International Journal of Antennas and Propagation

Volume 2015 (2015), Article ID 631217, 12 pages

http://dx.doi.org/10.1155/2015/631217

## Research on Polarization Cancellation of Nonstationary Ionosphere Clutter in HF Radar System

School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China

Received 25 April 2014; Revised 20 August 2014; Accepted 9 September 2014

Academic Editor: Hang Hu

Copyright © 2015 Xingpeng Mao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Oblique projection polarization filter (OPPF) can be applied as an effective approach for interference cancellation in high-frequency surface wave radar (HFSWR) and other systems. In order to suppress the nonstationary ionosphere clutter further, a novel OPPF based clutter suppressing scheme is proposed in this paper. The polarization and nonstationary characteristic of the clutter are taken into account in the algorithms referred to as range-Doppler domain polarization suppression (RDDPS) and the range-time domain polarization suppression (RTDPS) method, respectively. The RDDPS is designed for weak ionosphere clutter and implemented in the range-Doppler domain directly, whereas the RTDPS algorithm is designed to suppress the powerful ionosphere clutter with a multisegment estimation and suppression scheme. About 15–23 dB signal to interference ratio (SIR) improvement can be excepted when using the proposed method, whereas the targets can be more easily detected in the range-Doppler map. Experimental results demonstrate that the scheme proposed is effective for nonstationary ionosphere clutter and is proven to be a practical interference cancellation technique for HFSWR.

#### 1. Introduction

By exploiting the long-range propagation of vertically polarized electromagnetic wave in the band of 2–15 MHz, high-frequency surface wave radar (HFSWR) is able to receive vessel and low-flying aircraft echoes over the horizon. However, the signal environment of high-frequency (HF) band is far from satisfactory. The powerful shortwave radio interference, ionosphere clutter, and industrial interference that dominate the pure receiver noise in the HF band cause a significant limit of detection capability. Ionosphere clutter, which is often observed to mask multiple successive range and Doppler cells, is one of the main interference sources. In some cases, the power of the clutter is so high that even the target echoes are overwhelmed, resulting in poor detection and tracking performance [1, 2].

Adaptive beamforming schemes have been developed by using the space information of ionosphere clutter [3–5]. However, when the target is overlapped by ionosphere clutter from the same or close direction, such approaches are not ideal. According to the fact that the echoes of targets arriving along the surface of ocean used to be vertically polarized while ionosphere clutter is elliptically polarized, polarization filtering can be applied to improve the HF radar performance. By utilizing the polarization difference between the target and the clutter, polarization filter can be used to suppress interference [6–9]. However, a severe loss of coherent integration occurs when a conventional polarization filter is applied to coherent systems such as HFSWR, as an additional amplitude and/or phase distortion of the target signal will be introduced. In order to solve this problem, null phase-shift polarization filter (NPPF) [10] is proposed and then extended to a frequency domain null phase-shift multinotch polarization filter in [11, 12]. Such improvement effectively keeps the temporal coherence of the target within coherent integration time (CIT) and successfully paved a way for the application of polarization filtering in HFSWR. To improve the flexibility and convenience, oblique projection polarization filter (OPPF) [13] is further proposed to deal with nonorthogonal signals. Successful experimental evaluation of OPPF for suppressing shortwave radio interference in HFSWR is reported in [14].

However, the ionosphere clutter cancellation method, which meets the requirement of practical HFSWR system, is still an open issue, as the ionosphere clutter is usually nonstationary. In this paper, two types of clutter cancellation approaches are given to suppress the nonstationary ionosphere clutter. The range-Doppler domain polarization suppression (RDDPS) is designed for weak ionosphere clutter and performed in the range-Doppler domain, while the range-time domain polarization suppression (RTDPS) is designed for strong ionosphere clutter. The procedure of clutter polarization estimation and clutter suppression in segments for the RTDPS is emphasized in our research. And the segmentation parameter optimization is also discussed in detail to obtain a balance between the clutter cancellation and the target restoration. The specific utilization of RDDPS or RTDPS depends on the type of the clutter at the corresponding range cell.

