Research On Radar Signal Level Fusion Imaging Techniques

Radar signal level fusion imaging (RSLFI) is an emerging radar imaging technology. It is a combination of multi-sensor signal level fusion and radar imaging technology. It aims at improving the estimation precision of scattering center parameters and the resolution of radar imaging through the coherent fusion of the radar observations information from the different time, space and frequency. It is also in favor of target recognition and image comprehension. It is known that the resolution of traditional monostatic radar imaging system is restricted by the signal band width and observations coherent accumulate time. With the development of radar and information processing technology, the signal level fusion imaging based on the multi-radar observations becomes an effective method of overcoming the limitations of the monostatic radar imaging system. The information fusion based on the multi-radar observations can obviously improve the radar imaging resolution and the scattering center parameters estimation precision.Aiming at the background of inverse synthetic aperture radar (ISAR) imaging, this dissertation studied some basic problems and imaging algorithms in multi-radar signal level fusion imaging processing, including the theoretic frame, amplitude-phase compensation, high resolution range profile (HRRP) formation based on the multiple frequency band radar observations, supper-resolution range profile formation based on the sparse multiple subband radar observations and two-dimensional signal fusion supper-resolution ISAR imaging.The theoretic frame of multiple frequency band and multi-radar signal fusion imaging was presented firstly in the dissertation. Based on the research on the returned signal model of multi-radar fusion, a uniform amplitude-phase error (APE) model was founded. The relationship between residual phase error and the baseline of the two radars, the target size and the observation geometry were analyzed. The effects of residual phase error on the range profile formation were discussed and three modes of radar distribution were presented in order to reduce the residual phase error. The essential theory of multiple frequency band and multi-radar signal fusion imaging was also studied. After analyzing of the effects of the non-uniform sampling on amplitude-phase compensation and range profile formation based on the signal fusion, the uniform resample processing method was proposed.Based on the amplitude-phase error compensation (APEC) model of returned signal, two algorithms were presented. The parameters estimation of the algorithms utilizes the subspace characteristic of signal model and requires the overlap frequency observations of the two radars. Because the longer the signal extrapolated length is, the bigger the signal errors aroused will be. In order to reduce the effect of the signal extrapolated error on the APEC, an APEC algorithm based on the entropy-minimization principle was proposed. The algorithm needn't the overlap frequency observations and can effectively decrease the signal extrapolated length.With the analysis for the known scattering models and the non-stationary characteristic of radar signal, a HRRP formation method of multiple frequency band radar signal fusion was proposed based on the non-stationary time sequence processing. Compared with the HRRP formation algorithm based on the Prony model and autoregressive integrated moving average (ARIMA) model, the time-variant autoregressive (TVAR) model based algorithm has better fusion imaging result than others. In order to express the different components of the radar returned signal, the multi-resolution characteristic of range profile was analyzed and a HRRP formation algorithm based on the dual tree complex wavelet transform (DT CWT) and TVAR was presented. The algorithm builds the different non-stationary time sequence model for different scale signal based on the multi-resolution analysis and non-stationary modeling. It can better express the local characteristic of the returned signal.Under the sparse multiple subband observations condition, the known HRRP formation algorithms based on the signal fusion can not be applied because the blank between the two radar frequency bands is great. So a uniform parameter model for APEC and scattering center parameters estimation was presented. The estimation method of the model parameters based on the space spectrum estimation was studied. The effect of the spatial smoothing on the parameters estimation was analyzed and two spatial smoothing methods were proposed to reduce the complexity of parameters estimation. For the convenience of parameters estimation, a supper-resolution imaging algorithm based on the one dimension search and the estimation of signal parameters via rotational invariance technique was presented. The estimation of linear phase error and constant phase error were decoupled through constructing the special spatial steer vector matrix in the algorithm. The improvement function of radar signal level fusion on the resolution of radar imaging was analyzed with Rayleigh criterion and Cramer-Rao bound (CRB) for supper-resolution imaging algorithm. The CRB of scattering center parameters estimation were derived in monostatic radar imaging and multi-radar signal level fusion imaging. The effects of various factors on the resolution were analyzed and compared.Finally, the diversity of monostatic radar imaging and dual radar signal level fusion imaging was analyzed in wavenumber space. With the coplanarity imaging condition, the preprocessing technology of dual radar signal fusion imaging was studied. A reference distance matching method was proposed to remove the linear phase error and simplify the estimation of the angle between the two radars' line-of-sight. A new target rotation parameter estimation method was also put forward for the dual radar signal fusion. Based on the signal level fusion imaging model of the dual radar, a half-decoupled supper-resolution ISAR imaging algorithm with dual radar observations fusion was presented. Subsequently, a unite supper-resolution ISAR imaging algorithm with dual radar observations fusion was proposed in order to take full advantage of multiple frequency band to improve range resolution. The simulation results show that the two algorithms have much better estimation precision and robustness than monostatic radar imaging algorithm for the target scattering center parameters.