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An approach to the modulation recognition of MIMO radar signals
EURASIP Journal on Wireless Communications and Networking volume 2013, Article number: 66 (2013)
Abstract
Multipleinput multipleoutput (MIMO) radar is a new radar system and draws more and more attentions in recent years. Along with the development of MIMO radar, the MIMO radar countermeasure is brought into being. Since the modulation recognition is of great significance in the electronic reconnaissance, this article presents a modulation recognition method for the signals of the emerging MIMO radar. Signals of interest are classified into three categories based on instantaneous autocorrelation spectrum analysis first. Then noncoding MIMO radar signals are discriminated by spectrum analysis, and coding MIMO radar signals are recognized by source number estimation algorithm. Meanwhile, the subcarrier numbers of some kinds of MIMO radar signals are estimated. Simulation results verify the effectiveness of the method and the overall correct recognition rate is over 90% when the value of SNR is above 0 dB.
1. Introduction
The concept of multipleinput multipleoutput (MIMO) radar, which comes from communication system, has drawn considerable attention in recent years from both researchers and practitioners[1]. It builds a bridge between the research of radar and communication. MIMO radar is generally divided into two categories, one is statistical MIMO radar with widely separated antennas, and the other is coherent MIMO radar with colocated antennas. In both categories of MIMO radar system, multiple transmit antennas are employed to emit specific waveforms and multiple receive antennas process the reflected signals jointly. MIMO radar offers quite a lot of advantages, such as more degrees of freedom, higher resolution, and sensitivity and better parameter identifiability[2–4]. These advantages mostly result from waveform diversity. Due to the waveform diversity, intercepted signals in reconnaissance receiver are multicarrier signals. Accordingly, signal detection, parameter estimation, and modulation recognition are vastly different from singlecarrier (SC) signals adopted by conventional radars. As a result, it poses an emerging and powerful challenge in electronic countermeasures.
In order to occupy an advantageous position in the future electronic warfare, we need to investigate the feature of MIMO radar and study its electronic countermeasures. This article discusses the modulation recognition of MIMO radar signals, which is of great significant in the electronic reconnaissance. Since there are some actual difficulties in engineering practice for statistical MIMO radar, hereinafter we focus on coherent MIMO radar.
The reconnaissance technology of MIMO radar is rarely studied in the published literatures. Liang[5] and Xing et al.[6] discussed about the electronic reconnaissance technology of MIMO radar at system design and conceptual angle. Tang et al.[7] provided a new reconnaissance technology for MIMO radar. The aim of the article is to discriminate whether it is MIMO radar by the number of orthogonal waveforms. However, the location of suspicious radar is as known information, which is usually unknown in actual environment. Besides, Chen et al.[8] and Hassan et al.[9] were about the modulation identification of MIMO system, which is adopted in the wireless communication field. In[8], the combination of second and fourthorder of cumulants was used as the feature parameters, which were utilized to discriminate the orthogonal frequency division multiplexing signals from the SC modulations. In[9], highorder statistics and neural networks were employed to identify the modulation type of MIMO system with and without channel state information.
Based on the instantaneous autocorrelation spectrum of received signals, an approach to the modulation recognition of MIMO radar signals is proposed in this article. For conventional radar, SC signal is often adopted, such as monopulse (MP) signal, linear frequency modulation (LFM) signal, phasecoded (PC) signal, and frequencycoded (FC) signal. For MIMO radar, four basic modulation types[10–12] are involved in this article: MPMIMO (orthogonal MP signal in MIMO radar), LFMMIMO (orthogonal LFM signal in MIMO radar), PCMIMO (orthogonal PC signal in MIMO radar), and FCMIMO (orthogonal FC signal in MIMO radar). Here, we need to discriminate MIMO radar signal from conventional radar signal and recognize the modulation type of MIMO radar signal.
The remainder of this article is organized as follows. In Section 2, four basic emitting signal models of MIMO radar are given. The recognition method is introduced in Section 3. Instantaneous autocorrelation spectrum analysis, frequency spectrum analysis, and source number estimation algorithm (SNEA) are involved in this section. Simulation results are given in Section 4 and conclusions are drawn in Section 5.
2. Signal models
Assuming that the transmitting arrays of MIMO radar are uniform linear arrays (ULA), in the reconnaissance receiver, the four basic received MIMO signal models can be expressed as follows[10–12].
where exp() denotes the exponential function, M is the number of subcarriers of MIMO radar signal, f_{0} is the carrier frequency, f_{ p } = 1/T and △φ denote the frequency interval and phase difference between adjacent subcarriers, respectively, u denotes the chirp rate, I is the code length of coding signal, T_{ s } = 1/f_{ s } is the sampling interval, f_{s} is the sampling rate,${\varphi}_{m}\left(i\right)\in \left\{0,\frac{2\pi}{L},\dots ,\left(L1\right)\xb7\frac{2\pi}{L}\right\}$ denotes the phase of subpulse i of the m th component,${f}_{m}\left(i\right)\in \left\{0,\frac{1}{{T}_{1}},\dots ,\left(\mathrm{I}1\right)\xb7\frac{1}{{T}_{1}}\right\}$ denotes the frequency of subpulse i of the m th component, L is the distinct phase number in PCMIMO, T and T_{1} represent the pulse and subpulse width, respectively, g(t), 0 ≤ t ≤ T_{1} is the envelope function. Particularly, to ensure the orthogonality of components, ∅_{ m }(i) and f_{ m }(i) are usually obtained by intelligent algorithm, such as genetic algorithm and simulated annealing algorithm.
3. Recognition method
For the sake of convenience, S set is employed: S = {MPMIMO, LFMMIMO, PCMIMO, FCMIMO and SC signals}. This section will be divided into three parts. The first part classifies S set signals into three categories: SC signals S_{0} = {SC signals}, noncoding MIMO radar signals S_{1} = {MPMIMO, LFMMIMO} and coding MIMO radar signals S_{2} = {PCMIMO, FCMIMO}. S_{1} and S_{2} set signals are recognized in second and third parts, respectively.
3.1 Instantaneous autocorrelation spectrum analyses
The signal pulse parameters can be extracted from instantaneous autocorrelation features. Since the frequency characteristics of different modulation types are diverse, instantaneous autocorrelation function can be utilized to the modulation recognition of MIMO radar signals. The instantaneous autocorrelation function is defined as
3.1.1. S_{1}/(S_{0},S_{2}) selection

(a)
S_{1} set signals analysis
The instantaneous autocorrelation of LFMMIMO signal is
where k = m − l, k ≤ M − 1. If k = 0, it denotes the signal term. Otherwise, it denotes the cross terms.
For convenience, let Δn = 1, then we can get
Accordingly, the signal term is get as
The cross term is obtained as
From the above, the moduli of signal term and cross term are$\left\frac{\text{sin}\left(\mathit{M\pi}{f}_{p}/{f}_{s}\right)}{\text{sin}\left(\pi {f}_{p}/{f}_{s}\right)}\right$ and$\left\frac{\text{sin}\left[\left(M\leftk\right\right)\pi {f}_{p}/{f}_{s}\right]}{\text{sin}\left(\pi {f}_{p}/{f}_{s}\right)}\right$, respectively. If k = 1, it is the biggest cross term.
Figure1b shows the instantaneous autocorrelation spectrum of LFMMIMO signal. The simulated MIMO radar has a ULA comprising four transmitting antennas with halfwavelength spacing between adjacent antennas. The simulated SNR is 5 dB. By the way, for the other parts of Figure1, the simulation conditions are the same. For Figure1b, the maximum peak results from signal term while other peaks are from cross terms. As we can see, the biggest cross term appears as the second peak in the autocorrelation spectrum. To extract signal features, we define the feature parameter: the ratio of second peak and maximum peak, which can be expressed as follows:
If u = 0, Equation (7) is the instantaneous autocorrelation function of MPMIMO. Since u is not related to (10), MPMIMO and LFMMIMO have the equivalent values of R_{Amplitute}. Comparing Figure1a with b, the fact is verified since the values of every peak are the same. Given the value ranges of parameters in Equation (10), the minimum value of R_{Amplitute} can be obtained.
