基于采樣值隨機(jī)壓縮矩陣核空間的亞奈奎斯特采樣重構(gòu)算法
doi: 10.11999/JEIT180323
-
哈爾濱理工大學(xué)測(cè)控技術(shù)與儀器黑龍江省高校重點(diǎn)實(shí)驗(yàn)室 ??哈爾濱 ??150080
Sub-Nyquist Sampling Recovery Algorithm Based on Kernel Space of the Random-compression Sampling Value Matrix
-
The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China
-
摘要:
針對(duì)現(xiàn)有調(diào)制寬帶轉(zhuǎn)換器亞奈奎斯特采樣重構(gòu)算法性能不高問題，該文提出一種基于采樣值核空間的支撐重構(gòu)算法和隨機(jī)壓縮降秩方法，將兩者結(jié)合得到一種高性能采樣重構(gòu)算法。首先利用隨機(jī)壓縮變換在不改變未知矩陣稀疏特性的前提下將采樣方程轉(zhuǎn)化為多個(gè)新的多測(cè)量向量問題，然后利用采樣值矩陣核空間與采樣矩陣支撐正交的關(guān)系獲取聯(lián)合稀疏支撐集，最后通過偽逆完成重構(gòu)。從理論和實(shí)驗(yàn)兩個(gè)方面對(duì)所提方法進(jìn)行了分析和驗(yàn)證。數(shù)值實(shí)驗(yàn)表明，與傳統(tǒng)重構(gòu)算法相比，所提算法提高了重構(gòu)成功率、降低了高概率重構(gòu)所需的通道數(shù)，而且重構(gòu)性能總體上隨壓縮次數(shù)增加而提高。
-
關(guān)鍵詞:
- 稀疏重構(gòu) /
- 亞奈奎斯特采樣 /
- 多測(cè)量向量 /
- 調(diào)制寬帶轉(zhuǎn)換器
Abstract:To solve the low performance problem of the existing Modulated Wideband Converter (MWC)-based sub-Nyquist sampling recovery algorithm, this paper proposes a support recovery algorithm based on the kernel space of sampling value and a random compression rank-reduction idea. Combining them, a high-performance sampling recovery algorithm is achieved. Firstly random compression transforms are used to convert the sampling equation into several new multiple-measurement-vector problems, without changing the sparsity of the unknown matrix. Then the orthogonal relationship between the kernel space of sampling value and the support vectors of sampling matrix is utilized to obtain joint sparse support set of the unknown. The final recovery is performed by the pseudo inversion. The proposed method is analyzed and verified by theory and experiment. Numerical experiments show that, compared with the traditional recovery algorithm, the proposal can improve the recovery success rate, and reduce the channel number required for high-probability recovery. Furthermore, in general, the recovery performance improves with the rise of compression times.
-
PARK J, JANG J, IM S, et al. A sub-nyquist radar electronic surveillance system[J]. IEEE Access, 2018, 6: 10080–10091. doi: 10.1109/ACCESS.2018.2799304 QIN Zhijin, GAO Yue, PLUMBLEY M D, et al. Wideband spectrum sensing on real-time signals at sub-Nyquist sampling rates in single and cooperative multiple nodes[J]. IEEE Transactions on Signal Processing, 2016, 64(12): 3106–3117. doi: 10.1109/TSP.2015.2512562 鄭仕鏈, 楊小牛. 用于調(diào)制寬帶轉(zhuǎn)換器壓縮頻譜感知的重構(gòu)失敗判定方法[J]. 電子與信息學(xué)報(bào), 2015, 37(1): 236–240. doi: 10.11999/JEIT140127ZHENG Shilian and YANG Xiaoniu. A reconstruction failure detection scheme for modulated wideband converter based compressed spectrum sensing[J]. Journal of Electronics &Information Technology, 2015, 37(1): 236–240. doi: 10.11999/JEIT140127 DONOHO D L. Compressed sensing[J]. IEEE Transaction on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582 BARANIUK R G. More is less: Signal processing and the data deluge[J]. Science, 2011, 331(6018): 717–719. doi: 10.1126/science.1197448 LASKA J N, KIROLOS S, DUARTE M F, et al. Theory and implementation of an analog-to-information converter using random demodulation[C]. IEEE International Symposium on Circuits and Systems, New Orleans, USA, 2007: 1959–1962. FARDAD M, SAYEDI S M, and YAZDIAN E. Hardware implementation of iterative method with adaptive thresholding for random sampling recovery of sparse signals[J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2018, 26(5): 867–877. doi: 10.1109/TVLSI.2018.2791351 REN Shiyu, ZENG Zhimin, GUO Caili, et al. A low complexity sensing algorithm for wideband sparse spectra[J]. IEEE Communications Letters, 2017, 21(1): 92–95. doi: 10.1109/LCOMM.2016.2616471 MISHALI M and ELDAR Y C. From theory to practice: Sub-Nyquist sampling of sparse wideband analog signals[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 375–391. doi: 10.1109/JSTSP.2010.2042414 ADAMS D, ELDAR Y C, and MURMANN B. A mixer front end for a four channel modulated wideband converter with 62-dB blocker rejection[J]. IEEE Journal of Solid-State Circuits, 2017, 52(5): 1286–1294. doi: 10.1109/JSSC.2017.2647941 LEXA M A, DAVIES M E, and THOMPSON J S. Reconciling compressive sampling systems for spectrally sparse continuous-time signals[J]. IEEE Transactions on Signal Processing, 2012, 60(1): 155–171. doi: 10.1109/TSP.2011.2169408 ZHANG Ruoyu, ZHAO Honglin, JIA Shaobo, et al. Sparse multi-band signal recovery based on support refining for modulated wideband converter[C]. IEEE 13th International Conference on Signal Processing (ICSP), Chengdu, China, 2016: 304–309. COTTER F S, RAO D B, ENGAN K, et al. Sparse solutions to linear inverse problems with multiple measurement vectors[J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2477–2488. doi: 10.1109/TSP.2005.849172 CHEN Jie and HUO Xiaoming. Theoretical results on sparse representations of multiple-measurement vectors[J]. IEEE Transactions on Signal Processing, 2006, 54(12): 4634–4643. doi: 10.1109/TSP.2006.881263 BLANCHARD J D, CERMAK M, HANLE D, et al. Greedy algorithms for joint sparse recovery[J]. IEEE Transactions on Signal Processing, 2014, 62(7): 1694–1704. doi: 10.1109/TSP.2014.2301980 KIM J M, LEE O K, and YE J C. Compressive MUSIC: Revisiting the link between compressive sensing and array signal processing[J]. IEEE Transactions on Information Theory, 2012, 58(1): 278–301. doi: 10.1109/TIT.2011.2171529 MISHALI M and ELDAR Y C. Reduce and boost: Recovering arbitrary sets of jointly sparse vectors[J]. IEEE Transactions on Signal Processing, 2008, 56(10): 4692–4702. doi: 10.1109/TSP.2008.927802 -
下載:
計(jì)量
- 文章訪問數(shù): 2166
- HTML全文瀏覽量: 886
- PDF下載量: 57
- 被引次數(shù): 0
下載:
