# Rejection of Surface Clutter in Far Sidelobes for Airborne Radars

## Article information

## Abstract

In airborne radar, false alarms caused by surface clutter returns through antenna sidelobes need to be suppressed to detect the target of interest. Sidelobe blanking is widely used to reject false alarms. Ideally, an auxiliary antenna radiation pattern should cover entire sidelobes of the main antenna radiation pattern. However, this may not always be satisfied, especially in the case of far sidelobes (FSL) of the main antenna, for various reasons. In this work, rejection techniques for false alarms caused by surface clutter returns through FSL are proposed. The distortion of the measured angle and velocity of the surface clutter are explained, and a measurement process for estimating the true location and velocity of the clutter is derived. Subsequently, two techniques for false alarm rejection are presented. The first technique is based on the look-up table (LUT) of FSL, which is robust but not practical due to the time and cost required to build a LUT with sufficient resolution. In the second technique, false alarms are rejected when they satisfy predefined criteria derived from their common characteristics, and flight test results confirm that false alarms can be effectively rejected by applying the second technique.

**Keywords:**Airborne Radar; Electronically Scanned Array Antenna; False Alarm Rejection; Sidelobe Blanking; Surface Clutter

## I. Introduction

In airborne radar, it is important to suppress interferences, such as surface clutter returns that enter the radar through antenna sidelobes, to detect the targets of interest. Sidelobe blanking (SLB) is a traditional technique that is widely used to reject interferences entering through sidelobes. It utilizes an auxiliary antenna along with a main antenna [1–4]. Fig. 1 illustrates a typical SLB system [1]. For SLB, the gain of the auxiliary antenna should be sufficiently lower than that of the main antenna but high enough to cover the sidelobes of the main antenna, as shown in Fig. 1(a).

The received signal level of the main channel through the main antenna am is compared with that of the guard channel through the auxiliary antenna *a** _{g}*. If the ratio of the two signal levels is higher than a pre-determined threshold

*F*—i.e.,

*a*

*/*

_{g}*a*

*>*

_{m}*F*—it is assumed that the main channel signal has been received from the sidelobes of the main antenna and, therefore, is blanked, as shown in Fig. 1(b). Otherwise, the main channel output signal is conventionally processed for target detection.

The literature has proposed several effective methods for suppressing interferences, such as sidelobe canceling (SLC) or adaptive beamforming (ABF) [5, 6]. However, SLC requires extra auxiliary arrays, and ABF can only be utilized with a main array antenna consisting of elements that are fully equipped with individual receiver modules, such as active electronically scanned array (AESA) [7]. In contrast, SLB has been widely adopted in practical radar systems due to its simple hardware configuration and blanking logic.

Although the concept and implementation of SLB is simple, achieving an auxiliary antenna radiation pattern that fully covers the sidelobes of the main antenna in the entire angular region of interest is not an easy task [8, 9]. In particular, far sidelobes (FSL) from the main lobe of the main antenna are difficult for auxiliary antenna radiation patterns to cover [10] (as illustrated in Fig. 1(a)), and can lead to false alarms (which refer to the detection of false targets, such as noise or clutter) caused by interferences, such as surface clutter returns. In airborne radar, the antennas are covered by a radome, which contributes to increasing the sidelobe levels of the main antenna, further degrading the SLB performance. Moreover, if the main antenna is an electronically scanned array antenna (either passive or active), the failure of the array elements can increase its sidelobe level.

If the interference received through sidelobes is not blanked by SLB, the radar will not be able to identify false detections due to interference from normal target detection. To address this issue, this work proposes a practical method for reinforcing the false alarm rejection performance of SLB without any change in hardware. This means that the proposed method can easily be implemented in any existing airborne radar with an SLB system. As will be shown in Section II, the location of the interference deviates from its true value due to incorrect angular measurement, and the velocity (or Doppler frequency) shifts from the true value due to incorrect compensation for the radar platform radial velocity. In Section III, estimation methods for the true location and velocity of the interference caused by surface clutter returns are proposed. Finally, in Section IV, the rejection criteria of FSL false alarms are presented by analyzing the characteristics.

