The demodulation algorithm for AM-FM sinusoids proposed in reference[1]canonly deal with complex exponential sequences.In real applications,one needs to con-struct an analytic signal from real samples.Influenced by th...The demodulation algorithm for AM-FM sinusoids proposed in reference[1]canonly deal with complex exponential sequences.In real applications,one needs to con-struct an analytic signal from real samples.Influenced by the Hilbert Transformation,thecomplex noise in the analytic signal is colored.This paper analyzes the colored noise andthe disadvantage caused by the noise in the estimations of the instantaneous frequencyand amplitude factor using the algorithm in[1]and formulates a practical algorithm.展开更多
In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose freq...In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose frequencies have overlapped regions in Fourier transform domain and even have crossed points in time-frequency distribution (TFD) so that the proposed TVBF seems like a “soft-cutter” that cuts the frequency domain to snaky slices with rational physical sense. First, the Hilbert transform based decomposition is analyzed for the analysis of nonstationary signals. Based on the above analysis, a hypothesis under a certain condition that AM-FM components can be separated successfully based on Hilbert transform and the assisted signal is developed, which is supported by representative experiments and theoretical performance analyses on a error bound that is shown to be proportional to the product of frequency width and noise variance. The assisted signals are derived from the refined time-frequency distributions via image fusion and least squares optimization. Experiments on man-made and real-life data verify the efficiency of the proposed method and demonstrate the advantages over the other main methods.展开更多
In this paper we present a large scale, passive positioning system that can be used for approximate localization in Global Positioning System(GPS) denied/spoofed environments. This system can be used for detecting GPS...In this paper we present a large scale, passive positioning system that can be used for approximate localization in Global Positioning System(GPS) denied/spoofed environments. This system can be used for detecting GPS spoofing as well as for initial position estimation for input to other GPS free positioning and navigation systems like Terrain Contour Matching(TERCOM). Our Location inference through Frequency Modulation(FM)Signal Integration and estimation(LoSI) system is based on broadcast FM radio signals and uses Received Signal Strength Indicator(RSSI) obtained using a Software Defined Radio(SDR). The RSSI thus obtained is used for indexing into an estimated model of expected FM spectrum for the entire United States. We show that with the hardware for data acquisition, a single point resolution of around 3 miles and associated algorithms, we are capable of positioning with errors as low as a single pixel(more precisely around 0.12 mile). The algorithm uses a largescale model estimation phase that computes the expected FM spectrum in small rectangular cells(realized using geohashes) across the Contiguous United States(CONUS). We define and use Dominant Channel Descriptor(DCD) features, which can be used for positioning using time varying models. Finally we use an algorithm based on Euclidean nearest neighbors in the DCD feature space for position estimation. The system first runs a DCD feature detector on the observed spectrum and then solves a subset query formulation to find Inference Candidates(IC).Finally, it uses a simple Euclidean nearest neighbor search on the ICs to localize the observation. We report results on 1500 points across Florida using data and model estimates from 2015 and 2017. We also provide a Bayesian decision theoretic justification for the nearest neighbor search.展开更多
根据PCM／FM信号的特点，提出一种基于离散短时傅里叶变换（discrete short time Fourier transform，DSTFT）的软件化解调方法：首先利用DST-FT计算PCM／FM信号的0、1频点的频谱能量，然后通过比较2个频点频谱能量的大小实现信号的判...根据PCM／FM信号的特点，提出一种基于离散短时傅里叶变换（discrete short time Fourier transform，DSTFT）的软件化解调方法：首先利用DST-FT计算PCM／FM信号的0、1频点的频谱能量，然后通过比较2个频点频谱能量的大小实现信号的判决；详细论述此种解调方法的基本原理和实现过程，提出采用小数形式的频点来提高解调精度；利用C＋＋语言编程实现了该解调方法，并用从实装设备采集的PCM／FM信号对解调软件进行测试。测试结果表明：该方法对PCM／FM信号具有较好的解调性能，且实现原理简洁，运行效率较高，具有一定的工程应用价值。展开更多
文摘The demodulation algorithm for AM-FM sinusoids proposed in reference[1]canonly deal with complex exponential sequences.In real applications,one needs to con-struct an analytic signal from real samples.Influenced by the Hilbert Transformation,thecomplex noise in the analytic signal is colored.This paper analyzes the colored noise andthe disadvantage caused by the noise in the estimations of the instantaneous frequencyand amplitude factor using the algorithm in[1]and formulates a practical algorithm.
文摘In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose frequencies have overlapped regions in Fourier transform domain and even have crossed points in time-frequency distribution (TFD) so that the proposed TVBF seems like a “soft-cutter” that cuts the frequency domain to snaky slices with rational physical sense. First, the Hilbert transform based decomposition is analyzed for the analysis of nonstationary signals. Based on the above analysis, a hypothesis under a certain condition that AM-FM components can be separated successfully based on Hilbert transform and the assisted signal is developed, which is supported by representative experiments and theoretical performance analyses on a error bound that is shown to be proportional to the product of frequency width and noise variance. The assisted signals are derived from the refined time-frequency distributions via image fusion and least squares optimization. Experiments on man-made and real-life data verify the efficiency of the proposed method and demonstrate the advantages over the other main methods.
文摘In this paper we present a large scale, passive positioning system that can be used for approximate localization in Global Positioning System(GPS) denied/spoofed environments. This system can be used for detecting GPS spoofing as well as for initial position estimation for input to other GPS free positioning and navigation systems like Terrain Contour Matching(TERCOM). Our Location inference through Frequency Modulation(FM)Signal Integration and estimation(LoSI) system is based on broadcast FM radio signals and uses Received Signal Strength Indicator(RSSI) obtained using a Software Defined Radio(SDR). The RSSI thus obtained is used for indexing into an estimated model of expected FM spectrum for the entire United States. We show that with the hardware for data acquisition, a single point resolution of around 3 miles and associated algorithms, we are capable of positioning with errors as low as a single pixel(more precisely around 0.12 mile). The algorithm uses a largescale model estimation phase that computes the expected FM spectrum in small rectangular cells(realized using geohashes) across the Contiguous United States(CONUS). We define and use Dominant Channel Descriptor(DCD) features, which can be used for positioning using time varying models. Finally we use an algorithm based on Euclidean nearest neighbors in the DCD feature space for position estimation. The system first runs a DCD feature detector on the observed spectrum and then solves a subset query formulation to find Inference Candidates(IC).Finally, it uses a simple Euclidean nearest neighbor search on the ICs to localize the observation. We report results on 1500 points across Florida using data and model estimates from 2015 and 2017. We also provide a Bayesian decision theoretic justification for the nearest neighbor search.
文摘根据PCM／FM信号的特点，提出一种基于离散短时傅里叶变换（discrete short time Fourier transform，DSTFT）的软件化解调方法：首先利用DST-FT计算PCM／FM信号的0、1频点的频谱能量，然后通过比较2个频点频谱能量的大小实现信号的判决；详细论述此种解调方法的基本原理和实现过程，提出采用小数形式的频点来提高解调精度；利用C＋＋语言编程实现了该解调方法，并用从实装设备采集的PCM／FM信号对解调软件进行测试。测试结果表明：该方法对PCM／FM信号具有较好的解调性能，且实现原理简洁，运行效率较高，具有一定的工程应用价值。
