A Clutter Sample Selection-based Generalized Sidelobe Canceller Algorithm for Ionosphere Clutter

Suppression in HFSWR

Zhou Jianyu ,Wei Yinsheng , Xu Rongqing School of Electronics and Information Engineering

Harbin Institute of Technology Harbin, China

ssszjysss@163.com

Abstract—Ionosphere clutter is one of the majority interference in High Frequency Surface Wave Radar. Duo to the ionosphere is a time-varying, non-stationary and layered lossy media, the ionosphere clutter suppression problem become a stubbornly challenge. In this paper, a clutter sample selection-based Generalized Sidelobe Canceller (GSC) algorithm for ionosphere clutter suppression is proposed by analyzing the spatial characteristics of the ionospheric clutter in the measured data. Using the training sample selection strategy and modified calculation of covariance matrix, we have obtained efficient ionosphere clutter suppression capability. At the end of this paper, the superiority of our proposed algorithm was demonstrated via simulation based on measured data.

Keywords—HFSWR; Ionosphere clutter; GSC; training sample selection; spatial correlation

I. INTRODUCTION

High frequency surface wave radar (HFSWR) is a kind of instrument which can provide capabilities of targets monitoring and ocean remote sensing over the horizon by using high frequency electromagnetic wave (3MHz-30MHz). However, the high frequency band has an extremely complex electromagnetism environment, which contains radio interference, meteor trail, sea clutter and ionosphere clutter [1].

The HFSWR receiver can receive radio signal, which works on HF band. This radio signal is a kind of strong interference with some quite simple characteristics. For this kind of interference, we can use adaptive beam forming to suppress it. Meteor trail is an interference from the universe. Sea clutter is the echo from water surface, and its amplitudes spread from cell to cell, typically described by a Weibull or log-normal distribution [2].

Ionosphere clutter is the echo from single or multiple ionosphere layers. The ionosphere is a time-varying, non-stationary and layered lossy media, which brings great difficulties to suppress the ionosphere clutter. Duo to the ionosphere clutter always exists in the HFSWR, the radar detection performance is limited [3].

In decades, the ionosphere clutter suppression problem become a stubbornly challenge. Many methods have been

investigated, such as STAP-based ionosphere clutter suppression method [4], wavelet image processing (WIP) method [5], and wavelet oblique projecting filter (WOPF) method [6]. These algorithms are all utilized the measured data samples immediately, without any training sample selection or screening. This kind of sample selection pattern is unable to obtain training samples with unified and homogeneous characteristic. Several inappropriate training samples can even reduce the performance of the algorithms.

Therefore, this paper proposed a clutter sample selection-based GSC algorithm for ionosphere clutter suppression in HFSWR. In our method, neighborhood spatial correlation (NSC) coefficient is defined firstly to describe the spatial feature of the ionosphere clutter in section II. According to the statistical result of the measured data, in section III, we propose to utilize the NSC coefficient to select the training samples of GSC algorithm. Furthermore, we modify the calculation of covariance matrix by joint the NSC coefficient to obtain better performance of the ionosphere clutter suppression. At the end of this paper, the superiority of our proposed algorithm was demonstrated via simulation based on measured data.

II. SPATIAL CHARACTERISTICS OF IONOSPHERE CLUTTER

In order to describe the spatial characteristics of the ionosphere clutter in the echo data of HFSWR, we present the definition of the neighborhood spatial correlation coefficient.

Consider a scenario with a uniform linear array of M elements. For the echo data of the i th range bin and j th

Doppler bin ,1 ,2 ,

T

ij ij ij ij Mx x x x

, we can obtain the

spatial spectrum of the echo data:

=A T

ij ijb x (1)

Where matrix 1 2

T

KA a a a

is the

steering matrix function, that

2 sin / 2 1 sin /1, , ,j k j kTj f d c j M f d c

ka e e

(2)

We assume the echo data of the p th range bin and the

q th Doppler bin ,1 ,2 ,

T

pq pq pq pq Mx x x x

belong to

setij

, which is the neighborhood set of ijx

. In view of the

range expansion and Doppler expansion, neighborhood set ij

should exclude the positions, which is next to the target

cell ,i j , to protect target.

