基于壓縮采樣值的跳頻信號(hào)檢測(cè)和參數(shù)估計(jì)
發(fā)布時(shí)間：2019-02-12 12:29
【摘要】：跳頻通信具有抗干擾、低截獲和易組網(wǎng)等優(yōu)點(diǎn),在民用和軍事通信中有廣泛的應(yīng)用。近年來為了提高抗干擾能力,跳頻通信有向?qū)掝l帶、高跳速發(fā)展的趨勢(shì)。這給基于奈奎斯特采樣架構(gòu)的跳頻捕獲系統(tǒng)帶來了諸多問題,最突出的就是前端采樣數(shù)據(jù)量大、后續(xù)傳輸和處理困難。在不損失信息的前提下,壓縮感知技術(shù)能以極低的速率采集寬帶稀疏信號(hào),為解決跳頻信號(hào)的非協(xié)作接收和處理提供了新的思路。本文主要研究基于壓縮信號(hào)處理(Compressive Signal Processing, CSP)的跳頻信號(hào)檢測(cè)和參數(shù)估計(jì)算法。相對(duì)于基于奈奎斯特采樣值的傳統(tǒng)處理方式,基于少量壓縮采樣值的壓縮跳頻信號(hào)處理方式能有效的降低運(yùn)算量,簡(jiǎn)化信號(hào)處理流程,從而提高系統(tǒng)工作的時(shí)效性�，F(xiàn)將本文主要研究?jī)?nèi)容和創(chuàng)新點(diǎn)總結(jié)如下：1.在噪聲水平已知時(shí),針對(duì)高斯白噪聲中的未知信號(hào)檢測(cè)問題,提出一種壓縮能量檢測(cè)算法(Compressive Energy Detection, CS-ED)。根據(jù)單個(gè)壓縮采樣值在不同假設(shè)條件下其數(shù)字特征不同的特點(diǎn),該算法將壓縮采樣值的方差作為判決依據(jù),完成檢測(cè)任務(wù)。實(shí)驗(yàn)結(jié)果表明,該算法相對(duì)于傳統(tǒng)的能量檢測(cè)算法,CS-ED算法用少量檢測(cè)性能的損失換取了算法時(shí)效性較大的提高。2.在噪聲水平未知時(shí),提出一種基于壓縮信號(hào)處理的壓縮自相關(guān)檢測(cè)算法(Compressive Auto-Correlative Detection, CS-ACD)。該算法充分利用了信號(hào)的稀疏性和傳感矩陣的嚴(yán)格等距特性,由稀疏系數(shù)自相關(guān)向量的不同統(tǒng)計(jì)分布進(jìn)行檢測(cè)判決。仿真結(jié)果表明,在相同的壓縮采樣次數(shù)下,相對(duì)于重構(gòu)原信號(hào)后再做檢測(cè)的算法,CS-ACD算法擁有更低的錯(cuò)誤概率；通過和現(xiàn)有壓縮檢測(cè)算法的對(duì)比,在信噪比大于-2dB時(shí),CS-ACD算法可在保證檢測(cè)性能的前提下降低運(yùn)算量。3.針對(duì)僅存在單個(gè)跳頻信號(hào)的情況,提出一種基于壓縮信號(hào)處理的跳頻信號(hào)跳變時(shí)刻估計(jì)算法(Compressive Hopping Transition time Estimation, CS-HTE)。該算法僅需重構(gòu)單采樣周期內(nèi).,跳頻信號(hào)在傅里葉正交基上兩個(gè)權(quán)值最大的稀疏系數(shù),并根據(jù)這兩個(gè)系數(shù)的相對(duì)大小判定前后兩跳持續(xù)時(shí)間,通過不斷的滑動(dòng)壓縮采樣即可完成對(duì)單個(gè)跳頻信號(hào)的跳變時(shí)刻估計(jì)。CS-HTE能克服時(shí)頻不確定性帶來的不利影響,可有效提高跳頻信號(hào)參數(shù)估計(jì)的精度和時(shí)效性。4.針對(duì)跳頻電臺(tái)組網(wǎng)工作時(shí)的參數(shù)估計(jì)問題,在現(xiàn)有壓縮域波達(dá)方向估計(jì)算法基礎(chǔ)上,給出了一種基于壓縮信號(hào)處理的跳頻信號(hào)空時(shí)頻聯(lián)合估計(jì)算法(Compressive Spatial Time-Frequency Estimation, CS-STFE)。該算法利用壓縮陣列信號(hào)在空域和頻域的稀疏性,可對(duì)跳頻信號(hào)的波達(dá)方向和語圖進(jìn)行聯(lián)合估計(jì)。5.在寬帶跳頻信號(hào)壓縮采樣處理系統(tǒng)中,給出了FPGA和ARM數(shù)據(jù)共享和交互協(xié)議,設(shè)計(jì)產(chǎn)生了基于FPGA控制的寬帶壓縮感知測(cè)量波形,經(jīng)測(cè)試符合跳頻信號(hào)壓縮采樣處理系統(tǒng)的要求。
[Abstract]:FH communication is widely used in civil and military communication because of its advantages of anti-jamming, low interception and easy networking. In recent years, in order to improve the ability of anti-jamming, frequency-hopping communication has a trend of wide band and high speed hopping. This brings many problems to the frequency hopping acquisition system based on Nyquist sampling architecture. The most outstanding problem is that the front-end sampling data is large and the subsequent transmission and processing are difficult. On the premise of no loss of information, compressed sensing technology can collect wideband sparse signals at very low rate, which provides a new way to solve the problem of non-cooperative receiving and processing of frequency-hopping signals. This paper mainly studies the frequency hopping signal detection and parameter estimation algorithm based on compressed signal processing (Compressive Signal Processing, CSP). Compared with the traditional processing method based on Nyquist sampling value, the compressed frequency hopping signal processing method based on a small amount of compressed sampling value can effectively reduce