International Journal of Antennas and Propagation

Volume 2017, Article ID 7514916, 16 pages

https://doi.org/10.1155/2017/7514916

## MIMO High Frequency Surface Wave Radar Using Sparse Frequency FMCW Signals

^{1}National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China^{2}Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi’an, China

Correspondence should be addressed to Baixiao Chen; nc.ude.naidix@nehcxb

Received 14 March 2017; Revised 28 May 2017; Accepted 6 June 2017; Published 14 August 2017

Academic Editor: Ana Alejos

Copyright © 2017 Mengguan Pan and Baixiao Chen. 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

The heavily congested radio frequency environment severely limits the signal bandwidth of the high frequency surface wave radar (HFSWR). Based on the concept of multiple-input multiple-output (MIMO) radar, we propose a MIMO sparse frequency HFSWR system to synthesize an equivalent large bandwidth waveform in the congested HF band. The utilized spectrum of the proposed system is discontinuous and irregularly distributed between different transmitting sensors. We investigate the sparse frequency modulated continuous wave (FMCW) signal and the corresponding deramping based receiver and signal processor specially. A general processing framework is presented for the proposed system. The crucial step is the range-azimuth processing and the sparsity of the carrier frequency causes the two-dimensional periodogram to fail when applied here. Therefore, we introduce the iterative adaptive approach (IAA) in the range-azimuth imaging. Based on the initial 1D IAA algorithm, we propose a modified 2D IAA which particularly fits the deramping processing based range-azimuth model. The proposed processing framework for MIMO sparse frequency FMCW HFSWR with the modified 2D IAA applied is shown to have a high resolution and be able to provide an accurate and clear range-azimuth image which benefits the following detection process.

#### 1. Introduction

High frequency surface wave radar (HFSWR) refers to a classification of radar which operates in the HF band (3–30 MHz) and utilizes the surface wave mode of propagation. HFSWR systems can provide low-cost, 24-hour, all-weather, real-time, over-the-horizon surveillance of large ocean areas in excess of 200 nautical mile exclusive economic zone (EEZ) [1–4]. They can also provide real-time and all-weather measure for the oceanographical parameters including the surface currents, wave spectrum, wind direction, and intensity [5–10].

However, the HF band is a heavily congested part of the radio spectrum which makes it difficult to find a continuous silent frequency band to transmit radar signals. This limits the signal bandwidth of the HF radar system and results in a poor range resolution [11, 12]. In [11], a continuous measurement of noise and interference data in the frequency band of 3–6 MHz at Cape Race, Newfoundland, Canada, in the period between August 1, 1998, and May 10, 2000, shows that channels with a bandwidth of 20 KHz are readily available, while there is no channel available with a bandwidth of 100 KHz. Thus a signal bandwidth of several tens of kilohertz is common in typical HFSWR system which corresponds to a range bin of several kilometers.

Discontinuous spectrum signal with separate subbands distributed over a wide spectrum band is a solution to synthesizing a wideband waveform in a highly congested spectrum environment [13–18]. Systems using discontinuous spectrum waveform vary the carrier frequency at specific moments. A synthesizing processing of different segments gives a high resolution range estimate of the targets. In this paper, we combine the multiple-input multiple-output (MIMO) concept with the sparse spectrum signal and convert the temporal frequency diversity to spatial frequency diversity. Since the widespread use of FMCW signal [19] and its variation frequency modulated interrupt continuous wave (FMICW) signal [20] in HFSWR systems, for example, the SeaSonde [3, 9] and the WERA [10] systems, we consider the FMCW signal specially here and call the proposed system the MIMO sparse frequency FMCW radar system. The resulting system is a multifrequency MIMO radar system, actually, or similar to the synthetic impulse and aperture radar (SIAR) system [21–23]. But different from the conventional multifrequency MIMO radar, whose carrier frequencies are uniformly and continuously distributed, the proposed system has discrete and irregularly distributed spectrum bands. In the meantime, HFSWR systems can benefit from the MIMO concept in several other aspects [24–29]. Firstly, it provides adaptivity on transmit, thus doubling the degrees of freedom (DoF) compared with conventional phased array radar. Therefore, we can put more complexity in the transmitting element and in the limiting case; targets can be located in the horizon plane by using a single trivial receiving element. With the receiver system greatly simplified, it can even be equipped in small vessels [30, 31]. Secondly, MIMO approach has the ability to synthesize virtual antenna positions which results in a larger number of effective array elements. This is of particular interest for HFSWR as large antenna arrays are very costly. Lastly, in MIMO radar systems, orthogonal signals are transmitted by different sensors, resulting in a uniform distribution of the radiation energy in space. This meets the demand of HFSWR system for continuous whole spatial space surveillance.

