基于對數(shù)行列式散度與對稱對數(shù)行列式散度的高頻地波雷達(dá)目標(biāo)檢測器
doi: 10.11999/JEIT181078 cstr: 32379.14.JEIT181078
-
1.
哈爾濱工業(yè)大學(xué)電子與信息工程學(xué)院 ??哈爾濱 ??150001
-
2.
對海監(jiān)測與信息處理工業(yè)和信息化部重點實驗室 ??哈爾濱 ??150001
基金項目: 國家自然科學(xué)基金(61701140, 61571159, 61171182)，中央高校基本科研業(yè)務(wù)費專項資金(HIT.MKSTISP.2016 13, HIT.MKSTISP.2016 26)
High Frequency Surface Wave Radar Detector Based on Log-determinant Divergence and Symmetrized Log-determinant Divergence
-
1.
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
-
2.
Key Laboratory of Marine Environmental Monitoring and Information Processing, Ministry of Industry and Information Technology, Harbin 150001, China
Funds: The National Natural Science Foundation of China (61701140, 61571159, 61171182), The Fundamental Research Funds for the Central Universities (HIT.MKSTISP.2016 13, HIT.MKSTISP.2016 26)
-
摘要: 高頻地波雷達(dá)(HFSWR)利用電磁波繞射原理進(jìn)行目標(biāo)探測，具有超視距的特性。然而，探測距離的增加會使得雷達(dá)目標(biāo)回波能量減弱，進(jìn)而使得雷達(dá)探測能力下降。為了改善高頻地波雷達(dá)的探測性能，該文提出了一種基于信息幾何理論的局域聯(lián)合矩陣恒虛警率(CFAR)檢測器，利用信號在角度、多普勒速度和距離的多維信息進(jìn)行檢測；并使用對數(shù)行列式散度(LDD)和對稱對數(shù)行列式散度(SLDD)代替黎曼距離(RD)作為距離度量。最后，實驗結(jié)果驗證了該文提出的檢測器能夠有效地改善雷達(dá)對目標(biāo)的檢測性能。
-
關(guān)鍵詞:
- 高頻地波雷達(dá) /
- 目標(biāo)檢測 /
- 信息幾何 /
- 對數(shù)行列式散度
Abstract: High Frequency Surface Wave Radar (HFSWR) utilizes electromagnetic wave diffracting along the earth to detect targets over the horizon. However, the increase of target distance decreases the received echo energy, and this degrades the detection capability. A joint domain matrix Constant False Alarm Rate (CFAR) detector is proposed to improve the detection performance. It employs the multi-dimensional information of signal in azimuth, Doppler velocity and range domain to detect target, and Log-Determinant Divergence (LDD) and Symmetrized Log-Determinant Divergence (SLDD) are used to replace the Riemannian Distance (RD) as the measure of distance. Finally, the experiment results show that the detector presented by the paper can improve the detection performance effectively. -
表 1 不同距離度量方法及其幾何均值
度量方法 距離計算 幾何均值 RD ${{{D}}_R}^2({{{R}}_1},{{{R}}_2}) = {\rm{tr}}[{\lg ^2}({{{R}}_1}^{ - 1/2}{{{R}}_2}{{{R}}_1}^{ - 1/2})] $ ${\bar {{R}}_{t + 1}} = \bar {{R}}_t^{1/2}\exp \left[{\rm{ds}} \cdot \frac{1}{N}\mathop \displaystyle\sum \limits_{i = 1}^N \lg (\bar {{R}}_t^{ - 1/2}{{{R}}_i}\bar {{R}}_t^{ - 1/2})\right]\bar {{R}}_t^{1/2}$ LDD ${{{D}}_{\rm{LD}}}({{{R}}_1},{{{R}}_2}) = {\rm{tr}}({{R}}_2^{ - 1}({{{R}}_1} - {{{R}}_2})) - \ln \det({{R}}_2^{ - 1}{{{R}}_1})$ $\bar {{R}} = {\left( {\frac{1}{N}\mathop \sum \limits_{k = 1}^N {{R}}_k^{ - 1}} \right)^{ - 1}}$ SLDD $\begin{aligned} {{{D}}_{{\rm{SLD}}}}({{{R}}_1},{{{R}}_2}) =& \frac{1}{2}({\rm{tr}}({{R}}_2^{ - 1}({{{R}}_1} - {{{R}}_2})) - \ln \det({{R}}_2^{ - 1}{{{R}}_1}) \\ & + {\rm{tr}}({{R}}_1^{ - 1}({{{R}}_2} - {{{R}}_1})) - \ln \det({{R}}_1^{ - 1}{{{R}}_2})) \\ \end{aligned} $ $\bar {{R}} = {\left( {\frac{1}{N}\mathop \sum \limits_{k = 1}^N {{R}}_k^{ - 1}} \right)^{ - 1}}$ 
