Study On Background Cognition And Target Detection Techniques For High Frequency Surface Wave Radar

High Frequency Surface Wave Radar (HFSWR) is widely used in both millitary and civilian areas such as sea state sensing, surface target detection and the surveilance of the Exlusive Economic Zone (EEZ) for its excellent capability of long range, all weather and real-time surveilance. Its mechanism utilizing a vertical polarization electromagnetic wave that follows the curvature of the earth along the air-water interface with low propagation loss on highly conductive ocean surface makes ship target detection in HFSWR different from that in microwave radar. Clutter such as sea echo, ionospheric clutter, radio frequency interference, meteor, and environment noise cause the complexity and diversity of the detection environment. Multi-source scatter makes homogenous and non-homogenous echoes coexistent which results in the detection background consists of not only Gaussian clutter but also unknown, time-variant, and fluctuated non-Gaussian clutter. All of these lead to great difficulties in ship target detection for HFSWR.Thus, by deeply analyzing the detection environment, this dissertation summarizes detection characteristics and difficulties of HFSWR. Then an architecture of background cognitive information processing system for HFSWR is presented according to these requirements. The system consists of background cognitive information extraction (namely detection scene analysis) and multiple strategies/parameters detection. The former extracts and cognizes the environment information of the detection background by characteristics extraction and model establition, which includes clutter identification, detection region segmentation and evaluation, detection background classification and statistical analysis. The latter utilizes the background cognitive information for selecting proper detection strategies or setting parameters to optimize the detection performance. This dissertation is composed of the studies on the above-mentioned key techniques, and the outline is as follows:1. First-order sea clutter (namely Bragg peaks) plays an important role in HFSWR. On the one hand, its location paramter can be inversion tools for sea state sensing. On the other hand, its quasi-target features often result in false alarm and missing alarm in ship target detection. To solve these problems, this dissertation proposes an algorithm to identification single and splitting first-order sea cluter in HF band which combines characteristic knowledge with the location indicative information extracted from ridge feature of Bragg peaks in Range-Doppler (RD) map. Real data experiments show that the proposed algorithm, comparing with the classical peak detection method and the characteristic- knowledge-based method, can have a better identification performance even in the environment with ship targets as interference or in the strong/weak current shear condition.2. A detection region segmentation method is intorduced based on maximizing the separability of the resultant classes. This method uses statistics of the detection background in RD map to segment the detection region into four parts: strong scattering area, medium scattering area, weak scattering area and reference noise area. One of its most significant applications is to identificate the spread E, F layer ionospheric clutter. Wide range covering, strong intensity, time-variant, flucuated and irregular distribution of the spread E, F layer ionospheric clutter badly affects the system performance of HFSWR. A spread E, F layer ionospheric clutter identification method is proposed based on the region segmentation results and region characteristics of the clutter. First of all, convolution template is used for locating the edge of the clutter, then the ratio of the number of the samples belonging to some segmented region and the total number of the samples in the region of interest is used for setting the determinative threshold of the clutter region. Experiments manifest that the proposed method can describe the effect of the spread ionospheric clutter to HFSWR. The quantitative analysis is consistent with the real data observation. The result can be used as a worthwhile reference for clutter mitigation, carrier frequency selection or radar system evaluation.3. Detection background classification is achieved by combining the region segmentation results and the physical features of the detection background. Unary non-linear regression analysis method is utilized for pre-detection which elimites the non-homogenous samples in the detection background. Then, statistical analysis is operated on the different types of the detection background. Experiments with real data show that the statistical distribution in the pre-detected non-atmospheric noise background follows Weibull distribution with different shape parameters with a better likelihood than that without pre-detection. While the statistical distribution information of atmospheric noise background consisting of single component can be got without pre-detection. The result is an important basic for multiple strategies/parameters detection method.4. Background-cognitive-information-based multiple strategies/ parameters detection method for HFSWR is proposed. Multiple strtegies/parameters detection method is implemented by utilizing on-line accessed detection scene information, including clutter elimination, detection region segementation, detection background classification, statistical distribution estimation, peak detection, and 2-parameter normalization of Weibull distribution, etc. Selecting proper detection stategies/ parameters according to the different characteristics of the background help this method reduce the detection loss which often happens in the classical detectors because of model mismatch. Monte Carlo simulation with sythetic targets in real data as background is operated and experiments with real data is used for further verifying the performance of the algorithm. Comparing the proposed method with the classical and latest HF detection algorithms, it's found that the background-cognitive-information-based multiple strategies/paramters method has a better capability of detecting weak targets within a proper false alarm rate.In summary, compared with the classical and the Knowledge-Based (KB) detector, the proposed methods can extract the information of detection environment on-line by a single type of sensor. The capability of being aware of target's surrounding environment is improved which is the decision basis for multiple strategies/parameters detection in different environment and can provide effective help in detection optimization, system performance evaluation and intelligent management.