JPS624752B2 - - Google Patents

Description

[Detailed description of the invention]

The present invention relates to a device for predicting the future position of a target for controlling a flying vehicle or a gun. In a defense system such as an anti-aircraft gun that targets a moving object, the position of the target is observed using a radar or the like, and a prediction filter predicts the future position of the target based on the target position data. Thereafter, the firing specifications (firing data such as firing angle) are determined by performing interception calculations based on the flight characteristics of the anti-aircraft gun, etc., and the gun is fired. There has been a strong demand for a firing device for artillery shells and the like that uses such a prediction filter to increase the intercept rate. The present invention was made in view of the above circumstances, and detects the target maneuver situation, selects a prediction filter suitable for this situation, and uses the output of the selected prediction filter for interception calculations to calculate launch specifications. The object of the present invention is to provide a future position prediction device that can improve the target interception rate by firing artillery shells or the like by determining the future position. First, the prediction filter will be explained. As a prediction filter, least squares linear filter, least squares 2
order filter, weighted least squares linear filter, weighted least squares filter, α-β filter, α-
Possible filters include a β-γ filter, a Wiener filter, and a Kalman filter. All of these prediction filters are determined by assuming the target maneuver, setting an appropriate evaluation function, and minimizing (or maximizing) this evaluation function, so they are not universal for all targets. . This will be explained using a weighted least squares linear filter (hereinafter abbreviated as WLS) as an example. Let the coordinate system arbitrarily selected for the defense system be (X, Y, Z), and the target position data obtained using radar etc. be (x〓 ₁ , y〓 ₁ ,
z〓 ₁ )... (x〓n, y〓n, z〓n). Let (x〓n, y〓n, z〓n) mean the latest data, and let the interval between each data be τ. To simplify the explanation, only the X axis will be described below, but the same applies to the Y and Z axes. WLS calculates estimated values _o and _o of the target position and velocity so as to minimize the following equation (1). The predicted future position value x^ _p of the P stage future target is given by the following equation (2). x^ _p = _o + _o (τP) ...(2) When the standard deviation of the target position observation error is σ _p and the standard deviation of the error of the future position prediction value is σ^ _p , σ^ _p ² / σ^ _p ² is called the noise gain,
This refers to fluctuations in predicted values due to radar observation accuracy, and a filter with a small noise gain is a filter with high noise removal ability. The noise gain depends on the number of data items (n). Now, the weighting coefficient w _i of equation (1)
The following equation (3) is adopted as the number of data, and the noise gain counting result when the number of data n=50 is shown in FIG. w _i =k ^Ni (i=0, 1, 2...n-
1) ...(3) 0<k≦1 From Fig. 2, it can be seen that the closer k is to 1, the smaller the noise gain is. In addition to the above-mentioned noise gain, the performance specifications of the filter include responsiveness. For example, consider a target that is turning in a circle at a constant speed, as shown in Figure 3. A filter with good responsiveness predicts the future position direction of the target as the direction α (α″) shown in the dotted line in the figure, but a filter with poor responsiveness predicts the direction of the future position of the target in the direction α (α″) indicated by the dotted line in the figure.
α''), which causes an error.Here, when WLS is applied to a target rotating at 1 G at a speed of 1 Matsuha using the weighting coefficient shown in equation (3), the predicted future position direction of the target ( Figure 4 shows α in Figure 3 and the target velocity vector direction (θ in Figure 3).From Figure 4, it can be seen that the smaller k is, the smaller the filter delay (θ-α) is, and the response As can be seen from Figures 2 and 4, high noise removal ability (small noise gain) and good filter response are related to the The settings are contradictory, and it is impossible to create a filter that has both characteristics.Whether a filter with high noise removal ability or a filter with good response is better as a predictive filter depends on the goal of the defense system. In other words, if the target does not make many maneuvers, a filter with high noise removal ability should be used, and conversely, if the target makes many maneuvers, a filter with good responsiveness should be used. Therefore, in the present invention, a plurality of prediction filters are prepared, the target maneuver is detected in consideration of the properties of the prediction filter, and a prediction filter suitable for the target maneuver is used to improve the accuracy of predicting the future position of the target. The main components of the present invention include a prediction filter for target maneuver detection, a prediction filter for predicting future positions of a plurality of targets, and a filter switching circuit. This shows an example of
Target position data obtained by a radar or the like is guided to a first prediction filter 51, a second prediction filter 52, and a maneuver detection prediction filter 53. The first prediction filter 51 is a filter for predicting the target's future position with high accuracy when the target's maneuver is not too violent (for example, 1G or less), and for example, in the case of the WLS, k = 1.0. corresponds to this. Also, the second prediction filter 52
is when the target maneuver is to a certain extent (e.g.
This is a filter suitable for predicting the future position of a target (at least 1G), such as an α-β-γ filter. Further, the maneuver detection prediction filter 53 needs to be determined as a trade-off between the factors of the responsiveness of the system to the target maneuver and the factors of variations in speed estimation values due to noise gain. For example, consider a case where WLS is adopted as a prediction filter for maneuver detection. The ratio of the square of the standard deviation of the estimated velocity (σ _v ) to the square of the standard deviation of the observed value (σ _p ) σ _v ² /
σ _p ² , the so-called noise gain of the velocity estimate, is shown in FIG. The vertical axis of FIG. 6 indicates τσ _v /σ _i , where τ is the data interval, and the horizontal axis indicates the number of data n. As can be seen from FIG. 6, the noise gain changes with the number of data items n, and the closer the weighting coefficient K is to 1, the smaller it becomes. When the target turns at a constant speed of 1G, use WLS to calculate the square value of the difference between the estimated value of the current velocity vector and the estimated value of the velocity vector 2 seconds ago.

