Intelligent Target Design Based on Complex Target Simulation

Abstract

The emergence and popularization of various fifth-generation fighter jets with supersonic cruise, super maneuverability, and stealth functionalities have raised higher and more comprehensive challenges for the tactical performance and operational indicators of air defense weapon systems. The training of air defense systems requires simulated targets; however, the traditional targets cannot simulate the radar cross-section (RCS) distribution characteristics of fifth-generation fighter aircrafts. In addition, the existing target aircrafts are expensive and cannot be mass-produced. Therefore, in this paper, a corner reflector and a Luneburg ball reflector with RCS distribution characteristics of a fifth-generation fighter in a certain spatial area are designed for target simulation. Several corner reflectors and Luneburg balls are used to form an array to realize the simulations. The RCS value and distribution characteristics of the target can be combined with fuzzy clustering and a single-chip microcomputer to design an intelligent switching system, which improves the practicability of the intelligent target design proposed in this paper.

1. Introduction

International warfare has gradually developed from a backward pure weapon combat method to a systematic and information-based intelligent combat system. Therefore, it is very important in this era to improve a country’s scientific and technological levels and strengths relating to national defense. In modern radar electronic warfare, the position and role of a radar in electronic communication warfare are particularly prominent. The radar technology has seen very rapid development until now. A variety of anti-jamming and anti-radiation missile technologies are applied in modern radar systems. This is a very beneficial development for the radar system itself. However, it is a huge threat for military operations and national defense security. At the same time, new radar systems and technologies have emerged and developed rapidly, increasing the prominence of the role of passive interference in information electronic warfare.

Passive jamming [1] is an important field in the current anti-radar technology and it is widely used. The passive reflectors, which are often called the passive echo enhancers, are important simulated targets in military exercises. Due to the high cost of military equipment, passive radar targets are often used [2] to simulate the radar cross-section (RCS) of actual objects or equipment as well as their maneuvering characteristics in order to meet the needs of daily military training.

A passive radar target mainly refers to the target or device with specific requirements of RCS spatial distribution and maneuvering characteristics, such that the incident radar wave is concentrated within a certain spatial angle range and reflected back according to the direction of the incident radar wave. At present, the highest reflection is used for scaling. The structure of the reflector used for the passive radar target can include a ball, Luneburg ball, trihedral corner reflector, or their combination.

Passive false targets made with trihedral reflectors can serve as low-cost simulated targets during self-training [3,4] for sensitivity testing of radar systems. Trihedral reflectors are used as camouflage [5], whereby the actual equipment being simulated is covered with a bigger RCS.

A Luneburg ball is also called a Luneburg lens [6,7,8,9]. Its outstanding features include a high RCS value and a wide coverage angle of secondary radiation direction. It is mostly used as stealth material against radars in military fighter jets or military ships. For example, the American F-22 and F-35 stealth fighters use Luneburg balls as radar scatterers. The University of Electronic Science and Technology of China [10] carried out related optimization research on the layered design of Luneburg balls for Ku, Ka, and other frequency bands. The National University of Defense Technology [11] designed and optimized a compressed all-dielectric plane Luneburg lens antenna using optical transformation and analyzed its multi-beam performance.

The existing local and international application technology for simulated targets is relatively mature. Usually, a single corner reflector or an array of multiple corner reflectors is used for simulation. It is sufficient to have strong reflection states at different angles, but corner reflector is only used to simulate objects with a relatively simple structure. Objects with relatively simple shapes are used to simulate the RCS spatial distribution characteristics of the discovery direction, as carried out in [12]. The radar station antenna simulates its radiation pattern [13]; however, the RCS value of a single angle inversion can simulate the RCS of the radar antenna station. Nevertheless, the RCS of the radar antenna station distribution characteristics is considerably larger than the main beam range of the radar antenna station.