The spatial correlation coefficient between ijx

and pqx

can

be expressed as:

,,

ij pq

ij pq

ij pq

Cov b br b b

Var b Var b

(3)

Where ,ij pqCov b b

is the covariance of ijb

and pqb

, Var

is the variance. Define the neighborhood spatial correlation (NSC) coefficient

,

1,

ij

ij ij pqp q

r b bK

(4)

Where K is the element number of the setij

.

Due to the ionosphere is a kind of time-varying, non-stationary, dispersion and layered lossy media, the spatial distribution characteristics of different ionospheric clutter are also diverse. In this section, we will use the measured array data, which is collected form the HFSWR, to analyze the neighborhood spatial correlation coefficient of different ionosphere clutter.

Table 1 gives the statistical results of the neighborhood correlation coefficients of the three group measured data. The neighborhood spatial correlation coefficient of the sea clutter and noise is quite small, which shows that the spatial distribution of sea clutter and noise is uncorrelation. This result is tally with the feature of sea clutter and noise.

The neighborhood spatial correlation coefficient of the E layer ionosphere clutter and F layer ionosphere clutter is quite high. We can distinctly distinguish them from the sea clutter and noise. Furthermore, the neighborhood spatial correlation coefficient of the Es layer ionosphere clutter is also quite high. But there are still some ionosphere clutters with small neighborhood spatial correlation coefficient (such as Data 2 in table 1). Those clutters are usually presented as a ‘ball’ in the range-doppler spectrum, which are located near the zero Doppler frequency bin and occupied multiple range bin.

Table 1 STATISTICAL RESULTS OF THE NSC COEFFICIENTS

Data Sea clutter Noise E layer Es layer F layer range mean Var. range mean Var. range mean Var. range mean Var. range mean Var.

1 0.12-0.46

0.27 0.006 0.08-0.54

0.21 0.006 0.48-0.71

0.58 0.020 0.28-0.65

0.51 0.014 0.33-0.80

0.60 0.019

2 0.10-0.42

0.24 0.003 0.07-0.41

0.18 0.003 0.46-0.75

0.58 0.019 0.10-0.48

0.39 0.006 / / /

3 0.11-0.41

0.25 0.003 0.09-0.39

0.18 0.002 0.41-0.68

0.55 0.007 0.31-0.59

0.49 0.002 0.47-0.69

0.57 0.006

III. CLUTTER SAMPLE SELECTION-BASED GSC ALGORITHM

A. Signal Model and Assumptions

GSC is a classical adaptive beamforming algorithm, the structure of the conventional GSC is illustrated in Fig.1. As shown in the figure, the upper path includes the quiescent

signal matched filterqw . Traditionally,

qw is selected as a

fixed beam with a boresight to desire signal direction. The lower path includes the blocking matrix B and the interference cancelling filter

aw . The operation for remove the desired

signals from the received array data.

qw

aw

Fig.1. Generalized sidelobe canceller structure

The output can be expressed as

H

q az k w Bw x k (5)

Where 1M

qw C , M M P

B C and 1M P

aw C . Let

2

J E z k denote the cost function of MSE. Consider the

minimum variance distortionless response, we can rewrite the constrained optimization problem as

min mina a

H

q a x q aw wJ w Bw R w Bw (6)

Then we can obtain the optimum weight as

1

,

H H

a opt x x qw B R B B R w (7)

Accordingly, the overall weight vector for GSC beamformer can be expressed

,opt q a opt

w w Bw (8)

B. Clutter Sample Selection-based GSC algorithm

Consider the section of training sample selection of the conventional GSC algorithm, we generally put the measured data into the algorithm immediately without any screening or selection. This kind of sample selection pattern is unable to obtain training simples with unified and homogeneous spatial characteristic. It is hard to estimate the covariance matrix of the clutter accurately. Several inappropriate selected training samples will make the degree of freedom of the GSC algorithm consumed a lot, which makes the performance of the approach decline rapidly and robustness deterioration. The reasonable selection of the training samples will effectively improve the performance of the GSC algorithm.

In section II, we analyze the neighborhood correlation of the ionosphere clutter in HFSWR. According to the statistical result, we found that most of the ionosphere clutter (except the ‘ball’ shape ionosphere clutter) have high neighborhood spatial correlation coefficient. Moreover, those clutter have homogeneous spatial features, and using those data as training sample can estimate the covariance matrix accurately.