## II. Estimation of True Location and Velocity of Surface Clutter Returns through FSL

Generally, a radar is able to distinguish whether its detection is caused by the target in the main lobe or by interference through sidelobes by SLB. However, if the sidelobe level of the main antenna in a certain angular direction is higher than that of the auxiliary antenna, this detection is considered as a detection in the mainlobe, leading to a false alarm. In such a case, the measured angles (azimuth and elevation) and velocity deviate from the true angles and velocity.

### 1. Angular Measurement Error

In airborne radar, the target angle is commonly measured through monopulse processing [11]. The following example illustrates how the angular measurement is distorted due to FSL detection. A linear array antenna composed of *N* isotropic elements with element spacing *d* is considered. The target angle of the antenna can be measured by comparing the outputs of the sum (∑) and difference (Δ) channels. Notably, the ∑ channel output *s*_{∑} is the summation of the received signals from the total *N* elements, while the Δ channel output *s*_{Δ} is the difference between the summation of the received signals from one-half of the *N* elements and that from the other half of the *N* elements. Fig. 2(a) shows the *s*_{∑} and *s*_{Δ} with respect to the measured angle *θ* when *N* = 30 and *d* is half wavelength. Note that both values are normalized to the peak voltage of *s*_{∑}. The monopulse ratio (the ratio between *s*_{Δ} and *s*_{∑}, which is ideally a real number) within the half-power beamwidth of 3.4° is shown in Fig. 2(b). In conventional monopulse processing, when detection occurs, the monopulse ratio is calculated from the received signal and then compared with the curve in Fig. 2(b). For instance, supposing that the calculated monopulse ratio is 0.15, the radar processor decides that the measured monopulse angle is 0.36°, which is the angle marked with the “x” symbol in Fig. 2(b). This process assumes that the echo signal has been received through the mainlobe of the antenna. However, supposing that the signal is actually received through the FSL and not the mainlobe—for instance, at a 70° offset from the mainlobe peak direction—the monopulse ratio at the angle would be 0.15 (the same as that of 0.36°), which is marked with an “o” symbol in Fig. 2(c), the monopulse ratio for the entire angular range from −90° to +90°. However, since this process assumes that the echo signal has been received through the mainlobe of the antenna, the measured monopulse angle will still be the angle corresponding to the “x” symbol marked in Fig. 2(b) and 2(c), which is 0.36°, resulting in an angular measurement error *θ** _{err}* = 69.64°.

### 2. Velocity Measurement Error

The target velocity is measured using Doppler frequency. In airborne radar, the Doppler frequency is measured from the movements of both the target and the radar platform. Therefore, the radar platform velocity must be compensated to find the velocity of the target itself. Supposing that the radar platform is heading north with a constant altitude with velocity *v** _{r}*, and a target at angle

*θ*

*is approaching the radar with aspect angle*

_{t}*θ*

*and velocity*

_{asp}*v*

*, as shown in Fig. 3, the measured Doppler frequency*

_{t}*f*

*can be expressed as follows:*

_{d}From Eq. (1), the radial velocity of the target *v*_{t}_{,}* _{r}* can be calculated using the following equation:

The second term on the right-hand side of Eq. (2) compensates for the radar platform velocity, assuming that the target echo signal is received through the mainlobe of the antenna. However, if the signal is received through the FSL at *θ** _{fsl}*, the true radial velocity of the target would be as follows:

From Eqs. (2) and (3), the velocity measurement error *v** _{err}* can be obtained as follows:

### 3. Estimation of True Location and Velocity

In the previous sections, angular and velocity measurement errors resulting from interference caused by FSL were analyzed. In this section, the estimation process for the true location (based on angle) and velocity of the surface clutter in the FSL direction is described.