The remainder of this paper is organized as follows. In Section 2, the design of OPPF is introduced and the impact on the performance of the OPPF by the estimation error is analyzed. The nonstationary ionosphere clutter cancellation scheme is proposed in Section 3, whereas the applications of RDDPS and RTDPS are discussed in detail in Section 4. Then the experimental performance evaluation of the proposed scheme is given in Section 4. Finally, the conclusions are drawn in Section 5.

#### 2. Oblique Projection Polarization Filter

##### 2.1. Received Signal Model

Suppose the received wavefront is a completely polarized wave and its polarization angle and polarization angle difference are and , respectively. Ignoring the absolute phase, the incident signal can be described by Jones vector as in [10] aswhere is the horizontally polarized component of while is the vertically polarized component of . Vector is defined as the polarization steering vector of , and

##### 2.2. OPPF and Error Analysis

Suppose the polarization steering vectors of the target and interference are and , respectively. If the two steering vectors satisfy , a corresponding polarization oblique projection operator can be constructed by [13]where and represents the unit matrix.

The fundamental property of this polarization oblique projection operator can be given as

The scalar form of (4) can be given aswhere .

Usually, the received signal is a linear combination of the target , interference , and noise . According to (4), the output of the OPPF can be given as

When scalar form is required, the filtering process can be rewritten as

According to (5), (7) can be simplified as

Equations (6) and (8) indicate that the interference can be completely cancelled and the target is restored without distortion when the polarization steering vectors of the target and interference, which are utilized to build the subspaces of desired ranges and null spaces, are estimated accurately.

If the estimation accuracy is uncertain, in order to analyze the performance loss of OPPF that was caused by the estimation error, the mean square error (MSE) of the target can be defined as , where is the recovered signal of the target and and denote the mathematical expectation and the Frobenius norm. Substituting (7) into MSE, the general form of MSE of the target can be given as where , , and are the real polarization steering vectors of the target and the interference, and and are the estimated values, respectively.

Supposing the target signal, interference, and noise are uncorrelated, (9) can be simplified as where And , , and are the power of the target, interference, and noise, respectively. Define and as the coefficients of and . The error analysis of the MSE will be discussed in three cases as follows.

###### 2.2.1. MSE without Estimation Error: ,

Supposing the polarization steering vectors of the target and the interference are both accurately estimated, according to (11) and (12), we have . The MSE gets to its minimum as

###### 2.2.2. MSE with Target Estimation Error: ,

In this case, we have and , which indicates that the interference can be completely suppressed while the target is restored with distortion. Then the MSE can be simplified from (10) as

Ignoring the noise term, the MSE is mainly determined by and . According to the properties of oblique projection [15], it is beneficial to decompose vector into two specific components: the component that locates in the subspace spanned by and the one which lies in the subspace spanned by . Therefore, can be decomposed aswhere

According to [13], the first part in (17), which locates in the target subspace, will be reserved as a whole, whereas the second part will be cancelled by to null. Substituting (17) into (11), can be rewritten as

Therefore, (16) can be rewritten as

Equation (20) indicates that the MSE is mainly affected by the term of , which is the product of the target power and its corresponding coefficient . The lower the estimation accuracy of target’s polarization state is, the larger the value of will be, and the MSE increases. However, when the target is estimated with no error, (20) will be identical to (15).

###### 2.2.3. MSE with Interference Estimation Error: ,

Supposing the target signal is accurately estimated while the interference is not, we have and , which makes , . In this case, the target is perfectly restored while the interference is partly suppressed by OPPF. Decomposing vector into two specific components, we have where

Substituting (21) into (12), can be rewritten as

The MSE can be simplified as where and refers to the signal to noise ratio (SNR).

Equation (24) demonstrates that the interference cannot be suppressed completely when it is estimated with error. The degree of interference suppression is determined by the component in (21). The smaller the estimation deviation is, the larger this component will be and the performance of OPPF will be better.

Moreover, fixing the SIR and SNR and the power of the target, according to (14), it can be noticed that the part of in (15), (20), and (24) will increase and the MSE will become larger, when the polarization state of the target and the interference are getting close.

