# LEO-Based Satellite Constellation for Moving Target Detection
## 1 Introduction  
Spaceborne radar is confronted with low signal-to-noise ratio (SNR) problem in the long-distance detection process. For LEO-based satellite constellation, with the maturity of lightweight, standardized, low-cost development pattern as well as the rapid layout of different types of satellite echoes, i.e., communication, navigation and radar [1, 2], multi-source fusion detection technology becomes a feasible means for reliable target detection, in which multi-station joint detection strategy is adopted to make full use of the advantage of multi-angle energy accumulation. The difficulty of related research lie in how to construct the multi-source fusion detection statistic and further improve the target detection performance. Detection statistics are the key factors to CFAR performance, which are generally divided into the amplitude detection statistic [3] and the likelihood ratio detection statistic [4]. To address multi-source fusion detection issues, many relevant algorithms from different aspects have been discussed. The image reconstruction algorithm [5] is proposed by mining the correlation of oversampling signal between low resolution echo and high-resolution echo, in which the multi-resolution images are unified to the same resolution level, and thus the fusion detection performance degradation caused by resolution mismatch can be effectively avoided. The coherent CFAR detection algorithm [6] is developed by means of the orthogonal waveform to suppress clutter and interference. As a result, the target SNR and its corresponding CFAR detection performance can be improved. In literature [7], the spatial division algorithm under multi-source satellite configuration is demonstrated through constructing objective functions of distributed system, i.e., the path gain and thepositioning accuracy. In this way, the optimal spatial division criteria of key area can be achieved. Another type of typical method also referred as two-level threshold CFAR detection approach [8], is designed to solve the large data transmission problem for centralized detection system, where the local and global detection statistics are obtained by employing the primary threshold and the secondary threshold, respectively. On this basis, the analytical expressions of false alarm probability and detection probability are derived according to the multistage adaptive matched filter. For multiple platforms with different systematic error, the particle filter algorithm [9] is applied to compensate the measurement error and predict the adjacent target position based on the fused measurement information. Additionally, the target tracking accuracy can be significantly improved. Note that the first-order to third-order range migration can be simultaneously corrected by means of the symmetric keystone transform (SKT) without prior information, the multi-station fusion detection method [10] is presented by utilizing the space-ground asymmetric configuration in frequency modulation rate domain. In the aforementioned algorithms, the multi-source information fusion detection methods are realized in the image domain, and thus to improve the target SNR. However, it is difficult to fully exploit the multiple cell characteristics of the fused image, and hence results in the fusion detection performance degradation. To explore the enhanced CFAR detection method adapted to the multi-source fusion echoes, the core problem is how to mine the difference between the target cell and other background cells. In this paper, a novel multi-source fusion detection method based on LEO satellites is proposed, where the basic idea is that multi-source echoes are combined to enhance target detection performance under the condition of LEO satellite platforms. The rest parts are outlined as follows. In Section 2, the theoretical model and the proposed method is discussed in detail, where the performance of pre-compensation process, clutter suppression and multi-source fusion CFAR detection are discussed.