Research Article  Open Access
A Parameter Estimation Algorithm for Multiple FrequencyHopping Signals Based on Sparse Bayesian Method
Abstract
Parameter estimation and network sorting for noncooperative wideband frequencyhopping (FH) signals have been essential and challenging tasks, especially in the case with little or even no prior information at all. In this paper, we propose a nearly blind estimation approach to estimate signal parameters based on sparse Bayesian reconstruction. Taking the sparsity in the spatial frequency domain of multiple FH signals into account, we propose a sparse Bayesian algorithm to estimate the spatial frequency parameters. As a result, the frequency and direction of arrival (DOA) parameters can be obtained. In order to improve the accuracy of the estimation parameters, we employ morphological filter methods to further clean the data poisoned by the noise. Moreover, our method is applicable to the wideband signal models with little prior information. We also conduct extensive numerical simulations to verify the performance of our method. Notably, the proposed method works well even in low signaltonoise ratio (SNR) environment.
1. Introduction
Frequencyhopping (FH) communication is widely used in the military communications, due to its high confidentiality, antijamming capacity, low susceptibility, and strong networking capabilities [1–3]. However, these features also make it difficult to estimate the parameters of multiple FH signals, especially when there is no prior information about the hopping patterns. In general, the parameter estimation of FH signals often involves sorting and identifying of possible spatial FH signals belonging to different FH networks [2, 4, 5]. Parameter estimation and network sorting are two fundamental but not wellsolved problems in the field of FH signals reconnaissance.
Among various methods of parameter estimation, estimation methods based on maximum likelihood (ML) are practically intractable with few or no prior information, and thus several ML based methods have been proposed [6, 7]. Recently, another two techniques have attracted a lot of attentions, that is, timefrequency analysis and sparse reconstruction, since they can estimate the parameters by recovering clear timefrequency spectrum. Methods based on timefrequency analysis are attractive due to its simplicity and fast computation, but their performance degrades in low signaltonoise ratio (SNR) scenarios [8, 9]. By contrast, sparse reconstruction based methods achieve good timefrequency spectrum and accurate parameter estimation in low SNR conditions [10–12]. However, all these aforementioned methods suffer from degrading performance and mismatching of signals and parameters with multiple FH signals.
In order to achieve accurate estimation and network sorting results simultaneously, direction of arrival (DOA) information and blind separation (BS) is utilized with the assumption of multiple array elements. In many cases, FH signals are actually wideband, while many existing methods of DOA estimation consider the FH signals to be narrowband signals [13, 14]. To solve this issue, an expectation maximization (EM) approach is proposed for blind hop timing DOA and frequency estimation in the situation of possible bandwidth mismatch in [15]; the success of this approach mainly depends on the selection of the initial value. Unfortunately, it is difficult to obtain a reliable initial timefrequency spectrum using shorttime Fourier transform (STFT) especially in low SNR environments. Paper [16, 17] proposes a novel method for parameter estimation and network sorting of wideband FH signals, which can only be used for multiple orthogonal signals but fails in asynchronous signals.
With the assumption of wideband multiple FH signals, we propose a new method based on sparse reconstruction and morphological filtering to achieve better parameter estimation and network sorting results. We exploit the sparsity of the spatial frequencies of multiple FH signals to build redundant sparse dictionaries, based on which spatial frequencies are estimated for the reconstruction of FH signals and the DOA estimation can also be achieved. To improve the estimation accuracy in low SNR conditions, morphological filtering is used to achieve more accurate timefrequency and timespace spectrum. Therefore, parameters of FH signals can be estimated and then networks can be sorted with DOA. Moreover, the proposed method can be used for different multiple FH signals, different parameters, and different SNR conditions.
The rest of the paper is organized as follows. In signal model, the FH signal model and the sparse signal model are formulated. In proposed method, parameter estimation and network sorting algorithms are formulated mainly including sparse Bayesian reconstruction method and morphological filtering. In Algorithm Procedure, the procedure of the algorithm is summarized step by step. Several simulations and conclusions are given in Simulation and Analysis and Conclusion, respectively.
2. Signal Model
2.1. FH Signal Model
Assume that FH signals impinge onto a uniform linear array (ULA) with interantenna spacing ; the DOA is , and the bandwidth of receiver is , where , , and and are the maximum and minimum frequency of the signals, respectively. The th FH signal can be expressed as follows:where denotes the amplitude of the th signal, is the frequency of the th FH signal at the moment , and is the initial phase of the th FH signal.
The model after discrete sampling can be expressed as follows:where is the sampling frequency.