In addition, by searching the peaks of instantaneous autocorrelation spectrum of noncoding MIMO radar signal, the number of subcarriers can be obtained. For Figure1, the simulated MIMO radar has four transmitting antennas. That is, the subcarrier number is four. Seven peaks can be seen in Figure1a,b. Six of them, resulted from cross terms in (9), are symmetrical. Together with the single frequency at center, the distinct frequency number is four. That is, the subcarrier number is four for the received MIMO radar signal, which is equal to the actual subcarrier number of noncoding MIMO radar signal. For the instantaneous autocorrelation spectrum of noncoding MIMO radar signal, supposing that the number of peaks is a, the subcarrier number is (a + 1)/2.
Meanwhile, some signal modulation parameters of noncoding MIMO radar signal can be got from the instantaneous autocorrelation spectrum. For example, the chirp rate u and the frequency interval between adjacent subcarriers f_{ p }. They can be estimated from the location of peaks.

(b)
S_{2} set signals analysis
For PCMIMO and FCMIMO, substituting Equations (3) and (4) into Equation (5), respectively, the instantaneous autocorrelation functions are as follows:
where the superscript of ∅ and f denote the location of the code.
For Equation (11), since${\varphi}_{m}^{n+1}{\varphi}_{l}^{n}=\mathrm{\text{const}}$ in a subpulse, r_{PCMIMO}[n,1] is constant in a subpulse. If n is the last sampling points of a subpulse, then n + 1 is in the next subpulse. So, the value of$\left({\varphi}_{m}^{n+1}{\varphi}_{l}^{n}\right)$ suddenly changes and mutations appear in subpulses junctions. The constant values result in zero frequency. The mutations will bring about some low frequencies, the amplitudes of which are very small compared with the zero frequency. That is, only one obvious peak appears at the zero frequency in the frequency spectrum of PCMIMO.
For Equation (12), there are M × M components in a subpulse. M components, which are from${f}_{m}^{n+1}{f}_{l}^{n}=0$ in the case of m = 1 in a subpulse, result in zero frequency. If m ≠ l, then${f}_{m}^{n+1}{f}_{l}^{n}=\mathit{\Delta f}$ in a subpulse. There will be M^{2} – M cross terms, which result in low frequencies. Considering the symmetry, there are$\raisebox{1ex}{$\left({M}^{2}M\right)$}\!\left/ \!\raisebox{1ex}{$2$}\right.$ positive frequency components. M – 1 components have the same frequency in the worst case. However, since the code length I is far greater than the number of subcarrier M generally, the codes of each component are different in the same subpulse. So, the values of$\left({f}_{m}^{n+1}{f}_{l}^{n}\right)$ in a subpulse are diverse. The other subpulses as well. That is, the energies of the cross terms almost cannot be superimposed. Only one obvious peak appears at the zero frequency in the frequency spectrum of FCMIMO.
The instantaneous autocorrelation spectrums of PCMIMO and FCMIMO signals are shown in Figure1c,d, respectively. As can be seen, most of the energies gather at the zero frequency while some appear at low frequencies for both PCMIMO and FCMIMO, which verify the above analysis. As a result, values of R_{Amplitute} for coding MIMO radar signals are very small, which are less than the value of noncoding MIMO signals. The same to the SC signals, which can be seen in Figure1e,f.
Consequently, taking the ratio of second peak and maximum peak as the feature parameter, S_{1} signals are discriminated from S_{0} and S_{2} set, which is expressed as follows:
where γ is the value of the threshold.
Assuming that 4 ≤ M ≤ 50, 0.005 ≤ f_{ p }/fs ≤ 0.02, which satisfy most of the signal environment, the minimum value of Equation (10) is 0.7502. That is, for noncoding MIMO radar signal, the value of R is always greater than 0.7502. Obviously, for S_{0} or S_{2} set signals, the value of R is smaller than 0.7502. As a consequence, 0.75, the value of the threshold, permits classifying between S_{1} set and other modulation types.
3.1.2. S_{0}/S_{2} selection
Let Δn = 0, then r[n, 0] = s[n]s*[n].
For SC signals, r_{SC}[n, 0] = 1. As a result, the frequency of r_{SC}[n, 0] is zero.
For coding MIMO radar signals, according to (3) and (4), we can get the r[n,0] of PCMIMO and FCMIMO signals, which can be expressed as follows:
From Equations (14) and (15), we can see that there are some mutations in the correlation function. As a result, they will bring about some lowfrequency components. Since the frequency of r_{ SC }[n, 0] is zero, this allows us to discriminate SC signals from coding MIMO radar signals.
Employing Fourier transform on the r[n,0], which is obtained from S_{0} and S_{2} set signals, we can obtain its positive frequency spectrum.
where N_{DFT} denotes the points of discrete Fourier transform (DFT). Then we divide the positive frequency into two segments. One is the lowfrequency segment; the other is the rest part.
After that, we define the feature parameter R_{mean} as
where Mean() is the mean function. Since r_{SC}[n, 0] = 1 for SC signals, the frequency of r_{ SC }[n, 0] is zero, which implies that R 1_{r[n,0]} and R 2_{r[n,0]} are only affected by noise. Thus, R_{mean} approximately equals 1 in the additive white Gaussian noise (AWGN) condition. For coding MIMO radar signals, according to Equations (14) and (15), there are a few mutations in the r[n,0]. Hence, they will lead to some lowfrequency components, the values of ratio R_{mean} of coding MIMO radar signals are always greater than 1. This permits us to discriminate SC signals from coding MIMO radar signals.
Consequently, setting proper thresholds ς, S_{2} set signals are discriminated from S_{0} set signals, which is expressed as follows:
MPMIMO/LFMMIMO selection
According to the first part, S set signals are divided into three categories. To recognize the modulation type of MIMO radar signal, we need to keep working on S_{1} set and S_{2} set signals. In this part, our attention is focused on the S_{1} set. The goal is to discriminate MPMIMO signal from LFMMIMO signal.
Selecting two different lengths of time window for the signal and employing DFT on them, we can get
As can be seen in (20), s_{ a } is contained in s_{ b } and the sampling length of s_{ b } is greater than s_{ a }.
If the maximum value of S_{ a } is noted by Max(S_{ a }), and the corresponding frequency number is k_{max}, then the value of S_{ b }[k_{max}] is obtained. We define the following feature parameter
The frequency of MPMIMO signal is not affected by the time while LFMMIMO signal is modulated along time. That is, for MPMIMO signal, the energy at a frequency is increasing with the increase of sampling points while the LFMMIMO is not, for the energy present at the extra frequencies. Setting a proper threshold ξ, MPMIMO signal and LFMMIMO signal are separated.
3.3. PCMIMO/FCMIMO selection
The SNEA is employed to recognize the signals of S_{2} set in this part. For the sake of convenience, only the first code length signal is chosen. That is, the signal section going to be analyzed is s(n), n < T_{1}/T_{ s }. We first construct the observation matrix
and then get its autocorrelation matrix
where N − 1 + M_{ r } < T_{1}/T_{ s }.
For PCMIMO signal, according to Equation (3), we have
To simplify expressions, T_{ s } = 1 is employed in the calculation.