下載: 導(dǎo)出CSV
表 2 基本矩陣運算的復(fù)雜度
矩陣運算 表達(dá)式 浮點數(shù)計算次數(shù) 矩陣運算 表達(dá)式 浮點數(shù)計算次數(shù) 矩陣乘法 ${{{R}}_1}{{{R}}_2}$ $8{n^3} - 2{n^2}$ 矩陣求逆 ${{R}}_1^{ - 1}$ $8{n^3} - 2{n^2}$ 矩陣加法 ${{{R}}_1}{{ + }}{{{R}}_2}$ $2{n^2}$ 矩陣開方 ${{R}}_1^{1/2}$ $24{n^3} + 2{n^2} - 8n$ 矩陣的跡 ${\rm{tr}}({{{R}}_1})$ $8{n^2} - 6n - 2$ 矩陣指數(shù) $\exp ({{{R}}_1})$ ${n^4}/2 + 24{n^3} + 1.5{n^2} - n$ 矩陣行列式 $\det ({{{R}}_1})$ $8{n^2} - 2n - 6$ 矩陣對數(shù) $\lg ({{{R}}_1})$ ${n^4}/2 + 25{n^3} + {n^2} - 1.5n$
下載: 導(dǎo)出CSV
表 3 不同距離度量方法的復(fù)雜度
度量方法 距離計算復(fù)雜度 幾何均值計算復(fù)雜度 RD ${n^4}/2 + 73{n^3} + 5{n^2} - 15.5n - 1$ $(N + 1){n^4}/2 + (41N + 88){n^3} - (N + 6.5){n^2} - (1.5N + 9)n$ LDD $16{n^3} + 14{n^2} - 8n - 6$ $8(N + 1){n^3} - 2{n^2}$ SLDD $32{n^3} + 28{n^2} - 15n - 11$ $8(N + 1){n^3} - 2{n^2}$
下載: 導(dǎo)出CSV
-
姚迪, 張鑫, 楊強(qiáng), 等. 基于空間多波束的高頻地波雷達(dá)電離層雜波抑制算法[J]. 電子與信息學(xué)報, 2017, 39(12): 2827–2833. doi: 10.11999/JEIT170477YAO Di, ZHANG Xin, YANG Qiang, et al. Ionospheric clutter suppression algorithm based on space multibeam for high frequency surface wave radar[J]. Journal of Electronics &Information Technology, 2017, 39(12): 2827–2833. doi: 10.11999/JEIT170477 WANG Yiming, MAO Xingpeng, ZHANG Jie, et al. Detection of vessel targets in sea clutter using in situ sea state measurements with HFSWR[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(2): 302–306. doi: 10.1109/LGRS.2017.2786725 楊強(qiáng), 劉永坦. 復(fù)雜背景下的二維檢測研究[J]. 系統(tǒng)工程與電子技術(shù), 2002, 24(1): 34–37. doi: 10.3321/j.issn:1001-506X.2002.01.010YANG Qiang and LIU Yongtan. 2-D detection in complex background[J]. Systems Engineering and Electronics, 2002, 24(1): 34–37. doi: 10.3321/j.issn:1001-506X.2002.01.010 TURLEY M D E. Hybrid CFAR techniques for HF radar[C]. Radar 97, Edinburgh, UK, 1997: 36–40. LI Yang, ZHANG Ning, and YANG Qiang. Characteristic-knowledge-aided spectral detection of high frequency first-order sea echo[J]. Journal of Systems Engineering and Electronics, 2009, 20(4): 718–725. WANG Hong and CAI Lujing. On adaptive spatial-temporal processing for airborne surveillance radar systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(3): 660–670. doi: 10.1109/7.303737 ZHANG Xin, SU Yanhua, YANG Qiang, et al. Space-time adaptive processing-based algorithm for meteor trail suppression in high-frequency surface wave radar[J]. IET Radar, Sonar & Navigation, 2015, 9(4): 429–436. doi: 10.1049/iet-rsn.2014.0300 衣春雷. 