Assume elements of the array are isotropic, ignoring the effect of mismatch and mutual of antenna. Assume the incidence angle of the th signal is , and its steering vector at th discrete moment can be described as
Both frequency and incidence angle can be deduced from (3). Define as “spatial frequency”, where , when , and , so (3) can be rewritten as
Therefore, the signal vector at the th sampling moment can be expressed as follows:where is the array manifold array of the th sampling moment and denotes incoming spatial signals with incidence angle vector and spatial frequency vector . The steering vector changes with the signal frequency of FH signal. denotes the Gaussian White Noise vector with zero mean and variance , where denotes identity matrix.
2.2. Spatial Frequency Estimation Based on Sparsity
In this paper, the interelement spacing meet , and the incidence angle of signal is , so that the range of spatial frequency is (−0.5, 0.5). If the sets of FH frequencies are finite, the possible combination of spatial frequencies are also finite, which satisfy the sparse condition in the spatial frequency domain. Spatial frequencies can be divided evenly into a complete discrete sets , whose spacing of division is . The quantization error of each element in the spatial frequency corresponding to each signal is less than or equal to , which makes the spatial frequencies at any moment constitute a small subset. In this way, (5) can be extended to the output model of redundant arrays as follows:where ’s columns are steering vectors corresponding to each element in the sets . are the extended version of vector with frequencies from , which also extended from . if and only if . can be renamed as for simplicity.
snapshots of received data are grouped as an operational frame. Data in two consecutive frames are not overlapped. The th frame can be expressed aswhere and and denotes snapshots number. The position of nonzeros in is related to .
usually have small sampling interval, whose number of samples satisfies . This means is with great sparsity. In order to estimate parameters from (5), should be bound to sparsity while obtaining best fitting of to . Therefore, the optimization target function can be written as follows: where is the penalty factor and .
By the optimization above, the sparse solution can be obtained, whose positions and amplitude of nonzero rows indicate the spatial frequencies of signals and signal amplitudes, respectively.
3. Proposed Method
The proposed method based on sparse Bayesian reconstruction is presented as the following steps. Firstly, signal frequency and DOA are roughly estimated by spatial frequencies obtained from reconstruction processing. Then, the estimated frequency and DOA are processed by morphological filtering, which produces precise parameter estimation and network sorting.
3.1. Spatial Frequency Estimation
According to sparse Bayesian theory and (5), the probability density function of amplitudes from output data can be expressed as follows:where denotes the variance of the th frame.
To facilitate notation, intermediate variable is introduced to denote the power spectrum of incidence signal, and where and of different moments are mutually independent, and denotes Gaussian distribution with zeromean variance of . According to Bayesian Probabilistic Theory, the posterior probability density function (PDF) of with respect to can be expressed aswhere and denote the first and second moments of . Given and , and can be estimated as
From (12) and (13), it can be seen that the precision greatly depends on the prior distribution of and estimation of . Precise results can be obtained only when and can correspond to the energy of incidence signal.
To obtain more precisely, the observation data is used for optimization. The likelihood function of with respect to is
Taking logarithm of (14) and omitting constant terms, the target function with respect to iswhere is the estimated array output covariance matrix. By minimizing (15), , the power spectrum of incidence signal on spatial frequency sets , can be obtained.
In practice, it is difficult to optimize directly on (15), so expectation maximization (EM) algorithm is used to estimate . Every iteration of EM includes an Estep and an Mstep. In Estep, the firstorder moment and secondorder moment are estimated by maximizing (11). In the Mstep, the iteration of is estimated by calculating given :where denotes the th column of the matrix after the th iteration and denotes the element on the th row and th column of the matrix after the th iteration, which can be deduced from (12) and (13).
To improve the speed of iteration and avoid exception caused by many zeros in , this iteration can be modified aswhere is a small positive value.
The estimated power can be obtained after th iteration. The number of sources is known to be . The first peaks ordered by amplitude are reserved for further processing. Every peak of the spectrum corresponding to every incidence signal usually contains multiple consecutive nonzero amplitudes because of quantization error of . The spatial frequency can be roughly estimated via the linear interpolation of the main peak and the second largest amplitude beside the peak, as follows:where and denote the spatial frequency of the largest and the second largest value of the th peak in the spectrum , whose power is and , respectively.
Based on the rough estimation above, the spatial frequency estimations in the th iteration can be obtained. The unbiased estimation of corresponding noise iswhere , the notation denotes pseudoinverse operation. When and meet the requirement for convergence, the iteration is terminated, and the power spectrum is estimated to be , which is taken into (12) to estimate the firstorder moment .