Detection and identification techniques tend to focus on saliency, i.e., global rarity or local contrast. Alternative approaches to investigate the internal structure of objects using wideband acoustics [14,15] have shown some promise, but their performance does not enable rapid and effective detection of the target, and false alarm rates remain prohibitively high. The question of resolution has been raised again by the advent of very high-resolution side scan, forward-look, and Synthetic Aperture Radar (SAR) systems [16]. However, these systems require more data for calculations and the existing computer configuration cannot meet the requirements. Therefore, this paper uses the angle reflector and Luneburg ball to conduct array simulation, which overcomes the aforementioned problem.

A target to be simulated not only needs to meet the RCS value requirement, but also needs to satisfy the distribution characteristics of the corresponding angle of the simulated target. For most of the currently available targets, the inability to simulate the corresponding RCS distribution characteristics is a key problem that needs to be solved urgently.

A smart target design based on complex target simulation is proposed in this paper. This design is combined with a UAV by designing a corner reflector device that simulates the distribution characteristics with a sharp RCS, and a Luneburg ball reflector device that simulates the distribution characteristics of RCS in a stationary space. The simulation of a certain type of fighter aircraft can be realized as needed, such as F-22, F-35, J-20, and other fifth-generation fighter aircrafts. The intelligent expansion or combination of an angle reflector and a Luneburg ball is realized by Microcontroller Unit (MCU) or Field Programmable Gate Array (FPGA), and the corresponding simulation requirements are completed.

The rest of this paper is organized as follows: Section 2 introduces the principle of angle reflector and Luneburg ball reflector. Section 3 presents the design scheme and simulation verification of the angle reflector and Luneburg ball. Section 4 introduces the experimental verification of a fighter according to the RCS distribution characteristics. The paper is concluded in Section 5.

2. Design Principle

2.1. Radar Cross-Sectional Area

The radar cross-section (RCS) means that the radar emits a series of electromagnetic waves, which will generate a reflected echo signal when irradiated on the surface of a target object. The radar obtains the signal characteristics of the target after filtering and discriminating the signal. Under the far-field and high-frequency conditions, the general calculation expression of the field scattered by the target object’s surface is given as follows based on the Stratton–Chu integral equation:

$$E^s(r)=\frac{jk}{4\pi }\frac{e^{-jkr}}{r}{\displaystyle \underset{\mathrm{\Omega }}{\int }\stackrel{\Lambda }{s}}(M_s(r^{\prime })+Z_0\stackrel{\Lambda }{s}{J}_s(r^{\prime })\exp (jk{r}^{\prime }(\stackrel{\Lambda }{s}-\stackrel{\Lambda }{i}))d\omega$$

(1)

In (1), $\stackrel{\Lambda }{i}$ and $\stackrel{\Lambda }{s}$ are the unit vectors in the incident and receiving directions, respectively, $Z_0$ is the free space wave impedance, $\mathrm{\Omega }$ is the cross-sectional area of the wave incident on the surface of the object, and $J_s(r^{\prime })$ and $M_s(r^{\prime })$ are the current and magnetic current vectors, respectively.

According to (1), the frequency response $H(\omega )$ can be obtained independent of the radar incident frequency signal. Subsequently, the radar incident signal $S(t)$ is processed by Fourier transform to obtain the incident signal frequency response $S(\omega )$. The target echo scattering can be obtained by multiplying $H(\omega )$ and $S(\omega )$ to obtain the cross-sectional area $Y(\omega )$. The inverse Fourier transform (IFFT) of $Y(\omega )$ is used to obtain the target time domain echo $Y(t)$.

2.2. Corner Reflector

The corner reflector is generally composed of two or three metal plates that are orthogonally combined with one another. The trihedral angle has three anti-planes, which form a strong triple internal reflection. This provides a wide scattering pattern in the three-dimensional space.