the computational complexity, simplify the signal processing flow, and improve the timeliness of the system work. The main contents and innovations of this paper are summarized as follows: 1. When the noise level is known, a compression energy detection algorithm (Compressive Energy Detection, CS-ED) is proposed to detect unknown signals in Gao Si white noise. According to the different digital characteristics of a single compressed sampling value under different assumptions, the variance of the compressed sampling value is taken as the decision basis to complete the detection task. The experimental results show that compared with the traditional energy detection algorithm, the CS-ED algorithm gains a small loss of detection performance in exchange for a significant increase in the time-efficiency of the algorithm. 2. When the noise level is unknown, a compression autocorrelation detection algorithm (Compressive Auto-Correlative Detection, CS-ACD) based on compression signal processing is proposed. The algorithm makes full use of the sparsity of the signal and the strict equidistance of the sensor matrix, and detects and decides by the different statistical distribution of the sparse coefficient autocorrelation vector. The simulation results show that the CS-ACD algorithm has lower error probability than the original signal detection algorithm under the same compression sampling times. By comparing with the existing compression detection algorithm, when the SNR is greater than-2dB, the CS-ACD algorithm can reduce the computation cost under the premise of ensuring the detection performance. This paper presents a hopping time estimation algorithm (Compressive Hopping Transition time Estimation, CS-HTE) for frequency hopping signals based on compressed signal processing, which only has a single frequency hopping signal. The algorithm only needs to reconstruct the maximum sparse coefficients of the two weights of the frequency hopping signal on the Fourier orthogonal basis, and determine the duration of the two hops according to the relative size of the two coefficients. The jump time estimation of a single frequency hopping signal can be completed by continuous sliding compression sampling. CS-HTE can overcome the adverse effect of time-frequency uncertainty and improve the precision and timeliness of the parameter estimation of frequency hopping signal. 4. Aiming at the parameter estimation problem of frequency hopping station (FH) network, a joint space-time-frequency estimation algorithm (Compressive Spatial Time-Frequency Estimation, CS-STFE) based on compressed signal processing is presented based on the existing DOA estimation algorithm in compressed domain. Based on the sparsity of compressed array signals in spatial and frequency domain, the proposed algorithm can estimate the DOA and speech patterns of FH signals. In the broadband frequency hopping signal compression sampling processing system, the data sharing and interactive protocols between FPGA and ARM are given, and the waveform of broadband compression sensing measurement based on FPGA control is designed. The test meets the requirements of frequency hopping signal compression sampling processing system.