3.2. Precise Estimation of Spatial Frequency
The reconstruction in Section 3.1 would lead to estimation error in the range of . To reduce the quantization error, a precise estimator for spatial frequency is given in this section, based on the results of previous reconstruction.
By the algorithm in Section 4(1), the noise variance , the range of spatial frequencies , and the covariance matrix of signal waveform can all be obtained, where and denote spatial frequency for the largest and the second largest peaks in the th peak of the spectrum . Let and . can be obtained by crossing out two rows and two columns corresponding to and . . Equation (15) can be modified as follows in order to estimate spatial frequency precisely:where is the power of the th signal.
The target function for signal power and spatial frequency is
It can be inferred from ,
Taking (22) into , (23) can be deduced, where :
The precise estimation of spatial frequency is as follows:
In practical applications, the peak position can be obtained by searching in the smaller range , so that the spatial frequency can be precisely estimated. The computational complexity of the process above is moderate because it is conducted consecutively for signals; the search range of which is only .
3.3. DOA Estimation and Network Sorting
The spatial frequencies of signals can be obtained by algorithms listed in Sections 3.1 and 3.2. amplitudes can be obtained from peak positions from the estimated firstorder moment , where are signal vectors. The number of samples is small, so phasedifference method is used to estimate frequency for signals, with the following formula:where means calculating the phase of a complex number.
Therefore, the incidence angle can be estimated by with signal frequencies:
The incidence angle and frequency of signals can be obtained from every frame. Considering the spatial incidence angle of the signal in the processing time is basically invariant, those angles are divided into different clusters with known number of sources as . The frequencies corresponding to different spatial incidence angles are also categorized, so that different signals of different networks are sorted.
3.4. Parameter Estimation Based on Morphological Filtering
In the frequency estimation of FH signals, one frame data may include information about two FH frequencies of the same signal; therefore, the estimation results are often in low credulity, since they suffered from great fluctuation in low SNR condition.
Many image filtering algorithms have been proposed to exclude these data points. Among these methods, median filter, wiener filter, and morphological filter are conventional methods to deal with binary image. Median filter is often used to suppress saltandpepper noise and it is a nonlinear filter. But the image we are processing has the feature that there is only one nonzero value in every time index; the median filter method is inefficient with the image of this type. Wiener filter is also a nonlinear filter. Under the criterion of minimum meansquare error, wiener filter is the best filter. Since the wiener filter needs iteration, it has a high computational complexity. morphological filtering method is used to improve the timefrequency spectrum since it has lower computational complexity and it is easier to be realized.
Consider frequency estimates , ; , which is converted to binary image data after quantization by the following formulas:where and denote interval and bandwidth.
The obtained binary image is going through erosion and dilation operation. The scatter points of estimates can be eliminated by erosion, while the boundary can be restricted. The void in the signal can be filled by dilation, but the boundary would also be expanded. The erosion and dilation operations using structural element on the image are expressed aswhere is the domain of .
There is only one nonzero value in each column of the image, and the probability of presence of consecutive points with greater fluctuation is small, so that the structural element is as . By erosion and dilation, outliers can be excluded while normal values are unaffected, generating a clarified timefrequency matrix . The matrix is then turned back to the original estimates by coordinate transformation, with eroded scatter points modified by neighboring points. This transformation can be expressed as follows:where
The modified timefrequency spectrum is turned into binary form and analyzed for component connectivity in order to get the hop moment, period, and frequency sets. The binarization is the same as (27). As the timefrequency points are consecutive by time, connected component detecting could be used to extract every hop of every signal. Eight connected components’ detecting can get a better selection of the hop sequence than four connected components’ detecting for its loose condition. The four connected components and the eight connected components could be seen in Figure 1.
(a) Four connected components
(b) Eight connected components
The sets of points of each hop are , whose maximum and the minimum of time coordinates are and , indicating the moment of FH frequencies. The hop period estimate of the th signal is as follows:
Therefore, the frequency estimate of hop of th signal is
The DOA estimate of th signal is
4. Algorithm Procedure and Computational Complexity
The procedure of joint parameter estimation and network sorting of multiple FH signals is summarized as follows.
(1) Spatial Frequency Estimation Based on Sparse Bayesian Learning
Step 1. Initialize and , where can be solved by the leastsquare solution of signal amplitudes. , , .
Step 2. Update by (12), (13), and (17).
Step 3. Update by (19).