Although the angle and velocity of the interference caused by FSL are distorted, the range remains unaffected because it is measured by the time delay of the transmitted pulses. Suppose that the radar platform is flying with velocity *v** _{r}* at altitude

*h*

*, the radar beam is steered to*

_{r}*θ*with respect to the platform heading, as shown in Fig. 4. In the case of SLB failure, false alarms may occur due to the surface clutter return through FSL. If the detected range is

*r*

*, the elevation angle of the clutter in space-stabilized coordinate*

_{m}Furthermore, if the measured radial velocity and the azimuth and elevation angles are *v** _{m}*,

*θ*

_{m}_{,}

*, and*

_{az}*θ*

_{m}_{,}

*, respectively, the azimuth angle of the clutter in space-stabilized coordinate*

_{el}where *v*_{r}_{,}* _{x}* and

*v*

_{r}_{,}

*are the velocities of the radar platform in the*

_{y}*x*-axis and

*y*-axis, respectively. From (

*r*

*,*

_{m}Finally, the radial velocity of clutter *v*_{c}_{,}* _{r}* can be estimated from Eq. (4) as follows:

## III. Flight Test Results

In this section, false alarms that occurred during a real radar flight test are investigated, and the location and velocity of the surface clutter (which is the source of the false alarms) are estimated based on the process described in the previous section.

The flight test was performed over the South Sea near Geoje Island, Republic of Korea. The partial flight trajectory of the radar platform is shown in Fig. 5(a), where the platform is heading toward the northeast direction. The false alarms occurred during the flight test were caused by surface clutters in the dockyard and in a densely populated area on Geoje Island (marked with a dashed-line box). Fig. 5 shows the actual measurements of the radar (which are false alarms) and the estimated true values. The measured location (latitude and longitude), angles (azimuth and elevation) of the antenna coordinate, range, and velocity of the false alarms are shown using an “x” symbol in Fig. 5. These measured values are not the actual values of the sources of the detection (which are the surface clutters) but false values resulting from the angular measurement error and velocity measurement error described in the previous sections. Although there was no aircraft or any other obstacle in the measured location during the flight test, detection occurred continuously for approximately 3.5 seconds at angles close to

From these measurement results, the true clutter location, azimuth and elevation angles, and velocity were estimated using the procedure described in the previous section. The results are shown using the “o” symbol in Fig. 5. It is observed that the estimated true location of the clutter coincides with the dockyard and the densely populated area on Geoje Island, as shown in Fig. 5(a). Note that as the radar platform moves toward the northeast direction, the measured location varies accordingly, but the estimated location remains almost fixed at the clutter location.

Fig. 5(b) shows that the estimated true azimuth and elevation angles (

Furthermore, while the measured velocity is approximately −80 m/s, the estimated true velocity is almost 0 m/s, which coincides with the fact that the sources of the false alarms are surface clutters.

## IV. Rejection of False Alarms due to Surface Clutter Returns through FSL

As shown in the previous section, airborne radars may encounter false alarms due to surface clutter returns through FSLs. In this section, techniques for rejecting these false alarms are discussed.

### 1. Rejection by Referring to the FSL Look-Up Table

One of the most effective techniques for rejecting false alarms arising from surface clutter returns through FSLs is to refer to the look-up table (LUT) of FSL angles corresponding to the beam steering angles.

The LUT comprises a list of beam steering angles (both azimuth and elevation) within a beam steering limit and a list of FSL angles (both azimuth and elevation) corresponding to each beam steering angle that is not covered by the auxiliary antenna radiation pattern. When detection occurs, the FSL angles can be estimated using Eqs. (5) and (6). Since these angles are in space-stabilized coordinate, they can be transformed into antenna coordinate and then compared with the FSL angles in the LUT. If the estimated FSL angles are within the angular regions in which the gain of the FSLs of the LUT is higher than that of the auxiliary antenna, the detection is considered as a false alarm and therefore rejected.

In Fig. 6, an example of applying the LUT to reject interference from the FSL is presented. The black dots correspond to the list of beam-steering angles in a LUT. In this example, the resolution of the beam steering angles is assumed to be 5°, meaning that the LUT was built by measuring the FSLs while changing the beam steering angles with a 5° step (note that the black dots exist only within the beam steering angle limit, indicated using a solid line in Fig. 6. A smaller resolution value can be chosen to improve the precision of the LUT. However, as the resolution value decreases, the time and cost required to build the LUT increase).

Suppose that a detection occurs when the radar radiates beams at a steering angle of (*θ*_{s}_{,}* _{az}*,

*θ*

_{s}_{,}

*) = (5°, 13°), and the estimated FSL angle is (*

_{el}*θ*

_{fsl}_{,}

*,*

_{az}*θ*

_{fsl}_{,}

*) = (−78°, −50°), which are marked using red “+” and “o” symbols in Fig. 6, respectively. From the list of beam steering angles in the LUT, the value nearest to the actual beam steering angle (*

_{el}*θ*

_{s}_{,}

*,*

_{az}*θ*

_{s}_{,}

*) is*

_{el}A drawback of rejecting false alarms using the LUT is the time and cost involved in building the LUT. To build a LUT for FSL angles, radiation patterns of the main antenna need to be measured with full angular coverage (up to 90° from the antenna broadside). Since radars usually adopt planar array antennas with high directivity as their main antenna, radiation pattern measurement would require a near-field measurement system, which usually takes a considerable amount of time to measure the radiation pattern at a single beam steering angle to ensure full angular coverage. This measurement needs to be repeated for all beam steering angles within the beam steering limit for each frequency within the operational frequency band of the radar. Moreover, although finer resolutions of beam steering angles and frequency lead to more precise construction of the LUT, the time and cost required for the measurement increase.

### 2. Rejection based on False Alarm Characteristics

In this section, an alternative technique for false alarm rejection is discussed for cases in which LUTs with sufficient angular and frequency resolutions are not available. This alternative approach is based on the characteristics of false alarms caused by surface clutter returns through FSL. Specifically, in this technique, false alarms are rejected if they satisfy a number of predefined criteria derived from their common characteristics.

The first criterion is that the measured range *r** _{m}* should be longer than or the same as the radar platform altitude

*h*

*.*

_{r}If this criterion is not satisfied, the estimated elevation angle in the space-stabilized coordinate

The second criterion is that the measured velocity *v** _{m}* should be lower than the velocity threshold

*v*

*.*

_{thr}If a detection is actually a false alarm caused by surface clutter returns through the FSL, the estimated clutter velocity *v*_{c}_{,}* _{r}* in Eq. (7) should ideally be 0. The second term on the right-hand side in Eq. (7) is usually larger than the third term, since the measured angles (

*θ*

_{m}_{,}

*,*

_{az}*θ*

_{m}_{,}

*) correspond to the beam steering angles within the beam steering limit, but the estimated angles (*

_{el}*v*

*. Therefore, the second criterion prevents the possibility of rejecting real targets with nose-aspect angles, which are more critical threats to fighters than targets with tail-aspect angles.*

_{m}Despite the first and second criteria, the possibility of rejecting real targets with tail-aspect angles at mid- and long-ranges remains. Unfortunately, it is difficult to perfectly sort out false alarms caused by real tail-aspect angle targets. However, by applying the third criterion, the probability of rejecting real tail-aspect angle targets is reduced. The third criterion is that the difference between the signal power level of ∑ channel *p*_{∑} and Δ channel *p*_{Δ}, which is *p*_{∑Δ}, should be less than the threshold *p** _{thr}*.

The third criterion is based on the fact that the gain of the ∑ channel pattern is lower than that of the Δ channel pattern for most FSLs. This can be illustrated using the example of a planar array antenna with radiation patterns of the ∑, Δ* _{az}*, and Δ

*channels, as shown in Fig. 7(a), 7(b), and 7(c), respectively (note that the planar array antenna considered here is a hypothetical one—it is not the real array antenna used in the flight test in Section III. The radiation patterns of the real array antenna are not provided for security reasons). The pattern levels are normalized to the peak value of the ∑ channel pattern and presented in dB scale. Fig. 7(d) shows the angular regions where*

_{el}*p*

_{∑Δ}is higher than

*p*

*= 5 dB. The red-, blue-, and violet-colored regions represent the areas in which the third criterion is met for the Δ*

_{thr}*channel, Δ*

_{az}*channel, and both channels, respectively.*

_{el}*p*

_{∑Δ}is calculated only for the angles outside the beam steering limit corresponding to a 60° cone angle with respect to the broadside of the antenna, since the sidelobes of the main antenna within the cone angle can be covered by the radiation pattern of the auxiliary antenna. Also, since the probability of detection at low levels of the ∑ channel radiation pattern is extremely low,

*p*

_{∑Δ}is calculated only for the angles where the normalized ∑ channel radiation pattern level is higher than −60 dB.