【學(xué)位授予單位】：解放軍信息工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN914.41
本文編號(hào)：2420442
[Abstract]:FH communication is widely used in civil and military communication because of its advantages of anti-jamming, low interception and easy networking. In recent years, in order to improve the ability of anti-jamming, frequency-hopping communication has a trend of wide band and high speed hopping. This brings many problems to the frequency hopping acquisition system based on Nyquist sampling architecture. The most outstanding problem is that the front-end sampling data is large and the subsequent transmission and processing are difficult. On the premise of no loss of information, compressed sensing technology can collect wideband sparse signals at very low rate, which provides a new way to solve the problem of non-cooperative receiving and processing of frequency-hopping signals. This paper mainly studies the frequency hopping signal detection and parameter estimation algorithm based on compressed signal processing (Compressive Signal Processing, CSP). Compared with the traditional processing method based on Nyquist sampling value, the compressed frequency hopping signal processing method based on a small amount of compressed sampling value can effectively reduce the computational complexity, simplify the signal processing flow, and improve the timeliness of the system work. The main contents and innovations of this paper are summarized as follows: 1. When the noise level is known, a compression energy detection algorithm (Compressive Energy Detection, CS-ED) is proposed to detect unknown signals in Gao Si white noise. According to the different digital characteristics of a single compressed sampling value under different assumptions, the variance of the compressed sampling value is taken as the decision basis to complete the detection task. The experimental results show that compared with the traditional energy detection algorithm, the CS-ED algorithm gains a small loss of detection performance in exchange for a significant increase in the time-efficiency of the algorithm. 2. When the noise level is unknown, a compression autocorrelation detection algorithm (Compressive Auto-Correlative Detection, CS-ACD) based on compression signal processing is proposed. The algorithm makes full use of the sparsity of the signal and the strict equidistance of the sensor matrix, and detects and decides by the different statistical distribution of the sparse coefficient autocorrelation vector. The simulation results show that the CS-ACD algorithm has lower error probability than the original signal detection algorithm under the same compression sampling times. By comparing with the existing compression detection algorithm, when the SNR is greater than-2dB, the CS-ACD algorithm can reduce the computation cost under the premise of ensuring the detection performance. This paper presents a hopping time estimation algorithm (Compressive Hopping Transition time Estimation, CS-HTE) for frequency hopping signals based on compressed signal processing, which only has a single frequency hopping signal. The algorithm only needs to reconstruct the maximum sparse coefficients of the two weights of the frequency hopping signal on the Fourier orthogonal basis, and determine the duration of the two hops according to the relative size of the two coefficients. The jump time estimation of a single frequency hopping signal can be completed by continuous sliding compression sampling. CS-HTE can overcome the adverse effect of time-frequency uncertainty and improve the precision and timeliness of the parameter estimation of frequency hopping signal. 4. Aiming at the parameter estimation problem of frequency hopping station (FH) network, a joint space-time-frequency estimation algorithm (Compressive Spatial Time-Frequency Estimation, CS-STFE) based on compressed signal processing is presented based on the existing DOA estimation algorithm in compressed domain. Based on the sparsity of compressed array signals in spatial and frequency domain, the proposed algorithm can estimate the DOA and speech patterns of FH signals. In the broadband frequency hopping signal compression sampling processing system, the data sharing and interactive protocols between FPGA and ARM are given, and the waveform of broadband compression sensing measurement based on FPGA control is designed. The test meets the requirements of frequency hopping signal compression sampling processing system.
【學(xué)位授予單位】：解放軍信息工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN914.41
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