#### 3. Polarization Cancellation for Ionosphere Clutter

To apply an OPPF in ionosphere clutter cancellation, it is necessary to accurately estimate the polarization state of the target and the ionosphere clutter, so that the clutter can be suppressed to the maximum whereas the target can be effectively restored. With regard to the nonstationary characteristic of the ionosphere clutter, the polarization estimation methods and ionosphere clutter cancellation scheme should be properly designed.

##### 3.1. Polarization and Nonstationary Characteristic of Ionosphere Clutter

Ionosphere clutter, which is a kind of self-generated interference in HFSWR, is the radar signals reflecting back from the ionosphere. The polarization plane of the electromagnetic wave is usually rotated by the ionosphere along the radio propagation path, which makes the high power vertically polarized radar signals change into elliptically polarized ones. However, the echoes of targets arriving along the surface of ocean remain vertically polarized. Evident difference between the elliptical polarized ionosphere clutters and the vertically polarized target signals can be found in the dual-polarized HFSWR. That is, the power of the horizontally polarized component of ionosphere clutter is close to that of the vertically polarized part or even stronger. However, the power of the horizontally polarized component of the target echoes propagating along the ocean surface is quite weak compared with that of the vertically polarized component [16, 17].

Affected by the irregular activities of the free electrons and charged ions, the electron concentration of the ionosphere is unstable, which leads to the nonstationary feature of the ionosphere clutter [3]. The following characteristics of the ionosphere clutter are usually observed.(1)The ionosphere clutter is not stable in a CIT, and its duration time varies from several pulse periods to the whole CIT.(2)In each single pulse, some clutters occupy less range cells whereas some occupy more.(3)The nonstationary feature also leads to the different frequency spread in Doppler spectrum; some clutters locate in multiple successive Doppler cells while some exist in fewer Doppler cells.(4)The ionosphere clutter is usually elliptically polarized but its polarization state is unstable in the whole CIT [5]. As a result, it behaves as a partially polarized wave in a long time observation.

##### 3.2. Nonstationary Ionosphere Clutter Polarization Cancellation Scheme

According to the characteristics of ionosphere clutter discussed above, a novel OPPF based polarization cancellation scheme for nonstationary ionosphere clutter is proposed. Figure 1 gives the basic structure of this scheme. In Figure 1, the raw data is pretreated first to obtain the polarization information of the clutters and the targets, which is necessary for building the RDDPS or RTDPS; then the signals are filtered in range or range-Doppler domain, and the output of the filter will be utilized to perform range and Doppler processing in RTDPS or given as the result in RDDPS, respectively. Five steps are needed to finish the clutter cancellation.(1)Because of the weak power of radar echoes, range processing and Doppler processing need to be carried out first. Range-Doppler maps are generated from the raw data of each horizontal and vertical channel, respectively.(2)The state of all the samples in the range-Doppler map is then estimated, including the power value and polarization parameters. By using the basic characteristic of the signals in the range-Doppler map, the target and other clutters, such as ground clutter, ocean clutter, and ionosphere clutter, can be preliminarily separated and classified.(3)Based on the power estimation, the type of ionosphere clutter is identified cell by cell in range domain. The specific utilization of RDDPS or RTDPS at each range cell is determined by the clutter type at each range cell.(4)RDDPS and RTDPS algorithms, designed for different types of ionosphere clutter, will be used to suppress the clutter. The former one is designed for the ionosphere clutter with low power and the filtering process is carried out in range-Doppler domain directly. The latter one is designed for powerful clutter, and the filtering process is mainly carried out in the range-time domain. In a practical HFSWR system, the two types of ionosphere clutter may exist simultaneously and locate at different range cells. Therefore, the selection of RDDPS or RTDPS is decided by the clutter type at the corresponding range cell. Because of the adaptive scheme in this part, an optimal filtering effect can be obtained.(5)After all the range cells are treated with the procedure in (4), the final range-Doppler map is composed for RDDPS, whereas a range-Doppler processing is needed for RTDPS to obtain the final range-Doppler map.