Models And Detection-Estimation Algorithms For Multi-Component Radar Emitter Signals

Radar emitter signal detection and parameter estimation is a key process of signal processing in electronic intelligence systems and also a focus and difficulty in the signal processing of electronic countermeasure. In modern electronic warfare, the density of signals becomes denser and denser, and radar signals use more and more complex modulations. The dense and complex signal environment produces the situation that radar signal pulses reach receivers coincidently or successively and overlap each other, which forms multi-component radar emitter signals. The existence of multi-component radar emitter signals deteriorates the instantaneous frequency estimation method that uses a single pulse to perform detection, which easily results in estimation errors of instantaneous frequencies or the absence of important information, and therefore destroys statistic characteristics of pulses in a pulse train from one emitter and lowers correct recognition rates, even wrong recognition, of radar emitters. In the case of very limited number of pulses the method greatly affects the intelligence information gain of radar emitters. As the density of pulses grows denser and denser in the electronic warfare environment, the probability that pulses overlap is bigger and bigger. So the detection and estimation of multi-component radar emitter signals caused by overlapped pulses becomes an unavoidable and critical problem in modern electronic reconnaissance systems. Until now there is not a systematic study on detection-estimation algorithms for multi-component radar emitter signals. Therefore, this dissertation carries out a systematic, innovative and deep investigation on the problems of multi-component radar emitter signals and their detection-estimation algorithms to obtain the intelligence information of advanced radar emitters in a dense and complicated signal environment.This dissertation first discusses the meaning and model of multi-component radar emitter signals, on the basis of the analysis of source, characteristics and representation ways of multi-component radar emitter signals, and the present probability of overlapped pulses. Then, time-frequency analysis approaches and parameter estimation methods are used to design several detection-estimation algorithms for multi-component radar emitter signals to gain various useful information of signal components in time and frequency domains and modulation types. The detection-estimation algorithms consider various types of signals and the detection-estimation of multi-component radar emitter signals in the case that there are many overlaps and noise. The detection-estimation algorithms based on time-frequency analysis consist of S-method (SM) detection-estimation algorithm, reassigned SM detection-estimation algorithm and SHE (a hybrid approach of SM, Hough transformation and elimination method in time-frequency plane) detection-estimation algorithm. The detection-estimation algorithms based on parameter estimation is devised to detect the multi-component radar emitter signals composed of signal components with greatly different energies or containing high-order phase modulation signal components. Finally, these detection-estimation algorithms are comparatively analyzed from the three aspects of the algorithm implementation processes, computational complexity and detection-estimation performances, and conclusions are drawn. Main research fruits achieved in the systematic study of multi-component radar emitter signals and their detection-estimation algorithms are as follows:(1) The definition of multi-component radar emitter signals is given and the model of multi-component radar emitter signals is constructed. The investigation of multi-component radar emitter signals is a new and difficult problem in the signal processing of modern electronic countermeasure. The meaning and model of multi-component radar emitter signals is the basis of detection-estimation algorithms. Multi-component radar emitter signals are analyzed from their source, characteristics, representation ways and detection necessity. On the basis of analyzing radar emitter signals, the definition of a multi-component radar emitter signal is provided and the mathematical model of a multi-component radar emitter signal is given. Then, the features of multi-component radar emitter signal detection are discussed from the aspect of information collection and problems to solve, which can provide an important reference for detection-estimation algorithm design.(2) The detection mechanism of SM is deeply analyzed and a SM-based algorithm for detecting multi-component radar emitter signals is proposed. The detection mechanism of SM is studied by analyzing cross-term elimination, noise suppression and detection performance to gain the advantages and disadvantages of SM in the process of detecting multi-component signals. Making use of the characteristics of SM in detecting multi-component signals, a multi-component radar emitter signal detection-estimation algorithm is designed. Simulation experiments show that the SM detection-estimation algorithm has high time-frequency resolution, like pseudo Wigner-Ville distribution, and avoids the interference of cross terms, and that the algorithm is able to accurately detect the instantaneous frequency, starting and ending instants of each signal component and has strong detection ability to process noised signals. The detected values fluctuate around their ideal values, but the fluctuation ranges are small. When the algorithm detects noised signals (signal-to-noise ratio (SNR) is 0 dB), the detected values may violate their ideal values, but this does not affect the approximation of the detected values to their ideal values, thus the algorithm still obtains high estimation precision.(3) A multi-component radar emitter signal detection-estimation algorithm based on reassigned SM (RSM) is presented and its feasibility and implementation efficiency are discussed through algorithm process analysis and hardware realization. Aiming at the characteristics of multi-component radar emitter signals, time-frequency reassign is applied to improve the time-frequency concentration which is decreased by adopting frequency-domain window in the process of avoiding cross-terms of SM. Using the time-frequency reassign, the signal components in the SM distribution plane are detected more easily, and better estimation precision can be obtained. The analysis of algorithm implementation process and hardware realization shows that the main computing process of this detection-estimation algorithm is Fourier transform of the signal segment within a time-domain window and the detection-estimation algorithm is relatively simple and easy to realize for hardware. Results of simulation experiments show that RSM detection-estimation algorithm uses SM to remove cross-terms between signal components and applies time-frequency reassign to enhance time-frequency resolution. Thus, RSM detection-estimation algorithm can obtain high time-frequency resolution close to Wigner-Ville distribution and avoid the disturbance of cross-terms that Wigner-Ville owns. Also RSM detection-estimation algorithm can effectively detect both linear and non-linear frequency-modulated multi-component radar emitter signals.