Study Of Distributed Constant False Alarm Rate (CFAR) Detection With Multisensor

Distributed constant false alarm rate (CFAR) detection with multisensor is the combination of CFAR processing and distributed detection with multisensor. It possesses both of their advantages. Many researchers have studied the two fields deeply and respectively. Due to their depth and scope, there are still many problems to be unsolved in the field of their combination. This dissertation gives the deep studies about some of these problems. It is mainly considered that distributed CFAR detection in parallel topology is used to detect Swerling II fluctuating target in Gaussian background. A series of distributed CFAR detection algorithms are proposed and are analyzed. The main contents are as follows:The performance of the centralized CFAR detection is analyzed under the condition of varying p, the ratio between noise levels in local sensors. The analytic expression of performance of the centralized OS-CFAR detection is derived for p 1 and local signal-to-noise ratio (SNR) in nonhomogenous background. Result shows that the centralized CFAR detection is heavily influenced by the variation and mismatching of p, the CFAR processing should be accomplished in local processing. A new local multilevel quantization scheme is proposed, which is suitable for the distributed CFAR detection. It is shown to be superior over the common binary quantization. The upper limit of performance in Neyman-Pearson sense of the distributed CFAR detection based on local multilevel quantization is studied. Where, the correlation between samples of test cell in local processors is considered. The necessity of the distributed CFAR detection based on local test statistic (LTS) is discussed.Three new types of LTS are proposed. The distributed CFAR detection based on these new LTSs are developed. They are CA-R-SUM and OS-R-SUM, CA-S-SUM and OS-S-SUM, Max-OS and Min-OS, Max-CA and Min-CA. Their analytic expressions of detection performance are derived for p and in nonhomogenous background. For the R type LTS, the likelihood ratio (LR) fusion and generalized likelihood ratio (GLR) fusion are studied. A method of estimating local SNR is proposed. The above schemes are analyzed under the conditions of p 1 and and many mismatching case of p. Results show that the scheme based on S type LTS is a better choice for p = (such as OS-S-SUM). In the environment of varying or unknown p, the R type scheme isn't influenced by p atall. Hence, it is the best choice. However, the centralized CFAR detection and Min-OS are heavily influenced by the variation and mismatching of p . Therefore, CFAR should be accomplished in local processing. If the communication bandwidth is considered, The R and S type schemes are better than the P type scheme.The distributed CFAR detection is studied under the condition of noncoherent integration of multiple pulse. The analytic expression of detection performance of the distributed OS-CFAR detection is derived for video integration of multiple pulse in nonhomogenous background. Result shows that the distributed CFAR detection is still necessary for noncoherent integration of multiple pulse. Two LTSs suitable for distributed CFAR detection with multiple pulse integration are proposed. Their analytic expressions are derived. Results show that these new schemes obtain improvement over the binary integration and video integration.The distributed detection with feedback is analyzed. A new feedback scheme (FTFC, feedback to fusion center) is proposed. The expression of its detection performance and feedback step is derived. A theme about its convergence property of detection performance is proven. Compared with the old FTLP scheme, FTFC lowers the requirement about communication bandwidth. However, the new scheme possesses similar performance and convergence speed to FTLP.A theme is founded about the proof of CFAR property using the concept of invariant test. The common invariant statistics and invariant distributions are discussed in radar detection background. It involves many typical CFAR detection algorithms i...