters that are generated by target in-flight maneuvering. We call these disturbances the target maneuver noise. In a general case, the target maneuver noise is neither white noise nor Gaussian noise.

In the solution of the filtering problems, additionally to the target track model there is a need to specify a function between the m-dimensional vector of measured coordinates Yn and s-dimensional vector of estimated parameters θn at the instant of nth measuring. This function, as a rule, is given by a linear equation. In the considered case, the observed coordinates are the current target coordinates in a spherical coordinate system, the target range rn, the azimuth βn, and the elevation εn, or some specific coordinates for the CRS and radar coordinates, for example, the radar range, cosine between the radar antenna array axis and direction to the target. In some CRSs, the radial velocity r.n can serve as the measured coordinate. We note that the target track model jointly with the model of measuring process forms the model of united dynamic system representing a process subjected to filtering.

In accordance with developed procedures and methods to carry out statistical tests in mathematical statistics, the following approaches are possible to calculate the a posteriori pdf and, consequently, to estimate the parameters: the batch method when the fixed samples are used and the recurrent algorithm consisting in sequent accurate definition of the a posteriori pdf after each new measuring. Using the first approach, the a priori pdf of the estimated parameter must be given. Under the use of the second approach, the predicted pdf based on data obtained at the previous step is used as the a priori pdf on the next step. Recurrent computation of the a posteriori pdf of estimated parameter, when a correlation between the model noise and measuring errors is absent, is carried out by the formula (5.17). In a general case of nonlinear target track models and measuring process models, computations by the formula (5.17) are impossible, as a rule, in closed forms. Because of this, under solution of filtering problems in practice, various approximations of models and statistical characteristics of CRS noise and measuring processes are used. Methods of linear filtering are widely used in practice. Models of system state and measuring of these methods are supposedly linear, and the noise is considered as the Gaussian noise.

In the course of discussion of linear filtering algorithms and extrapolation under the fixed sample size of measurements, we consider several types of target track, namely, (a) the linear target track,

(b) the polynomial target track, and (c) the second-order polynomial target track. Additionally, we consider the algorithm of extrapolation of target track parameters and dynamic errors of target track parameter estimation. The likelihood function of the vector parameter θN estimated by sequent measurements {YN} is given by (5.25). The vector likelihood equation is presented by (5.27). In a general case, the final solution of likelihood equation for correlated measure errors is defined by (5.29). Potential errors of target track parameters estimated by the polynomial target track model can be obtained using a linearization of the likelihood equation (5.27). The final form of the correlation matrix of errors of target track parameter estimations can be presented by (5.33). The variance

of error of the smoothed coordinate estimation using three uniformly precise measurements is equal to 5/6 of the variance of individual measurement error. The variance of error of the coordinate increment estimation is only one half of the variance of error of individual measurement error owing to inaccurate target velocity estimation, and the correlation moment of relation between the errors of coordinate estimation and its increment is equal to one half of the variance of error of individual coordinate measurement. Figure 5.3 represents a coefficient of accuracy under determination of the normalized elements of correlation error matrix of linear target track parameter estimation versus the number of measurements. As follows from Figure 5.3, to obtain the required accuracy of estimations there is a need to carry out a minimum of 5–6 measurements. Turning to the algorithms of linear target track parameter estimations (5.43) and (5.44), we can easily see that these algorithms are the algorithms of nonrecursive filters and the weight coefficients ηxˆ(i) and η 1xˆ (i) form a sequence of impulse response values of these filters. To apply filter processing using these filters, there is a need to carry out N multiplications between the measured coordinate values at each step, that is, after each coordinate measurement, and the corresponding weight coefficients and, additionally, N summations of the obtained partial products. To store in memory device (N − 1) records of previous measurements, there is a need to use high-capacity memory. As a result, to realize such a filter, taking into account that N > 5, there is a need to use high-capacity memory and this realization is complex. Formulas (5.70) through (5.75) show that in the case of equally discrete and uniformly precise measurements, the optimal estimation of target second-order polynomial track parameters is determined by the weighted summing of measured coordinate values. The weight coefficients are the functions of the sample size N and the sequence number of sample i in the processing series. The coefficient of accuracy under determination of the normalized elements of correlation error matrix of target second-order polynomial track parameter estimation versus the number of measurements is shown in Figure 5.4. Comparison of diagonal elements of the matrix (5.80) that are characteristics of accuracy under estimation of the second-order polynomial target track coordinate and the first increment with analogous elements of the correlation error matrix of target linear track parameter estimation (see Figure 5.3) shows that at low values of N the accuracy of target linear track parameter estimation is much higher in comparison with the accuracy of target second-order polynomial track parameter estimation. Consequently, within the limits of small observation intervals it is worthwhile to present the target track as using the first-order polynomial. In this case, a high quality of cancellation of random errors of target track parameter estimation by filtering is guaranteed. Dynamical errors arising owing to mismatching of target movement hypotheses can be neglected as a consequence of the narrow approximated part of the target track.