The RCS value of the corner reflector is obtained by comparing with the calibration body as follows:

$$\sigma =4\pi r^2\frac{{\left|E_{\varphi }^S(\mathrm{a})\right|}^2}{{\left|E_i^S(R)\right|}^2}$$

(2)

where $\sigma$ is the RCS value, a is the side length of the right angle reflector, $R$ is the side length of the size of the calibration body, $E_{\varphi }^S$ is the radar cross-sectional area of the corner reflector, and $E_i^S$ is the radar cross-sectional area of the calibration body.

Equation (2) shows that if the angle between the two metal plates of the corner reflector is decreased, the vector difference between $\stackrel{\Lambda }{i}$ and $\stackrel{\Lambda }{s}$ will be reduced accordingly. Consequently, the stable space angle range corresponding to the corner reflector will be reduced. The RCS value will also decrease accordingly, which can be more in line with the sharp-angle RCS distribution characteristics of the target to be simulated.

2.3. Luneburg Ball Reflector

A Luneburg ball reflector (Luneburg lens) is a passive reflector made by coating a layer of metal reflector on the surface of a Luneburg ball, based on the principle of reflection and refraction of light in an uneven medium. The ideal Luneburg ball reflector is a mirror-symmetric spherical structure whose permittivity gradually changes along the ball radius. The permittivity gradually changes from 2 in the center to 1 on the surface of the Luneburg ball. The relative permittivity of the outermost medium of the reflector is the same or close to that of air [17].

The Luneburg ball refractive index $n$ is a function of distance $r$ from the center of the ball to a point on the surface of the ball. It can be written as follows:

$$n(r)=\sqrt{2-{(\frac{r}{R})}^2}$$

(3)

where $r$ represents the distance from the center of the ball to a point on the outer surface of the dielectric ball, and R represents the radius of the Luneburg ball.

The dielectric constant of each layer of the dielectric ball is

$$\epsilon (r)=n{(r)}^2=2-{(\frac{r}{R})}^2$$

(4)

However, it is not possible to actually find materials that have a continuous dielectric constant gradient. Due to the unavailability of required materials, several layers of materials with different dielectric constants are usually used in actual production to simulate the effect of a dielectric constant with a continuous gradient, which not only reduces the processing difficulty but can also achieve the basic performance of a Luneburg Ball.

3. Simulation Design

First, we design the corner reflector, as shown in Figure 1. The corner reflector model considers the X, Y, and Z axes of the coordinate system as three right-angle sides. The positive direction of the X axis corresponds to the horizontal angle of 0°, the positive direction of the Y axis corresponds to the vertical angle of 90°, and the positive direction of the Z axis corresponds to the pitching angle of 0°, where three metal plates are set as the ideal metal conductor surfaces with a side length of 0.5 m. A far-field plane wave with a frequency of 24 GHz is used for irradiating the corner reflector. The scanning angle range is −90°~90° in the azimuth dimension, the pitching angle is 55°, and the angle scanning interval is 1°. Figure 2 shows the simulation results.

As Figure 2 shows, the maximum value of the RCS of the corner reflector is 25 dBsm, which is consistent with the theoretically calculated value given by (4). Figure 3 shows a change in the angle between the two sides to 60°, and the corresponding simulation results are shown in Figure 4.

Figure 4 shows that the 10 dB fluctuation range of the horizontal azimuth plane RCS value of the corner reflector is about 6° between 27° and 33°. The 8 dB fluctuation range of the elevation azimuth plane RCS value is between 29° and 83°. The 10 dB fluctuation range of the RCS value in the pitch and azimuth plane is about 61° between 24° and 85°. This kind of fluctuation range can be used to simulate the RCS distribution characteristics in the sharp angular range, i.e., within a ±15° range. Other parts are shielded with absorbent materials.

Next, we design the Luneburg ball using FEKO. The Luneburg ball is irradiated by a far-field plane wave with a frequency of 24 GHz. The scanning angle ranges are 0° in the azimuth dimension and −90°~90° in the elevation dimension, and the angular scanning interval is 0.1°, as shown in Figure 5.

The Luneburg ball simulation structure consists of three dielectric layers and an outermost air layer. Equation (4) can be used to calculate the relative permittivity of the dielectric layer.

Table 1. Design of the dielectric constant of each layer of a Luneburg ball.

Layers	Medium Reflectance	Relative Permittivity
1	1.4	1.96
2	1.34	1.7956
3	1.22	1.4884
4	1	1

shows the specific design parameters.

Figure 6 shows the simulation results obtained after setting the relevant parameters of the Luneburg ball according to Table 1.

As Figure 6 shows, the maximum RCS value of the Luneburg ball is about 0 dBsm, and the stable spatial angular range of the RCS is greater than or equal to 110°. A change in the size of the Luneburg ball can vary the corresponding maximum RCS value if other simulation conditions and parameters are consistent. The width of the coverage angle in the secondary scattering direction can be changed by varying the size and position of the metal reflector. Therefore, the RCS distribution characteristics of the target stationary fluctuation range can be satisfied by changing the size of the Luneburg ball and appropriately designing the metal reflector wave material.

In this way, based on the RCS distribution characteristics of the simulated target, a limited number of corner reflectors and Luneburg ball reflectors can be selected to form an array to simulate the corresponding target.

4. Experimental Verification

Figure 7 shows the RCS distribution characteristics of a certain type of a fighter aircraft.

As Figure 7 shows, there are four peaks in the vicinity of −90°, 0°, 90°, and 180°, out of which 0° corresponds to the position of the nose, −180° corresponds to the position of the tail, and −90° and 90° are directly opposite to the position of the wings. Therefore, four angle reflectors can be used to simulate the distribution characteristics of the corresponding sharp RCS. There are four stationary RCS distribution characteristics in the middle of the four peaks, whose fluctuation range is not more than 3 dB. The corresponding stationary RCS distribution characteristics can be simulated using four Luneburg balls. This means that four corner reflectors and four Luneburg ball reflectors can be used to form an array, where the included angle of the corner reflector near 0° is controlled at about 20–30°. Figure 8 shows the test results.

Figure 8 shows that a peak appears near 0°, and its RCS value is roughly the same as that of Figure 7. The peaks corresponding to −90° and 90° are also simulated accordingly. Therefore, the target design of the complex target simulation proposed in this paper meets the requirements. However, it can only correspond to the simulation of one target. Therefore, the simulated targets are added into a data set and classified using fuzzy clustering or deep learning algorithms. Figure 9 shows an example RCS of a target simulated in this paper.

As Figure 9 shows, the RCS distribution characteristics of an F-22 fighter jet contain six spikes. Therefore, it can also be simulated with six angle reflectors and six Luneburg balls reflectors.

5. Conclusions

The continuous improvement of scientific and technological means of military warfare has made it particularly important to design and simulate various targets for assessing the performance of ground air defense systems. An intelligent target design based on complex target simulation was proposed in this paper to accurately simulate various types of targets. The main conclusions are as follows:

• The corner reflector could simulate the RCS distribution characteristics of the target sharp angle by changing the size and angle between the two metal plates on both its sides.
• The Luneburg ball reflector could simulate the RCS distribution characteristics of the target stationary space angle by changing the size and position of metal reflectors.
• The classification and intelligent shifting of several models using the fuzzy clustering algorithm and single-chip microcomputer is the focus of future research on intelligent target design for complex target simulation.
• In future research, a certain type of aircraft will be simulated using the variable structure angle reflector and its array form, as well as the Luneburg ball reflector. However, it is necessary to further consider how to construct the hybrid array of the variable angle reflector and Luneburg balls and to predict and evaluate the accuracy of the simulated target. At the same time, the distribution characteristics of the RCS simulating a certain range of pitch angles should be considered for accurate radar target detection, and an error analysis should be made.