Step 4. Repeat Steps 2 and 3 until the convergence condition is met, and output and .
(2) Precise Estimation of Spatial Frequency
Step 5. Conduct precise estimation of spatial frequency by (24), producing .
(3) DOA Estimation and Network Sorting
Step 6. Estimate the instantaneous frequency and DOA by (25) and (26).
Step 7. Update frame for operation and repeat (1), (2), and Step 6 to obtain the timefrequency spectrum and spacetime spectrum. Put spacetime spectrums into different categories by different number of signals. Frequency and DOA of signal are separated based on the result of categorization.
(4) Parameter Estimation of FH Signals Based on Morphological Filtering
Step 8. The timefrequency spectrum generated by (27) is binarized and morphologically filtered and modified by (28).
Step 9. Detect the connected components of modified binary spectrum and extract the timefrequency point set of each hop. Estimate hop period, frequency, and DOA by (31), (32), and (33).
The computational complexity of the proposed method mainly depends on the heuristic and search algorithm. From the procedure of the algorithm, we can find that the computational complexity of one iteration mainly focuses on Steps 2 and 3. Therefore, it is in the order of , while the computational complexity of the algorithm in [11] is in the order of . It could be easily seen that the computational complexity of the proposed method is lower than the algorithm in [11].
5. Simulation and Analysis
In this section, the proposed method is analyzed by simulation to verify factors contributing to its performance.
Experiment 1. Consider an ULA with eight elements and interelement spacing of 2.5 m. The bandwidth of receiver is . Two farfield FH signals with incidence angle of and are assumed. The hop periods of two signals are 100 us and 50 us, respectively. The frequency set of the first signal is 42 MHz, 35 MHz, 53.5 MHz, 48.7 MHz, with 47 MHz, 53 MHz, 34.3 MHz, 42.5 MHz, 49 MHz, 56 MHz, 39 MHz being the counterpart for the second signal. The total time length of signal procession is 300 us. Two signals are sampled with the rate of 150 MHz, and every operational frame has 100 snapshots.
The simulated timefrequency spectrum and spacetime pattern with SNR = 0 dB are depicted in Figures 2 and 3. Parameter estimates are listed in Tables 1 and 2.


As seen in Figure 2, the frequency estimates fluctuate greatly around the real value, corresponding to greater variance due to fewer number of snapshots in the sparse Bayesian method. By contrast, the estimates after morphological filtering are more accurate as shown in Figure 3.
As seen from Tables 1 and 2, the estimates of parameters of two sources are accurate. In the SNR of 0 dB, the error margin of DOA estimate is within 1° and the error margin of frequency sets is within 50 kHz, which indicates the proposed method is available for practical application.
Experiment 2. Consider an ULA with eight elements and interelement spacing of 2.5 m. The bandwidth of receiver is . FH signals are generated with incidence angles randomly selected in and frequencies randomly selected in . The frequency interval between adjacent two points is greater than 1 MHz. Assume the two frequencies meet at any moment. The periods of two signals are 100 us and 50 us, respectively. The total time length of signal procession is 300 us. To get a convincing result, the signal number is set to be 2 and 3, respectively. And signals are sampled with the rate of 150 MHz, and every frame has 100 snapshots. In every SNR condition from −5 dB to 10 dB by 1 dB, 100 MonteCarlo simulations are performed.
Figures 4, 5, and 6 show the estimation bias under frequency, DOA, and period in different SNR conditions of the proposed method and the particle filtering method in [14]. The bias of signal frequencies, DOAs, and hop period are defined as follows:where and denote the frequency and estimate of the hop of the th signal in the th experiment; and denote the DOA and estimate of the th signal in the th experiment. and denote the period and estimate of th signal in the th experiment. denotes the total number of experiments.
From Figures 4, 5, and 6, it can be seen that when SNR is greater than 5 dB, the bias of frequency and DOA of these two methods are similar, while when SNR is smaller than 5 dB, the proposed method shows much better performance. And the bias of period estimation of these two methods is nearly the same in all SNRs.
6. Conclusions
The reconnaissance and processing of multiple FH signals are challenging tasks in modern warfare. By sparse Bayesian reconstruction method, the parameter estimation and network sorting of multiple wideband FH signals are successfully achieved and precise estimation of parameters in low SNR conditions is conducted by the proposed method. The procedure of the algorithm has been deduced in theoretical analysis. And the excellent performance of the algorithm is verified by simulation.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
This research was supported by the National Natural Science Foundation of China (no. 61601500).