The extrapolation problem of target track parameters is to define the target track parameter estimations at the point that is outside the observation interval using the magnitudes of target track parameters determined during the last observation or using a set of observed coordinate values. Under polynomial representation of an independent coordinate, the target track parameters extrapolated using the time τex are defined by (5.81) through (5.83), which allow us to define the extrapolated coordinates for each specific case of target track representation. The error correlation matrix of target linear track parameter extrapolation at equally discrete coordinate measurement is given by (5.90). If the independent target track parameter coordinate is represented by the polynomial of the second order, the target track parameter extrapolation formulas and the error correlation matrix of target linear track parameter extrapolation are obtained in an analogous method.

Inconsistency between the polynomial model and nonlinear character in changing the vector of estimated target track parameters leads to dynamic errors in smoothing that can be presented in the form of differences between the true value of the vector of estimated target track parameters and the mathematical expectation of the vector estimate. To describe the dynamic errors of estimations of the target track parameters by algorithms that are synthesized by the maximum likelihood criterion and using the target track polynomial model, we employ the well-known theory of errors at approximation of arbitrary continuously differentiable function f(t) within the limits of the interval tN − t0 by the polynomial function of the first or second order using the technique of least squares.

Comparison of the dynamical errors of approximation of the polar coordinates by the polynomials

of the first and second power at the same values of target track parameters Vtg, rmin, Teq and the sample size (N − 1)Teq shows us that the dynamic errors under linear approximation are approxi-

mately one order higher than under quadratic approximation. Moreover, under approximation of rtg(t) and βtg(t) by the polynomials of the second power, the dynamic errors are small in comparison with random errors and we can neglect them. However, as shown by simulation, if the target is in an in-flight maneuver and the CRS is moving, the dynamic errors are essentially increased, because nonlinearity in changing the polar coordinates is increased sharply at the same time.

Methods of estimation of the target track parameters based on the fixed sample of measured coordinate, which are discussed in the previous sections, are used, as a rule, at the beginning of the detected target trajectory. Implementation of these methods in the course of tracking is not worthwhile owing to complexity and insufficient accuracy defined by the small magnitude of employed measures. Accordingly, there is a need to employ the recurrent algorithms ensuring a sequential, that is, after each new coordinating measurement, adjustment of target track parameters with their filtering purposes. At the recurrent filter output, we obtain the target track parameter estimations caused by the last observation. By this reason, a process of recurrent evaluation is further called sequential filtering, and corresponding algorithms are called the algorithms of sequential filtering of target track parameters.

In a discussion of the recurrent filtering algorithms, we obtain that the optimal recurrent linear filtering algorithm is defined by the Kalman filtering equations. A discrete optimal recurrent filter possesses the following properties: (a) filtering equations have recurrence relations and can be realized well by the computer system; (b) filtering equations simultaneously represent a description of procedure to realize this filter, and in doing so, a part of the filter is similar to the model of target track (compare Figures 5.2 and 5.6); (c) the error correlation matrix of target track parameter estimation Ψn is computed independently of measuring Yn; consequently, if statistical characteristics of measuring errors are given then the correlation matrix Ψn can be computed in advance and stored in a memory device; this essentially reduces the time of realization of target track parameter filtering.

As we can see from Figure 5.7, with an increase in n the filter gains in the coordinate and velocity are approximated asymptotically to zero. Consequently, with an increase in n the results of last measurements at filtering the coordinate and velocity are taken into consideration with less weight, and the filtering algorithm ceases to respond to changes in the input signal. Moreover, essential problems under realization of the filter arise in computer systems with a limited capacity of number representation. At high n, the computational errors are accumulated and commensurable with a value of the lower order of computer system that leads to losses in conditionality and positive determinacy of the correlation matrices of extrapolation errors and filtering of the target track parameters. The “filter divergence” phenomenon, when the filtering errors are increased sharply and the filter stops to operate, appears. Thus, if we do not take specific measures in correction, the optimal linear recurrent filter cannot be employed in complex automatic radar systems. To overcome this problem is a great advance in automation of CRSs.