The key ingredient of MIMO radar operation is that multiple orthogonal waveforms can be used simultaneously. The orthogonality of multichannel sounding signals means that their echoes are allowed to be separated at the receiver. Systems using FMCW or FMICW are quite different from systems using pulsed signal both in receiver structure and following signal processing method as they are deramping processing based, rather than matched filtering based. Naturally, the orthogonality of the waveforms compatible with FMCW radar is different from the counterpart of pulsed radar. MIMO sparse frequency systems with a matched filtering based receiver are presented by Lesturgie and Wang in [28] and [29], respectively. Some orthogonal waveforms for MIMO FMCW radar systems are discussed in [32, 33]. For our proposed MIMO sparse frequency FMCW radar system, the receiver and the signal processor are deramping processing based, and the orthogonality is surely achieved by the frequency offset of different channels.

We establish the signal model of a monostatic MIMO sparse frequency FMCW radar and propose an overall framework for the corresponding receiver and signal processor. We formulate the process of deramping and channel separating. And following crucial step is the range-azimuth processing which synthesizes a wideband signal and a narrow beam associated with the large virtual array. This step is actually the synthetic impulse and aperture processing (SIAP) in conventional SIAR systems [22, 23]. We set up a two-dimensional (2D) spectral analysis model for the range-azimuth processing. The sparsity of the carrier frequency leads to the irregularity of the sampling instants in the range domain, which results in the high sidelobe levels of the periodogram (matched filtering) output. Therefore different from the continuous spectrum multiple carrier frequency MIMO system, periodogram is not applicable for the sparse frequency MIMO system. In this paper we apply the concept of iterative adaptive approach (IAA) in the range-azimuth processing. IAA was first proposed in [34] for target direction of arrival (DOA) estimation. It can be interpreted as an iteratively weighted least-square periodogram which eliminates almost completely the leakage problems of the conventional periodogram method in a fully data-adaptive manner [34, 35]. Two-dimensional IAA algorithm has been proposed in [36] for the range-Doppler imaging and in [37] for range-angle processing of MIMO radar. In these papers the sliding window correlating is applied in the range domain which is not applicable in our proposed system as a line spectrum estimation problem is actually incorporated in the range domain. Therefore, a 2D IAA algorithm particularly compatible with our signal model is presented and the complexity is analyzed. And it is verified to be quite suitable as it can provide a clear range-azimuth image with sharp peaks at the locations of the targets and near-zero values at the other locations which benefits the following detection process.

This paper is organized as follows. Section 2 introduces the proposed MIMO sparse frequency FMCW HFSWR system and formulates the signal model. Section 3 presents a general framework for the receiver and signal processor of the proposed system. The input signal to the range-azimuth processor is formulated and at the end of this section, a two-dimensional spectral analysis model is set up for the range-azimuth processing. Section 4 presents a most intuitive solution, periodogram method for the range-azimuth processing. The spatial and temporal resolution and the coupling between the two domains are discussed in this section. Section 5 introduces the IAA algorithm into the range-azimuth processing and proposes a two-dimensional IAA algorithm which specially matches our model. Section 6 gives a complete design example for the MIMO sparse frequency FMCW radar. The whole signal processing flow is simulated. The range-azimuth image formed by periodogram and our modified 2D IAA are compared. Finally, conclusions are provided in Section 7.

#### 2. Signal Model for the MIMO Sparse Frequency FMCW HFSWR System

A HF radar system using MIMO technique and sparse frequency FMCW signal is illustrated in Figure 1. The number of transmitting sensors is and the number of receiving sensors is . Although the transmitters and receivers are apart, they are close enough to form a monostatic system. In this paper, we set up the model in the case of monostatic. The proposed system is actually a frequency diverse MIMO radar with each transmitting element emitting the same waveform of different carrier frequencies. The carrier frequency of each transmitting element is chosen in the guidance of current spectrum environment. Therefore, a frequency management subsystem should be cooperated to assist the main radar system which is already a common practice for currently deployed HF surface wave or skywave radars [38–41]. The frequency management subsystem monitors the spectrum environment, evaluates the monitoring results, and finally gives suggestions for frequency selection in real-time. It works concurrently with the main radar system. A typical HF spectrum distribution is illustrated in Figure 2. The shading areas below the threshold correspond to the silent frequency bands which can be used by the radar. The adjoining relatively silent segments in the frequency span to are chosen as the working bands of the system. Section 3 presents the requirement of the minimum frequency interval for each transmitted waveform to be separated at the receiver. We denote this frequency interval as . Thus in a relatively large silent spectrum block, several working bands can be placed in only if they are at least apart from each other. Let the start frequency and the bandwidth of each segment be and , . The signal bandwidth shall not exceed the minimum bandwidth of the working bands, that is, . Then all the transmitting elements transmit the same FMCW waveform with bandwidth equal to and sweep period equal to but with different start frequencies . The time-frequency relationship of the transmitted signals is illustrated in Figure 3.