References
 D. J. Torrieri, “Mobile frequencyhopping CDMA systems,” IEEE Transactions on Communications, vol. 48, no. 8, pp. 1318–1327, 2000. View at: Publisher Site  Google Scholar
 A. J. Viterbi, “Spread spectrum communications: myths and realities,” IEEE Communications Magazine, vol. 40, no. 5, pp. 34–41, 2002. View at: Publisher Site  Google Scholar
 X. Liu, N. D. Sidiropoulos, and A. Swami, “Joint hop timing and frequency estimation for collision resolution in FH networks,” IEEE Transactions on Wireless Communications, vol. 4, no. 6, pp. 3063–3073, 2005. View at: Publisher Site  Google Scholar
 L. Zhang, H. Wang, and T. Li, “Antijamming messagedriven frequency hopping – part I. system design,” IEEE Transactions on Wireless Communications, vol. 12, no. 1, pp. 70–79, 2013. View at: Publisher Site  Google Scholar
 L. Zhang and T. Li, “Antijamming messagedriven frequency hoppingpart II: capacity analysis under disguised jamming,” IEEE Transactions on Wireless Communications, vol. 12, no. 1, pp. 80–88, 2013. View at: Publisher Site  Google Scholar
 C. C. Ko, W. Zhi, and F. Chin, “MLbased frequency estimation and synchronization of frequency hopping signals,” IEEE Transactions on Signal Processing, vol. 53, no. 2, pp. 403–410, 2005. View at: Publisher Site  Google Scholar
 K.C. Fu and Y.F. Chen, “Blind iterative maximum likelihoodbased frequency and transition time estimation for frequency hopping systems,” IET Communications, vol. 7, no. 9, pp. 883–892, 2013. View at: Publisher Site  Google Scholar
 S. Barbarossa and A. Scaglione, “Parameter estimation of spread spectrum frequencyhopping signal using timefrequency distributions,” in First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications, pp. 213–216, 1997. View at: Google Scholar
 T.C. Chen, “Joint signal parameter estimation of frequencyhopping communications,” IET Communications, vol. 6, no. 4, pp. 381–389, 2012. View at: Publisher Site  Google Scholar  MathSciNet
 D. Angelosante, G. B. Giannakis, and N. D. Sidiropoulos, “Estimating multiple frequencyhopping signal parameters via sparse linear regression,” IEEE Transactions on Signal Processing, vol. 58, no. 10, pp. 5044–5056, 2010. View at: Publisher Site  Google Scholar
 L. Zhao, L. Wang, G. Bi, L. Zhang, and H. Zhang, “Robust frequencyhopping spectrum estimation based on sparse bayesian method,” IEEE Transactions on Wireless Communications, vol. 14, no. 2, pp. 781–793, 2015. View at: Publisher Site  Google Scholar
 D. Angelosante, G. B. Giannakis, and N. D. Sidiropoulos, “Sparse parametric models for robust nonstationary signal analysis: Leveraging the power of sparse regression,” IEEE Signal Processing Magazine, vol. 30, no. 6, pp. 64–73, 2013. View at: Publisher Site  Google Scholar
 Z.C. Sha, Z.T. Huang, Y.Y. Zhou, and F.H. Wang, “Frequencyhopping signals sorting based on underdetermined blind source separation,” IET Communications, vol. 7, no. 14, pp. 1456–1464, 2013. View at: Publisher Site  Google Scholar
 Z.C. Sha, Z.M. Liu, Z.T. Huang, and Y.Y. Zhou, “Online hop timing detection and frequency estimation of multiple FH signals,” ETRI Journal, vol. 35, no. 5, pp. 748–756, 2013. View at: Publisher Site  Google Scholar
 X. Liu, J. Li, and X. Ma, “An EM algorithm for blind hop timing estimation of multiple FH signals using an array system with bandwidth mismatch,” IEEE Transactions on Vehicular Technology, vol. 56, no. 5, pp. 2545–2554, 2007. View at: Publisher Site  Google Scholar
 W. Fu, Y. Hei, and X. Li, “UBSS and blind parameters estimation algorithms for synchronous orthogonal FH signals,” Journal of Systems Engineering and Electronics, vol. 25, no. 6, pp. 911–920, 2014. View at: Publisher Site  Google Scholar
 S. Xie, L. Yang, J.M. Yang, G. Zhou, and Y. Xiang, “Timefrequency approach to underdetermined blind source separation,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 2, pp. 306–316, 2012. View at: Publisher Site  Google Scholar
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Copyright © 2017 Kunfeng Zhang 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.