In a general form, the problem of recurrent filter stabilization is the problem of ill-conditioned problem solution, namely, the problem in which small deviations in initial data cause arbitrarily large but finite deviations in solution. The method of stable (approximated) solution was designed for illconditioned problems. This method is called the regularization or smoothing method. In accordance with this method, there is a need to add the matrix αI to the matrix of measuring errors Rn given by (5.127), where I is the identity matrix, under the synthesis of a regularizing algorithm of optimal filtering of the unperturbed dynamic system parameters (5.134). A general approach to obtain the stable solutions by the regularization or smoothing method is the artificial rough rounding of measuring results. However, the employment of this method in a pure form is impossible since a way to choose the regularization parameter α, is generally unknown. In practice, a cancellation of divergence of the recurrent filter can be ensured by effective limitation of memory device capacity including in the recurrent filter. In line with this, we consider several cases. For example, in the case of introduction

of an additional term into the correlation matrix of extrapolation errors we obtain that the recurrent filter with an additional term approximates the filter with finite memory capacity at the corresponding choice of the coefficients c0 , c1,…, cs. Under introduction of artificial aging of measuring errors and equally discrete and uniformly precise coordinating measurements, the smoothing coefficients of the filter are converged to positive constants in the filter safe operating area. However, it is impossible to find the parameter s for this filter so that the variance and dynamic errors of these filters and the filter with finite memory capacity are matched. In the case of gain lower bound for the simplest case of the equally discrete and uniformly precise coordinating measurements, the gain bound is defined directly by the formulas for Gn at the given effective memory capacity of the filter. Computation and simulation show that the last procedure is the best way by criterion of realization cost and rate to define the variances of errors under the equally discrete and uniformly precise coordinating measurements from considered procedures to limit the recurrent filter memory capacity. The first procedure, that is, the introduction of an additional term into the correlation matrix of extrapolation errors, is a little worse. The second procedure, that is, an introduction of the multiplicative term into the correlation matrix of extrapolation errors, is worse in comparison with the first and third ones by realization costs and rate of convergence of the error variance to the constant magnitude.

In the consideration of filtering methods and algorithms of target track parameters, we assume that the model equation of target track corresponds to the true target moving. In practice, this correspondence is absent, as a rule, owing to the target maneuvering. One requirement of successful solution of problems concerning the real target track parameter filtering is to take into consideration a possible target maneuver. The state equation of maneuvering target is given by (5.144). According to precision of CRS characteristics and estimated target maneuver, three approaches are possible for designing the filtering algorithm of real target track parameters: (a) the target has limited possibilities for maneuver, for example, there are only random unpremeditated disturbances in the target track (there is a need to note that the methods discussed in the previous section to limit a memory capacity of the recurrent filter by their sense and consequences are equivalent to the considered method to record the target maneuver since a limitation in memory capacity of the recurrent filter, increasing a stability, can simultaneously decrease the sensitivity of the filter to short target maneuvering); (b) we assume that the target performs only a single premeditated maneuver of high intensity within the observation time interval; in this case, the target track can be divided into three parts: before starting, in the course of, and after finishing the target maneuver, as the intensity vector of target maneuver at the instants of start and finish of target maneuvering are subjected to statistical estimation in this case by totality of input signals (measuring coordinates); consequently, in the given case, the filtering problem is reduced to designing a switching filtering algorithm or switching filter with switching control based on analysis of input signals; this algorithm concerns a class of simple adaptive algorithms with a self-training system;

(c) it is assumed that the targets subjected to tracking have good maneuvering abilities and are able to perform a set of maneuvers within the limits of observation time; these maneuvers are related to air miss relative to other targets or flight in the given space; in this case, to design the formula flowchart of filtering algorithms of target track parameters there is a need to have data about the mathematical expectation E(gmn) and the variance σ2g of target maneuver intensity for each target and within the limits of each interval of information updating; these data (estimations) can be obtained only based on an input information analysis, and the filtering is realized by the adaptive recurrent filter.

The problems of target maneuver detection or definition of the probability to perform a maneuver by target are solved in one form or another by adaptive filtering algorithms. Detection of target maneuver is possible by deviation of target track from a straight-line trajectory by each filtered target track coordinate. However, in a spherical coordinate system, when the coordinates are measured by a CRS, the trajectory of any target, even in the event that the target is moving uniformly and in straight line, is defined by nonlinear functions. For this reason, a detection and definition of target maneuver characteristics under filtering of target track parameters using the spherical coordinate system are

impossible. To solve the problem of target maneuver detection and based on other considerations, it is worthwhile to carry out a filtering of the target track parameters using the Cartesian coordinate system with the origin in the CRS location. This coordinate system is called the local Cartesian coordinate system. The formulas of coordinate transformation from the spherical coordinate system to the local Cartesian one are given by (5.149). Transformation to the local Cartesian coordinate system leads to an appearance of nonuniformly precise correlation between the coordinates at the filter input, which, in turn, leads to the complexity of filter structure and additional computer cost under realization. Also, there is a need to take into consideration that other operations of signal reprocessing by a CRS, for example, target pip gating, target pip identification, and so on, are realized in the simplest form in the spherical coordinate system. Therefore, the filtered target track parameters must be transformed from the local Cartesian coordinate system to the spherical one during each step of target information update. Thus, to solve the problem of adaptive filtering of the track parameters of maneuvering targets it is worth employing the recurrent filters, where the filtered target track parameters are represented in the Cartesian coordinate system and comparison between the measured and extrapolated coordinates is carried out in the spherical coordinate system. In this case, the detection of target maneuver or definition of the probability performance of target maneuver can be organized based on analysis of deviation of the target track parameter estimations from magnitudes corresponding to the hypotheses of straight-line and uniform target moving.

There is a statistical dependence between estimations of target track parameters by all coordinates in the considered recurrent filter. This fact poses difficulties in obtaining the target track parameter estimations and leads to tightened requirements for CRS computer subsystems. We can decrease the computer cost refusing from optimal filtering of target track parameters, making the filter simple. In particular, a primitive simplification is a rejection of joint filtering of target track parameters and passage to individual filtering of Cartesian coordinates with subsequent transformation of obtained target track parameter estimation coordinates to the polar coordinate system. The procedure of simplified filtering version is discussed. Comparison of accuracy characteristics of optimal and simple filtering procedures with double transformation of coordinates is carried out by simulation. For example, the dependences of root-mean-square error magnitudes of target track azimuth coordinate versus the number of measurements (the target track is indicated in the right top corner) for optimal (curve 1) and simple (curve 2) filtering algorithms at rmin = 10 and 20 km are shown in Figure 5.9. In Figure 5.9, we see that in the case of a simple filter, accuracy deteriorates from 5–15% against target range, course, speed, and the number of observers. At rmin < 10 km, this deterioration in accuracy is about 30%. However, the computer cost is decreased approximately one order.

Under adaptive filtering, the linear dynamic system described by the state equation given by (5.144) is considered as the target track model. Target track distortions caused by a deliberate target maneuver are represented as a random process, the mean E(gmn) of which is changed step-wise taking a set of fixed magnitudes (states) within the limits of range [−gmmax , + gmmax ]. Transitions of step-wise process from the state i to the state j are carried out with the probability Pij ≥ 0 defined by a priori data about the target maneuver. The time when the process is in the state i before transition into the state j is the random variable with arbitrary pdf p(ti). The mathematical model is the semi-Markov random process. Distortions of the target track caused by a deliberate target maneuver and errors of intensity estimations of deliberate target maneuver are characterized by the random component ηn in adaptive filtering algorithm. The matrices Φn, Γn, and Kn are considered as known. Initially, we consider the Bayesian approach to design the adaptive filtering algorithm for the case of continuous distortion action gmn.

As is well known, an optimal estimation of the parametric vector θn at the quadratic loss can

˄ 

be defined from (5.170). The problem to estimate the vector θn is reduced to weight averaging the estimations qˆn (gmn ) that are the solution of filtering problem at the fixed magnitudes of gmn.

The estimations qˆn (gmn) can be obtained by any way that minimizes the MMSE criterion including the recurrent linear filter or Kalman filter. The problem of optimal adaptive filtering will be solved

when the a posteriori pdf p(gmn|{Y}n ) is determined at each step. Determination of this pdf by a sample of measurements {Y}n and its employment with the purpose to obtain the weight estimations is the main peculiarity of the considered adaptive filtering method.

A flowchart of the adaptive filter realizing the described system of equations is shown in Figure 5.10. The adaptive filter consists of (m + 1) Kalman filters connected in parallel and each of them is tuned on one of possible discrete values of parametric disturbance. The resulting estimation of filtered target track parameters is defined as the weight summation of conditional estimations at the outputs of these elementary filters. The weight coefficients P(xm jn|{Y}n ) are made more exact at each step, that is, after each measurement of the coordinate x using the recurrent formula (5.186). Blocks for computation of the correlation matrix Ψn of errors of target track parameter estimation and filter gain Gn are shared for all elementary filters. Therefore, the complexity of realization of the considered adaptive filter takes place owing to (m + 1) multiple computations of extrapolated and smoothed magnitudes of filtered target track parameters and computation of the weights P(xm jn |{Y}n ) at each step of updating the target information.

The adaptive filter design based on the principle of weight of partial estimations can be simplified carrying out the weight of extrapolated values only of filtered target track parameters, and using this weight, to compute the filtered target track parameters by the usual (undivided) filter instead of weighting the output estimations of filtered target track parameters. The equation system of the simplified adaptive filter differs from the previous one by the fact that the extrapolated values of filtered target track parameters determined by (5.180) are averaged with the definite weights. After that we can define more exactly the estimations of filtered target track parameters taking into consideration the nth coordinate measure using the well-known formulas for the Kalman filter.

As follows from Figure 5.12, the adaptive filter allows us to decrease the dynamic error of filtering twice in comparison with the nonadaptive filter even in the case of low-accuracy fragmentation of possible acceleration range. In the case considered in Figure 5.12, these errors do not exceed the variance of errors under coordinating measurement. There is a need to take into consideration that the labor intensiveness of the considered adaptive filter realization by the number of arithmetic operations is twice higher in comparison with the labor intensiveness of the nonadaptive filter realization. With an increase in the number m of discrete values of maneuver acceleration (fragmentation with high accuracy) within the limits of the range (−xmax …xmax ), the labor intensiveness of the considered adaptive filter realization is essentially increased.

A logical flowchart of a possible version of the unified complex radar signal reprocessing algorithm is shown in Figure 5.13. In accordance with this block diagram, each new target pip selected from the buffer after coordinate transformation into the Cartesian coordinate system is subjected to the definite stages of signal processing discussed in this chapter. Under an adopted structure to process new target pips (to form beginnings of tracking target trajectories), a cycle of a single realization of the considered algorithm is the random variable, statistical characteristics of which will be discussed in the next chapters. In conclusion, we note that the discussed flowchart of the unified complex radar signal reprocessing algorithm is not typical, that is, acceptable for all practical situations. This is only an example allowing us to make some calculations during further analysis.

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FIGURE 6.1  Flowchart of radar system.

made at the output of the receiver, so a target is declared to be present when the receiver output exceeds a predetermined threshold. If the threshold is set too low, the receiver noise can cause excessive false alarms. If the threshold is set too high, detections of some targets might be missed that would otherwise have been detected. The criterion to determine the level of the decision threshold is to set the threshold so that it produces an acceptable predetermined average rate of false alarms due to receiver noise.

After the detection decision is made, the track of a target can be determined, where a track is the locus of target locations measured over time. This is an example of data processing. The processed target detection information or its track might be displayed to an operator; or the detection information might be used to automatically direct a missile to a target; or the radar output might be further processed to provide other information about the nature of the target. The radar control ensures that the various parts of radar operate in a coordinated and cooperative manner, as, for example, providing timing signals to various parts of the radar as required.

The radar engineer has a resource time that allows good Doppler processing, bandwidth for good range resolution, space that allows a large antenna, and energy for long-range performance and accurate measurements. External factors affecting radar performance include the target characteristics; external noise that might enter via the antenna; unwanted clutter echoes from land, sea, birds, or rain; interference from other electromagnetic radiators; and propagation effects due to the Earth’s surface and atmosphere. These factors are mentioned here to emphasize that they can be highly important in the design and application of radar systems.

6.1  CONFIGURATION AND FLOWCHART OF RADAR CONTROL SUBSYSTEM

The radar control subsystem is considered as a purposeful change in the structure of a complex radar system (CRS) and, as a consequence, a variation of radar subsystem parameters to reach a maximum effect under radar control. The main tasks of a complex radar control subsystem are to organize an optimal functioning of the radar system as a subsystem in the control subsystem of a higher level. A necessity to control the parameters and structure of a CRS in dynamical mode is caused by the complexity and abruptness of environment conditions, that is, man-made and/