The value of *p** _{thr}* should be decided considering the trade-off between two probabilities: the probability of failing to reject the false alarms caused by FSL (i.e., the probability that the third criterion is not satisfied within the FSL region) and the probability of failing to detect the real target in the mainlobe (i.e., the probability that the third criterion is satisfied within the mainlobe region). The first probability

*P*

_{fail}_{,}

*can be estimated from the ratio of the angles that do not satisfy the third criterion to the total angles outside the beam steering limit. Similarly, the second probability*

_{FSL}*P*

_{fail}_{,}

*can be estimated through the ratio of the angles that satisfy the third criterion to the total angles within the mainlobe beamwidth. Note that the value of*

_{ML}*P*

_{fail}_{,}

*(or*

_{FSL}*P*

_{fail}_{,}

*) for the Δ*

_{ML}*channel will not be the same as that of*

_{az}*P*

_{fail}_{,}

*(or*

_{FSL}*P*

_{fail}_{,}

*) for the Δ*

_{ML}*channel. Fig. 8 shows the values of*

_{el}*P*

_{fail}_{,}

*and*

_{FSL}*P*

_{fail}_{,}

*for the Δ*

_{ML}*and Δ*

_{az}*channels in terms of*

_{el}*p*

_{thr}. It is observed that with an increase in

*p*

*,*

_{thr}*P*

_{fail}_{,}

*decreases but*

_{FSL}*P*

_{fail}_{,}

*increases. When*

_{ML}*p*

*= 5 dB, the values of*

_{thr}*P*

_{fail}_{,}

*are 5.30% for the Δ*

_{FSL}*channel and 4.58% for the Δ*

_{az}*channel. This means that the probability of failing to reject false alarms caused by FSL when the third criterion is applied is less than 6%, which is sufficiently low. Meanwhile, the values of*

_{el}*P*

_{fail}_{,}

*are 14.03% for the Δ*

_{ML}*channel and 12.39% for the Δ*

_{az}*channel, implying that the probability of rejecting real targets within the beamwidth is more than 12%.*

_{el}To mitigate the risk of rejecting real targets within the beamwidth and to enhance the probability of rejecting false alarms caused by surface clutter returns through FSL, the *M-*out-of-*N* criterion is applied—the detection is rejected only if all the criteria are satisfied for at least *M* detections in *N* continuous detections. The characteristics that allow improvements in the detection of real targets and false alarms by FSL by applying the *M*-out-of-*N* criterion are noted below.

If detections originate from a real target with a sufficient signal-to-noise ratio, their measured angle will converge to the true angle of the target as detections are continued through tracking [13]. In other words, the measured monopulse angle *θ** _{mon}* will converge to zero as detections continue.

*θ*

*for the real target during the flight test (not shown in Fig. 5 for brevity and security reasons) is presented in Fig. 9(a), where it is observed that*

_{mon}*θ*

*converges to zero as detections continue. Thus,*

_{mon}*p*

_{∑Δ}becomes higher than

*p*

*and converges to a certain value (corresponding to the peak gain of ∑ channel and the null depth of Δ channel). In Fig. 9(b),*

_{thr}*p*

_{∑Δ}for a real target during the flight test is presented, showing that it becomes higher than

*p*

*= 5 dB and converges to approximately 30 dB. However, occasionally,*

_{thr}*p*

_{∑Δ}is lower than

*p*

*. For various reasons, such as fluctuations of the target radar cross-section [14], interference signals, or radar hardware degradation,*

_{thr}*θ*

*can be increased, and consequently,*

_{mon}*p*

_{∑Δ}is reduced, as indicated by the detections marked in red dashed circles in Fig. 9(a) and 9(b). Without the

*M*-out-of-

*N*criterion, these detections would have been rejected on applying the third criterion, even though they are not false alarms caused by FSL.