Let
Obviously, rank(A_{0}) = 1, where rank(*) denotes the rank of matrix *. Observing Equation (23), we have S_{ ob } = A_{0}S_{0}. Then$R={A}_{0}{S}_{0}{S}_{0}^{H}{A}_{0}^{H}/N$. According to the nature of rank, it is easy to get that rank(R) = 1 for PCMIMO signal.
For FCMIMO signal, according to (4), we have
Let
where f_{1}, f_{2},…, f_{ M } denote the first frequency code of each component. To ensure the orthogonality between components, the values of f_{1}, f_{2},…, f_{ M } are not equal. According to Equations (29) and (30), we have S_{ ob } = A_{1}S_{0} and$R={A}_{1}{S}_{0}{S}_{0}^{H}{A}_{1}^{H}/N$ for FCMIMO signal. Substituting Equation (4) into S_{0}, we can obtain that rank(S_{0}) = M. If M_{ r } > M, then rank(A_{0}) = M, and so rank(R) = M for FCMIMO signal. Hence, PCMIMO signal and FCMIMO signal are recognized. Meanwhile, the number of subcarriers of FCMIMO signal is obtained by the rank of R.
Here, Akaike Information Criterion[13] is adopted to calculate the rank of the autocorrelation matrix. By comparing the rank of autocorrelation matrix with 1, PCMIMO and FCMIMO are discriminated. That is
At last, the modulation recognition method can be summarized as following flowchart in Figure2.
4. Simulation and analysis
Simulation results are shown in this section. Modulation types in S set, which are given by Ω_{1} and Ω_{2}, are used to test the validity of the proposed approach.
The figure following each MIMO radar signal denotes the number of subcarriers. Suppose that the received signals are imbedded in complex AWGN and are rectangular pulse shape. Particularly, experiments are operated on two different simulation conditions, which are showed in Table1.
4.1. The classification results
First, we study the performance of the proposed classifier with several values of SNR. Because of lack of space, simulation results under 0 dB is selected as representatives. Confusion matrices for Ω_{1} and Ω_{2} are shown in Tables 2 and3, respectively, on two different conditions listed in Table1. 1000 signals are utilized for each modulation scheme.
From Tables 2 and3, we can see that the approach based on instantaneous autocorrelation spectrum successfully classifies the S set signals into three categories. The correct classify rates are all over 90%. It shows that this approach is well prepared for the following modulation recognition. Moreover, the performance shown in Tables 2 and3 is alike on the constant condition, which indicates that the method is unaffected by subcarrier numbers of MIMO radar signal.
4.2. The recognition results
Figures 3,4,5, and6 represent the final recognition results. As can be seen, the total recognition probability is over 90% when the value of SNR is as low as 0 dB. The results demonstrate that the modulation types are well recognized by the suggested method.
This method presents an excellent performance for noncoding MIMO radar signals, which is of high recognition rate even if the value of SNR is −5 dB. Comparing Figure3 with Figure5, the recognition performance is almost the same, which demonstrates that the subcarrier number influences the proposed method slightly. This can also be illustrated by comparing Figure4 with Figure6. Comparing Figure4 with Figure3, the performance of FCMIMO signal in low SNR slightly decreased, which results from high code rate. The same conclusion can be obtained by comparing Figure5 with Figure6.
4.3. The estimation results
As is mentioned above, some parameters of MIMO radar signal can be estimated simultaneously by the recognition method. Here, the estimation results of subcarrier numbers of noncoding MIMO radar signal and FCMIMO radar signal are presented as representative.
The simulation is conducted on the condition 1. The signals used to test are Ω_{3} and Ω_{4}, which can be expressed as
Figures 7 and8 show the relative error of estimation. As can be seen, the subcarrier numbers of MIMO radar signal are almost correctly estimated when the value of SNR above 0 dB. If the values of SNR below 0 dB, the relative error of estimation is acceptable. Comparing Figure7 with Figure8, the relative error of Figure8 is smaller than Figure7. This results from the calculation methods of relative error. By the proposed approach, for the different subcarriers, the absolute error changes slightly. Then the relative error decreases with the increases of subcarriers.