Calculate [Formula] and calculate ΔV and target rotation. times time

The relationship with [Formula] is shown in FIG. It can be seen from FIG. 7 that k=0.9 has better responsiveness, but FIG. 6 shows that k=0.95 is better for noise gain. By the way, ΔV is a value corresponding to the turning G of the target, that is, the maneuver of the target. If the target is going straight, ΔV = 0, and if the target is turning 1 G, ignoring the delay of the prediction filter, the 8th It can be determined from the relationship shown in the figure. In Fig. 8, if the current target position is A, B is the target position T seconds ago, O is the target turning center, R is the turning radius,
V ₁ and V ₂ are estimated velocities at target positions A and B, θ
is the target turning angle. Therefore ΔV is the velocity
It is given as the difference vector between the V ₁ vector and the velocity V ₂ vector, and can be obtained from the following equation. R=V ² /G θ=VT/R ΔV≒|V ₁ |・θ=GT In other words, there is a one-to-one correspondence between ΔV and target turning G, and for the sake of the following explanation, ΔV and G will not be distinguished. . On the other hand, in FIG. 5, the switching circuit 54 uses the turning G obtained from the maneuver detection predictive filter 53.
Information (ΔV) ² selects the prediction filter used for interception calculation. That is, the output of the first prediction filter 51 is selected for 1G or less, and the output of the second prediction filter 52 is selected for 1G or more. A highly accurate target future position can be obtained in the calculation device 55 from the optimal predicted value output selected in this way.

[Brief explanation of the drawing]

Fig. 1 is a diagram shown to explain the X-axis component of the target position data on the coordinate system, Fig. 2 is a diagram showing the noise gain characteristics in a weighted least squares linear filter, and Fig. 3 is a diagram showing the target position data. A diagram showing the relationship with the response of the prediction filter, Figure 4 shows the weighted minimum 2
FIG. 5 is a block diagram showing an embodiment of the future position prediction device according to the present invention. FIG. 6 is a diagram showing the response of the first-order filter for maneuver detection. Diagram showing the noise gain characteristics when using the following filter, 7th
The figure is a characteristic diagram showing the relationship between the turning time and the speed vector difference during the target turning, and FIGS. 8a and 8b are diagrams shown to explain the speed vector and the speed vector difference. 51...First prediction filter, 52...Second prediction filter, 53...Prediction filter for maneuver detection, 54...Switching circuit, 55...Interceptor calculation device.

Claims (1)

[Claims] 1. A first prediction filter with good noise gain, a second prediction filter with good responsiveness, a target maneuver detection prediction filter that detects the maneuver of a moving target to be intercepted, and a and means for selecting a prediction filter suitable for the maneuver situation from among the first and second prediction filters, and calculation means for calculating the future position of the target using the prediction filter output by this means. Future position prediction device.