In contrast, if the detection originates from interference through FSL, both *θ** _{mon}* and

*p*

_{∑Δ}will continuously fluctuate and not converge. These phenomena were observed for the false alarms discussed in Section III, as shown in Fig. 9(c) and 9(d). Note that

*p*

_{∑Δ}of the Δ

*channel is mostly lower than that of the Δ*

_{az}*channel since the azimuth angles of FSL angles are larger than those of the elevation angle, as shown in Fig. 5(b). It is observed that*

_{el}*p*

_{∑Δ}fluctuates continuously, and occasionally exceeds

*p*

*. Thus, without the*

_{thr}*M*-out-of-

*N*criterion, these false alarms would not have been rejected, since the third criterion would be applicable.

The decision regarding the values of (*M*, *N*) should consider the following issues. As *M* increases, the risk of rejecting real targets within the beamwidth declines, and the probability of rejecting false alarms by FSLs increases. However, since at least *M* detections are required, the time required to decide whether the detection should be rejected also increases. As *N* for a given *M* increases, the probability of rejecting false alarms through FSL is enhanced, but the risk of rejecting real targets within the beamwidth increases as well. In Table 1, the rejection ratios (ratios of the number of rejected detections to that of total detections) for various combinations of (*M*, *N*) are provided for the cases in Fig. 9(b) (detections of the real target through mainlobe) and Fig. 9(d) (false alarms due to surface clutter through FSL). As previously mentioned, the rejection ratio for the detection of the real target declines as *M* increases. Therefore, for a given *M*, the rejection ratio of false alarms improves as *N* increases, but that for detections of the real target increases as well. Note that the optimum choice of (*M*, *N*) depends on the radar system requirements. For instance, if waiting for the decision of rejection until the 7th detection is allowable, (*M*, *N*) = (4, 7) is optimum. However, if the radar system requires faster decisions, (*M*, *N*) = (2, 4) or (3, 5) would be a better choice.

## V. Conclusion

In airborne radar, surface clutters can induce false alarms which disturb the detection of a target of interest. Although SLB is a simple and efficient technique for rejecting false alarms, interference from FSLs that are not covered by an auxiliary antenna may occur in real airborne radar systems.

The false alarm rejection technique proposed in this paper focuses on false alarms caused by surface clutter returns through FSLs. These false alarms can be rejected precisely if a LUT of FSL angles with fine resolutions of frequency and beam steering angles is available. However, the time and cost required to build such a LUT are not negligible. Thus, this paper provides an alternative technique that rejects false alarms based on their common characteristics. The rejection criteria presented in the previous section were derived based on these characteristics. Moreover, they also reduced the risk of rejecting the detection of real targets. The flight test results further verified that false alarms due to surface clutter returns through FSL can be effectively rejected using the proposed technique. It is also emphasized that parameters *p** _{thr}*,

*M*, and

*N*should be customized based on the characteristics and requirements of airborne radar systems.

## Acknowledgments

This work was supported by the Agency for Defense Development Grant funded by the Korean government (No. 274190001).

## References

## Biography

Ji Hwan Yoon, https://orcid.org/0000-0002-1661-4395 received his B.S. and Ph.D. degrees in electrical and electronic engineering from Yonsei University, Republic of Korea, in 2008 and 2016, respectively. Since March 2016, he has been with the Agency for Defense and Development, South Korea, where he is currently a senior researcher. His research interests include airborne radar systems, active electronically scanned array (AESA) radars, and reflectarrays.

Yeonhee Park, https://orcid.org/0000-0002-6399-663X received her M.S. degree in electrical and electronic engineering from Yonsei University, South Korea, in 2016. Since March 2016, she has been working for the Agency for Defense and Development, South Korea, where she is currently a principal researcher. Her research interests include airborne radar systems.

Ji Eun Roh, https://orcid.org/0000-0001-5156-9860 received her Ph.D. in computer science and engineering from POSTECH, South Korea, in 2006. Since March 2006, she has been working for the Agency for Defense and Development, South Korea, where she is currently a principal researcher. Her research interests include airborne AESA radar systems, radar resource management, and radar data processing.