Detection Methods Of Manoeuvring Targets In Narrowband And Wideband Radars

The detection for manoeuvring targets in narrowband radar or wideband radar is a difficult problem in radar detection applications, and becomes attractive in the radar engineering domain. In the narrowband radar, it takes a long duration for a target to move across a range cell, because the range cell is much longer compared with the target's length. A long dwelling duration is helpful for detecting weak target in noise or clutter. However, unavoidable target manoeuvring in a long dwelling duration gives rise to the trouble in target detection, and the phase histories of the echoes become more complex. Thus, the conventional Fourier analysis can't integrate the target's echoes effectively. In the work of this dissertation, the Fractional-order Fourier transform are utilized to integrate the target's echoes with complex phase history effectively, and the better performance is obtained.In the wideband radar system, because of the high resolution ability of the wideband radar, the“point target”model in the narrowband case is not suitable for the wideband case. Wideband high-resolution radars (HRRs) can spatially resolve a target into a number of scattering centers depending on the range extent of the target and the range resolution capability of the radar. In this case, the target is named as“range spread target”. Because of the target's manoeuvring, range walking happens unavoidably between different pulses. Moreover, because of the target's rotation and target scatterers's recombination, the phases of target's echoes in different range cells become desultory, which cannot be well modeled. Thus, due to the absence of precise model, the research for the wideband manoeuvring target detection is focused on the character detection of the multi-pulses range profiles, and a series of practical effective detectors are proposed.The main content of this dissertation is summarized as follows:In the first part, the problem of the narrowband manoeuvring target detection is discussed. Then, an overview of the available detection method is given. In terms of the shortcomings of the detectors available, we propose a novel nonparametric detection method for weak target in strong noise, based on the Fractional Fourier transform, denoising methods in image domain and double-characters detector. The detection procedure is consisted of three steps. First, an image is constructed by the Fractional Fourier transforms of successive angles in one period. Then, the threshold procedure is utilized to transform the image into a binary image. After the multiple median filtering, the binary image is refined where the isolated noise pixels are removed. Finally, two complementary features are extracted from the refined image, and a double-characters detector is proposed to decide whether the target is present or not. The simulation experiments of four different typical signals show that the proposed detection method is more effective than other detectors, and the detection performance is more robust.In the second part, a character detection method for the range spread target in the white Gaussian noise is proposed. The proposed detection scheme is a general framework of range spread target detection, which is based on the geometric characters in the Wigner-Ville distribution (WVD) of two adjacent mixer outputs. The method is suitable for the case that stretch linear frequency modulated signal is transmitted. The cross time-frequency distribution is utilized to reveal the time-frequency feature of target's echoes. Two simple image processing tools (thresholding procedure and morphological filtering) are used to abstract the time-frequency structure corresponding to the target. This method is applicable to the case that range walking happens, and has no limitation of the prior information, such as the scattering density, target types and the energy level of the Gaussian noise. Because of the combination of the energy information and the special time-frequency structure information, the proposed method obtains good detection performance.In the third part, the modified cross correlation matrix (MCOM) detector is proposed to detect the range spread target in the white Gaussian noise using multi-pulses range profiles. It is composed of sigmoid-type shrinking map for noise suppression and cross correlation integration of refined high resolution range profiles (HRRPs). Based on the fact that strong scattering cells are sparse in target HRRPs, nonlinear shrinkage maps are designed to refine received HRRPs prior to integration. By which, most of noise-only cells in received HRRPs are suppressed while strong scattering cells most probably relevant to target signature are preserved. Since target's scattering geometry is almost unchanged except range walking during integration, the refined target HRRPs from consecutive pulses are highly similar while refined noise-only HRRPs are dissimilar due to randomicity. The modified correlation matrix of multiple refined HRRPs is used to measure their similarity. The test statistic, a weighted integration of the entries of the modified correlation matrix, is constructed for target detection. The proposed detector does not depend on strict target return model and can work in mild conditions. The real target data and simulated noise are used to evaluate the detector and the experimental results show that it achieves better detection performance than some existing methods.In the fourth paper, a practically efficient method is proposed to detect range-spread targets of maneuvering flight in white Gaussian noise. The proposed detector consists of a two-dimensional (2D) nonlinear shrinkage map for noise reduction followed by a geometric average (GA) integrator. High resolution range profiles (HRRPs) from multiple consecutive pulses are stacked into a 2D grayscale range-pulse image along range cells and pulses. Based on the local statistics of the images, a 2D nonlinear shrinkage map is designed for reducing noise while preserving target echoes before integration. Later, a GA integrator operating on filtered HRRPs, relevant to the high-order cross-correlation, is designed for target detection. The GA integration exploits the waveform similarity of the filtered target HRRPs and the sparsity and position randomicity of the nonzero cells in the filtered noise-only HRRPs. The mechanism of the detector is analyzed with means of raw radar data. Finally, the experimental results of raw radar data and comparisons with other detectors are reported, showing that the proposed detector can efficiently detect range-spread targets of maneuvering flight in white Gaussian noise.In the fifth part, a waveform-cross-entropy (WCEN)-based detection scheme is proposed to detect manoeuvring range-spread targets in homogeneous weather clutter. The input of the detector is composed of complex-valued high resolution range profiles (HRRPs) in an observation window received from a train of coherent pulses. The observation window contains a detection window and reference range cells around it, where the received data in reference range cells is clutter-only secondary data. The detector consists of temporal approximate whitening filtering for clutter suppression followed by the waveform-cross -entropy-based detection. The former is operated on the received vector at each range cell to whiten the clutter and enhance target returns. In the latter, the WCEN of each pair of filtered power-HRRPs is calculated to capture their sparsity, similarity and energy and then the average of the WCENs of all the pairs is used as the test statistic to decide whether a target is present or not. Finally, we assess the proposed detector by the raw radar target data collected by high range resolution radar and simulated weather clutter. The experimental results show that the...