船載高頻地波雷達(dá)海雜波抑制方法研究[D].[博士論文], 哈爾濱工業(yè)大學(xué), 2017.YI Chunlei. Study on sea clutter suppression for shipborne high frequency surface wave radar[D]. [Ph.D. dissertation], Harbin Institute of Technology, 2017. RADHAKRISHNA RAO C. Information and the accuracy attainable in the estimation of statistical parameters[J]. Bulletin of the Calcutta Mathematical Society, 1945, 37(3): 81–91. WANG Meng, NING Zhenhu, XIAO Chuangbai, et al. Sentiment classification based on information geometry and deep belief networks[J]. IEEE Access, 2018, 6: 35206–35213. doi: 10.1109/ACCESS.2018.2848298 黎湘, 程永強(qiáng), 王宏強(qiáng), 等. 雷達(dá)信號處理的信息幾何方法[M]. 北京: 科學(xué)出版社, 2014: 13: 18. ZHANG Fode, NG H K T, and SHI Yimin. Information geometry on the curved q-exponential family with application to survival data analysis[J]. Physica A: Statistical Mechanics and its Applications, 2018, 512: 788–802. doi: 10.1016/j.physa.2018.08.143 TRANSTRUM M K, SARI? A T, and STANKOVI? A M. Information geometry approach to verification of dynamic models in power systems[J]. IEEE Transactions on Power Systems, 2018, 33(1): 440–450. doi: 10.1109/TPWRS.2017.2692523 HIAI F and PETZ D. Riemannian metrics on positive definite matrices related to means. II[J]. Linear Algebra and Its Applications, 2012, 436(7): 2117–2136. doi: 10.1016/j.laa.2011.10.029 劉俊凱, 王雪松, 王濤, 等. 信息幾何在脈沖多普勒雷達(dá)目標(biāo)檢測中的應(yīng)用[J]. 國防科技大學(xué)學(xué)報, 2011, 33(2): 77–80. doi: 10.3969/j.issn.1001-2486.2011.02.018LIU Junkai, WANG Xuesong, WANG Tao, et al. Application of information geometry to target detection for pulsed-Doppler radar[J]. Journal of National University of Defense Technology, 2011, 33(2): 77–80. doi: 10.3969/j.issn.1001-2486.2011.02.018 STEIN C. Inadmissibility of the usual estimator for the mean of a multivariate normal distribution[C]. The Third Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, USA, 1956: 197–206. HUA Xiaoqiang, CHENG Yongqiang, WANG Hongqiang, et al. Geometric means and medians with applications to target detection[J]. IET Signal Processing, 2017, 11(6): 711–720. doi: 10.1049/iet-spr.2016.0547 LENGLET C, ROUSSON M, DERICHE R, et al. Statistics on the manifold of multivariate normal distributions: theory and application to diffusion tensor MRI processing[J]. Journal of Mathematical Imaging and Vision, 2006, 25(3): 423–444. doi: 10.1007/s10851-006-6897-z -
圖(7) / 表(3)
計量
- 文章訪問數(shù): 2562
- HTML全文瀏覽量: 940
- PDF下載量: 48
- 被引次數(shù): 0
下載:
