Bai et al. EURASIP Journal on Advances in Signal EURASIP Journal on Advances Processing (2021) 2021:63 https://doi.org/10.1186/s13634-021-00769-9 in Signal Processing RESEARCH Open Access Clutter cancellation in passive radar using batch-based CLEAN technique Xia Bai* , Jiatong Han, Juan Zhao, Yuan Feng and Ran Tao *Correspondence: [email protected] School of Information and Abstract Electronics, Beijing Institute of Passive radar (PR) systems need to detect the presence of a target response, which is Technology, 5 South Zhongguancun Street, Haidian many orders of magnitude weaker than the clutter (direct signal and multipath). District, 100081 Beijing, China Indeed, the clutter cancellation is a key stage within a PR processing scheme. One of the most effective techniques in this field is using the CLEAN approach. In this paper, the batch-based CLEAN technique based on GMP and FFT has been proposed, which can speed up the computational processing and have better cancellation gain. Furthermore, segmenting operation can be applied to the signal obtained over long time. It is helpful to enhance temporal or spatial efficiency and overcome effect of time-varying clutter. Experiment results with simulated and real passive radar data verify the effectiveness of the proposed algorithm. Keywords: Clutter cancellation, Passive radar, CLEAN, Matching pursuit 1 Introduction Passive radar (PR) is a special bistatic radar system, which exploits signals of opportunity (e.g., FM, DVB-T, GSM, Wi-Fi, etc.) designed for other applications [1–3]. At the last years, lots of literatures on passive radar have been published worldwide. PR systems have received much interest in military as well as commercial applications, mainly because of their covertness, low-cost implementation, and immunity to jamming, etc. [4–7]. A typical PR system consists of two receive channels. One receive channel would sample the signal received directly from the transmitter of opportunity. This is known as the “ref- erence” channel. The other “surveillance” channel is used to detect echoes from targets. The surveillance channel also contains the small fraction of the direct signal and other stationary clutter. In general, the clutter causes serious degradation of dynamic range and masking of weak targets by sidelobes. It is great significance for passive radar signals pro- cessing to study clutter cancellation. Now, many solutions have been put forward and can be divided into two categories: (1) adaptive filtering methods and (2) fixed coeffi- cient methods. Some evaluation of clutter cancellation methods also have been presented [8–12]. The adaptive filtering methods mainly included least mean squares (LMS), recur- sive least squares (RLS), and least square lattice (LSL). These adaptive techniques can © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Bai et al. EURASIP Journal on Advances in Signal Processing (2021) 2021:63 Page 2 of 19 automatically adjust to time-varying signal statistics [10]. Fixed coefficient filtering tech- niques assume that the clutter terms are fixed throughout the coherent processing interval (CPI). The sample matrix inversion (SMI) [12]isatypicalcoefficientfiltering method. The filter output using the SMI technique in matrix form can be calculated by least squares (LS). In particular, the LS algorithm is very attractive due to its simplic- ity. Note that the cancellation performance of the fixed coefficient algorithms degraded significantly for time-varying scenario. Clearly, the selection of an appropriate method requires careful consideration of various factors according to the actual circumstance. CLEAN is another commonly used fixed coefficient filtering method. It was initially developed as a deconvolution tool in the mid-1970s [13, 14]. Recently, some CLEAN-type algorithms have been widely employed to remove the sidelobe effect for the radar signal processing. The CLEAN can be carried out in the range-compressed domain [15–17]or thetimedomain[18, 19]. The latter iteratively removes the direct signal and the echoes of stationary objects from the surveillance signal. The CLEAN technique is very similar to matching pursuit (MP), which chooses at each iteration an atom from the dictionary that is best adapted to approximate the residual [20]. The aim of MP is to build a sparse decomposition using an iterative procedure, which has been used in a wide variety of application areas [21, 22]. In this paper, we introduce an extension of the MP to pursue efficiency of clutter cancellation for passive radar. Our approach named GMP is literally a generalization of the MP in the sense that multiple atoms are identified per iteration and coefficients are estimated by use of LS. Based on GMP and fast Fourier transform (FFT), a batch-based CLEAN technique with a reasonable memory requirement and compu- tational time is proposed. It can identify and remove the largest components of clutter at each iteration; in other words, the clutter cancellation method has a batch processing style. This paper is organized as follows. In Section 2, the passive radar clutter signal model and CLEAN concept are described. In Section 3, we present GMP and discuss about how to carry out batch-based CLEAN. In Section 4, the performance of the proposed tech- nique is verified by using simulated and real passive radar data. The last section concludes this paper. 2 Model and basis of CLEAN 2.1 Passive radar signal model Typical passive radar geometry is depicted in Fig. 1. The transmitter illuminates a target and the reference antenna directs towards it. The surveillance antenna directs towards the target. We assume that the target moves with a given velocity, and s(t) is the transmit signal. Meanwhile, echoes of the reference channel and surveillance channel are denoted as sref (t) and ssurv(t),respectively. Since, at the reference channel, the direct signal is received by the main lobe of the receiver antenna, it is possible to assume that the signal collected by the reference channel does not contain clutter. In other words, the scatterer contributions received from the sidelobes are usually negligible. And then, the reference signal can be written as sref (t) = Ars(t − τr) + nref (t) (1) where Ar and τr, respectively, are the complex amplitude and delay for the direct signal. The amplitude Ar represents propagation effect, and the delay τr, which equals to the Bai et al. EURASIP Journal on Advances in Signal Processing (2021) 2021:63 Page 3 of 19 Fig. 1 The passive radar scenario bistatic baseline range over the speed of light, is generally assumed to be known and fixed. nnef (t) is the thermal noise. A more general model of the reference signal with noise and multipath interference is beyond the scope of this paper, and the detailed description can be found in [23, 24]. Assuming that there is only one target, the signal collected by the surveillance channel can be expressed as ssurv(t) = starget(t) + sclutter(t) + nsurv(t) (2) where starget(t) = Amts(t − τmt)exp(j2πfmtt) sclutter(t) = Ais(t − τi) = sci(t) i i The complex amplitude, delay, and Doppler shift for the moving target are Amt, τmt, and fmt, respectively. Similarly, the ith path (direct or multipath) sci(t) results in its own unique complex amplitude Ai and delay τi. nsurv(t) is the thermal noise at the surveillance antenna. To obtain target Doppler and range information, cross ambiguity function (CAF) between the surveillance and reference signals is an effective tool. The definition of CAF is (τ ) = ( ) ∗ ( − τ) (− π ) sCAF , fd ssurv t sref t exp j2 fdt dt (3) Tint where Tint is the coherent processing interval (CPI), τ is the delay, fd is the Doppler fre- quency, and ∗ denotes the complex conjugate. By substituting Eq. (2)intoEq.(3), Eq. (3) can be approximated as follows: sCAF(τ, fd) = sCAF,target + sCAF,clutter + sCAF,noise ≈ sCAF,target + sCAF,clutter (4) Bai et al. EURASIP Journal on Advances in Signal Processing (2021) 2021:63 Page 4 of 19 where (τ ) = ( ) ∗ ( − τ) (− π ) sCAF,target , fd starget t sref t exp j2 fdt dt Tint (τ ) = ( ) ∗ ( − τ) (− π ) sCAF,clutter , fd sci t sref t exp j2 fdt dt i Tint It was concluded that the final range-Doppler surface can be written as the sum of the range-Doppler surfaces of each component. In many cases, the clutter signal is much stronger than the useful echo signal. Thus, without compensation, the sidelobe of these clutter components sCAF,clutter may mask weak moving target sCAF,target . 2.2 CLEAN processing MP can be viewed in the radar context as a CLEAN algorithm: estimate iteratively the strongest components of the signal (in the correlation magnitude sense) and remove them from the original signal. We will then provide a brief description. The goal of clutter cancellation is to remove sclutter(t) from the surveillance channel signal ssurv(t) by first estimating the unknown clutter sclutter(t) and then subtracting this estimated component. From Eqs. (1)and(2), we can obtain ( ) = ( ) = − τ sclutter t sci t wisref t i (5) i i = / τ = τ − τ where wi Ai Ar and i i r is the relative discrete time delay of the ith path between the reference channel and surveillance channel.