The key contribution of our work is to select homogeneous sample and modify the calculation of covariance matrix. In view of section II, we can obtain the NSC matrix , where the element of NSC is calculated by formula (4). By threshold detection, a region

HS with high NSC coefficient can be given

1,

0,

ij

H ijij

s (9)

Where is the statistic threshold of NSC coefficient, and we can obtain it by statistics. We add the NSC coefficient to the covariance matrix calculating equation

1 1

0 0

1 1

0 0

K DH

ij ij iji j

k D

ijI j

X X

R I (10)

In this modification, we can keep the homogeneous sample making more contribution to covariance matrix.

IV. SIMULATION RESULTS

In this section, we will use the measured array data, which contains ionosphere clutter, to show performance of our approach. The array has 16 sensors, which is works on the frequency of 5.9MHz, and separated by half a wavelength of the narrowband sources. Fig.2 presents the range doppler spectrum of the measured data, and the beam direction is 4 . The ionosphere clutter can be seen obviously in the

figure, and the target is masked by the ionosphere clutter. Fig.3a gives the NSC coefficient of the measured data, which is calculated by formula (4). Fig.3b plots the region with high NSC coefficient. The threshold 0.5 is chosen through statistical results. Furthermore, since the ionosphere height is generally above 100km, we do not consider the near-area.

The processed results of the measured data are presented in Fig.4. Fig.4a plots the RD spectrum with the traditional GSC algorithm when the beam is set to 4 . The target is difficult to detect and is still masked by the ionosphere clutter. Fig.4b plots the RD spectrum with the clutter sample selection-based GSC (CS-GSC) algorithm we proposed above when the beam is set to 4 . As the figure shows, the ionosphere clutter is suppressed and the target can be detected easily.

Fig.2. Range doppler spectrum of the measured data

a b

Fig.3. NSC analysis

a NSC coefficient distribution

b Region with high NSC coefficient

To underline the advantage gained, Fig.5 gives the Doppler profiles at the 70-th range bin and 76-th range bin when the beam is set to 10 and 4 respectively. Those two range bins have two real targets, which are masked by the ionosphere clutter, and the Doppler bin of the target is 357-th and 365-th respectively. In order to compare with traditional algorithm, the figure also show the results of conventional GSC approach. Obviously, it is clear that conventional GSC algorithm has limited capability to suppress the ionosphere clutter. Compared to the two algorithms, Table 2 shows the performance comparison of the traditional GSC method and our proposed method. The algorithm we proposed can better suppress the ionosphere clutter than traditional GSC method. The signal-to-clutter-and-noise ratio (SCNR) gain of our method is higher than that of the traditional method 8.4dB and 10dB. Our new approach can process the ionosphere clutter better.

a b

Fig.4. Range doppler spectrum with

a traditional GSC algorithm

b proposed algorithm

Fig.5. Doppler profiles

Table 2 PERFORMANCE COMPARISON

Target Angle Range

bin Doppler

bin Original SCNR

SCNR gain GSC CS-GSC

1 -10° 70 357 4.9dB 5.7dB 14.1dB

2 4° 76 365 5.1dB 0.9 dB 10.9 dB

V. CONCULSION

In this paper, we have analyzed the spatial characteristic of ionosphere clutter in HFSWR and proposed the definition of neighborhood spatial correlation (NSC) coefficient. Further, we use the measured data, which is collected from HFSWR, to analyze the NSC coefficient of different ionosphere clutter. According to the statistical result, we find that the majority of the ionosphere clutter (except several ‘ball’ shape ionosphere clutter) have high NSC coefficient, which means those clutters have homogeneous spatial features. Therefore, we propose clutter sample selection-based GSC algorithm. We select the ionosphere clutter with high NSC coefficient as training samples, and add the NSC coefficient to the calculation of covariance matrix for making the homogeneous samples contribute more. It is expected that the covariance matrix can be estimated more accurately by apply those measures. Finally, it is proved by testing with measured data that the target masked by ionosphere clutter can be detected more easily by utilizing our method than the conventional GSC algorithm.

ACKNOWLEDGMENT

The authors would like to thank Professor Wei Yinsheng for his suggestion in paper organization and support in our research.

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