Research On And Implementation Of Automatic Modulation Recognition Of Digital Communication Signals

In modern information-based warfare, automatic modulation recognition ofcommunication signals is extremely important, especially for digital communicationsignals. The signal-to-noise ratio (SNR) is an important index of the quality of theintercepted signals. In order to design a modulation recognition receiver based on theprevious work, the modulation recognition algorithms of communication signals areinvestigated in this thesis, which are mainly on the digital communication signals. TwoSNR estimation algorithms are proposed, and two automatic modulation recognitionalgorithms of narrowband and broad band intercepted signals are given. Furthermore,the proposed algorithms are implemented in the modulation recognition receiver. Themain work of this thesis could be summarized as follows:1. Two SNR estimation algorithms have been proposed.The first algorithm is based on the higher order moments (HOM) of the receivedsignal, which is a semi-blind SNR estimation algorithm. In this algorithm, the receivedsignal is modeled as a deterministic signal corrupted by complex Gaussian noise. Withthe mean-ergodicity properties of the squared and quartic modulus of the sequence, theCramer–Rao bounds (CRBs) of the signal power, noise power as well as the SNRestimation variances are derived. For the non-constant envelope modulated signal, thesignal is required to be divided into a series of successive segments according to thedigital modulation symbol period, resulting in estimating the SNR of a constantenvelope signal. The intermediate frequency (IF) signal at the output of the receiver, isdirectly processed without the knowledge of the modulation type or realizing carriersynchronization. The proposed algorithm is robust to the symbol number and theoversample factor. Simulation result shows that the average estimation standarddeviation is lower than0.18dB (500symbols, oversample factor is100, SNR scopefrom-5dB to25dB).The second algorithm is based on the concept of eigenvector decomposition andsubspace. The proposed approach could accurately estimate a wide range of SNR valuesof the intercepted signal without any a priori knowledge, e.g., the modulation type, thesymbol rate of the received signal, more the carrier synchronization is not required.Computer simulations confirm that the average estimation bias is less than0.1dB (2500symbols, true SNR from5dB to20dB) when the dimension of the autocorrelationmatrix is50. Under the same simulation conditions, the proposed approach performsfaster than the algorithm given above.2. A modulation recognition algorithm based on the decision theory is proposed forthe narrowband modulation types.Firstly, the concept of zero-amplitude (ZA) transformation of a discrete random sequence is given. For narrowband signals, an instantaneous amplitude extractionalgorithm is presented without Hilbert transformation. Directly based on the observedsamples, the proposed algorithm does not need to perform FFT (Fast FourierTransformation, FFT) or IFFT (Inverse Fast Fourier Transformation, IFFT), whichresults in being real time implemented. The symbol synchronization is not necessaryand the algorithm is robust to SNR offset, symbol number and carrier frequencymismatch. Simulation results show that the maximum error is less than0.09when theSNR varies from-20dB to32dB.Secondly, for the digitally modulated types with rectangular baseband pulseshaping, an instantaneous phase and frequency extraction method is proposed whichdoes not need to perform phase unwrapping. Compared to the traditional method whichuses phase unwrapping, this novel approach performs much faster. Simulation indicatesthat under the constraint of10000symbols, the novel method is50milliseconds fasterthan the traditional method, thus it can be implemented in the real time environment.Thirdly, with the knowledge of the instantaneous amplitude, the instantaneousphase, the instantaneous frequency, as well as the ZA transformation of a sequence,12classification feature parameters can be extracted. All of these parameters can becalculated relatively easily by utilizing the conventional signal processing tools. Thisless time-cost procedure requires less a priori knowledge and is powerful in noisesuppression. As a result, this proposed method could also be implemented in real timesystem.Fourthly, by utilizing the12classification feature parameters mentioned above, anautomatic modulation recognition algorithm based on the decision theory is given forthe narrowband communication signals. Computer simulation shows that the averagerecognition rate of the algorithm is over98%(with known carrier frequency andrectangular digital pulse shaping, SNR is3dB,4×104samples). More, the averageclassification time is less than50milliseconds.3. For broad band modulation types, a modulation recognition algorithm ispresented which is based on the spectral coherence function (SCF), the fourth-andeighth-order cyclic cumulants (CCs) of the received signal. First, the semi-blind SNRestimation algorithm is applied so as to evaluate the SNR of the received signal, then aminimum distance criterion is employed to classify the signal. This recognitionalgorithm has the advantage of SCF’s insensitive nature to additive noise and to channeleffects. It does not require a priori knowledge such as carrier frequency, carrier phaseand symbol rate etc. To evaluate its performance, simulations have been performed inGaussian and multipath fading channels, respectively. It demonstrates that the averagerecognition rate of the recognition algorithm in Gaussian channels is above97%(withSNR is10dB and4096samples), and the rate is above90%in multipath fadingchannels with not more than4paths (SNR is10dB and4096samples). 4. A modulation recognition receiver is implemented, the front end of whichemploys broad band IF structure based on band pass sampling to collect data of allkinds of modulated signals. The receiver is operated in three modes, i.e. the real mode,the post processing mode and the off-line analysis mode. In order to evaluate theperformances of the two proposed recognition algorithms, field tests have been carriedout on10digital and analog modulation types. The experiment results have confirmedtheir validity.The proposed automatic modulation recognition algorithms of communicationsignals have been implemented in a novel communication countermeasure equipment.Its excellent performance promotes the finalization of the equipment. Theoreticalanalysis, simulation results and the field tests confirm that the proposed algorithmspresented are not only creative in theory but have great potential.