(4) A detection-estimation algorithm, SHE, is proposed to detect multi-component radar emitter signals. The model of SHE detection-estimation algorithm is given. An elimination method performed in the time-frequency plane is introduced to detect signal components one by one. SHE appropriately combines SM, Hough transform with time-frequency elimination method. On the basis of the computation of SM time-frequency distribution of a signal, the noise suppression ability is improved by accumulating the linear frequency modulation signals or phase encoding signals in the parameter space of Hough transform. The introduced time-frequency elimination method is employed to detect signal components one by one, which can avoid the disturbance between signal components and multi-valued detection in the signal location of Hough transform. The starting and ending time of signal components is obtained by using a pulse detection threshold. The phase codes can be achieved by using a phase detection threshold. Simulation experiments show that SHE is able to accurately detect the instantaneous frequency and the duration time of each signal component, and that SHE can still achieve high estimation precision for the signals with low SNR (SNR is-5dB). Also the experiments show that the phase coding information of signal components can be gained by using phase elimination approach in the time-frequency plane.(5) A multi-component radar emitter signal detection-estimation algorithm based on Product High Ambiguity Function (PHAF) is proposed. In this algorithm, a novel strategy like peeling onion is presented to detect signal components sequentially according to their energy magnitudes. The algorithm can effectively detect the multi-component radar emitter signals containing high-order phase modulation signal components or weak energy signal components. Through multiplying several high ambiguity functions with different time-delays, the proposed detection-estimation algorithm can strengthen the useful signal components and weaken noise and irrelative cross-terms to avoid the case that High Ambiguity function (HAF) is easily affected by noise and clutter waves formed by cross-terms when HAF is employed to process multi-component signals. Also, the detection-estimation algorithm reduces high computing complexity due to the use of a maximum likelihood estimation method. The onion-peeling strategy in the detection-estimation algorithm is helpful to detect the signal components with weaker energy because it eliminates the side-effects of signal components with stronger energy and cross-terms. Results of simulation experiments show that the presented algorithm can detect the multi-component radar emitter signals with various order phase modulations effectively. The detection order of signal components depends on the energy (amplitude) of signal component, so the signal components with stronger energy are first detected and therefore they do not affect the detection of other signal components. The detection order of the signal components with various orders of phase is not relative to their orders of phase. The estimation precision of parameters relates the energy of signal components. The stronger the signal components are, the higher the estimation precision is. When noised signals (SNR is OdB) are detected, the estimation error increases only in a small scale and there is not significant decrease for estimation precision. (6) The performances of several multi-component radar emitter signal detection-estimation algorithms based on time-frequency distributions and parameter estimation are comparatively investigated. The application scopes, advantages and disadvantages of the detection-estimation algorithms are analyzed. Performance indexes for the detection-estimation algorithms are summarized. In the process of analyzing the implementation procedure of algorithms, a fast time-frequency analysis method based on SM is proposed to deal with multi-component radar emitter signals. The performances of detection-estimation algorithms are analyzed through algorithm implementation process, computing complexity and detection capability. The proposed fast time-frequency analysis method is used to decrease the computing complexity of time-frequency detection-estimation algorithms. The analysis of computing complexity of detection-estimation algorithms shows that the parameter estimation detection algorithm is better than the time-frequency detection-estimation algorithms. Among the time-frequency detection-estimation algorithms, the computing complexity of SHE is the highest and RSM detection-estimation algorithm is slightly worse than SM detection-estimation algorithm. The detection-estimation performance, mainly considering detection precision, noise suppression and application scope, is an index to evaluate the effectiveness of detection-estimation algorithms. The performance analysis of time-frequency detection-estimation algorithms is based on time-frequency distributions. The detection-estimation performance of parameter estimation detection algorithm is analyzed through computing the variance of estimation values and Cramer-Rao Bound. The detection-estimation performance analysis shows that RSM has higher time-frequency resolution than SM because RSM inherits the good points of SM about noise suppression and cross-terms elimination, so RSM is an ideal time-frequency distribution. The analysis also shows that SHE has better detection ability than other algorithms in processing the signals with low SNR, and that PHAF detection-estimation algorithm has the best detection ability in processing the signals with high order phase modulations and can detect multi-component radar emitter signals with mixed order phase modulations or weaker energy signal components. After summarizing the performance of detection-estimation algorithms, a table containing the performance indexes for comparing detection-estimation algorithms is provided to choose the detection-estimation algorithm for different situations and also to be a basis of further discussion and application research of multi-component radar emitter signals. This work is supported by the National Natural Science Foundation of China (60971103,60702026,60572143), Scientific and Technological Funds for Young Scientists of Sichuan (09ZQ026-040), and the Doctorial Innovation Foundation of Southwest Jiaotong University.