5. Conclusions
This article presents an approach to recognize the modulation type of MIMO radar signals for the first time. Three feature parameters are proposed in the recognition method. First, the intercepted signal is classified based on the instantaneous autocorrelation spectrum. Then, taking advantage of the difference in frequency domain, MPMIMO signal and LFMMIMO signal are discriminated. At last, SNEA is employed to recognize PCMIMO signal from FCMIMO signal. Besides, subcarrier numbers of noncoding MIMO radar signal and FCMIMO signal are estimated simultaneously. Simulation results verify that the proposed method can extract the features of each modulation type, and effectively recognize the signals in the given set. This result can be provided as an analysis reference for the research of MIMO radar countermeasures.
References
 1.
Li J, Stoica P: MIMO Radar Signal Processing. New Jersey: Wiley; 2008.
 2.
Bliss DW, Forsythe KW: Multipleinput multipleoutput (MIMO) radar and imaging: degrees of freedom and resolution. In Proceedings of the ThirtySeventh Asilomar Conference on Signals, Systems and Computers, vol. 1. California, USA; 2003:5459. November
 3.
Forsythe KW, Bliss DW, Fawcett GS: Multipleinput multipleoutput (MIMO) radar: performance issues, in Proceedings of the ThirtyEighth Asilomar Conference on Signals, Systems and Computers, vol 1. California: USA; November 2004. pp. 310–315
 4.
Li J, Stoica P, Xu LZ, Roberts W: On parameter identifiability of MIMO radar. IEEE Signal Process. Lett. 2007, 14(12):968971.
 5.
Liang BC: Electronic reconnaissance technology for MIMO radar. Shipboard Electron. CM (in Chinese) 2008, 31(5):1719.
 6.
Xing RY, Zhou CG, Tong TJ: The characteristic of foreign MIMO radar system and the space electronic reconnaissance and countermeasure. Foreign Inf. War (in Chinese) 2007, 1: 3839.
 7.
Tang XW, Tang J, Tang B: A new electronic reconnaissance technology for MIMO radar. In Proceedings of the 2011 IEEE CIE International Conference on Radar, vol. 1. Chengdu, China; 2011:7983. October
 8.
Chen J, Kuo YH, Liu XL: Modulation identification for MIMOOFDM signals. In Proceedings of the IET Conference on Wireless, Mobile and Sensor Networks, vol. 1. Shanghai, China; 2007:10131016. December
 9.
Hassan K, Hamouda W, Dayoub I: Blind modulation identification for MIMO systems. In Proceedings of the IEEE Global Telecommunications Conference, vol. 1. Miami, USA; 2010:15. December
 10.
Liu B, He ZS, Zeng JK, Liu BY: Polyphase Orthogonal Code Design for MIMO Radar Systems. In Proceedings of the CIE International Conference on Radar, vol. 1. Shanghai, China; 2006:14. October
 11.
Liu B, He ZS: Orthogonal discrete frequencycoding waveform design for MIMO radar. Chin. J. Electron. 2008, 25(4):471476.
 12.
Liu B: Research on generation of orthogonal waveform and signal processing for MIMO radar. Dissertation, University of Electronic Science and Technology (in Chinese). 2008.
 13.
Wax M, Kailath T: Detection of signals by information theoretic criteria. IEEE Trans. Acoust. Speech Signal Process. 1985, 33(2):387392. 10.1109/TASSP.1985.1164557
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Wang, X., Xiong, Y., Tang, B. et al. An approach to the modulation recognition of MIMO radar signals. J Wireless Com Network 2013, 66 (2013). https://doi.org/10.1186/16871499201366
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Keywords
 Modulation recognition
 MIMO radar signal
 Instantaneous autocorrelation spectrum
 Source number estimation
 Subcarrier number