For joint sensing of multiple Cognitive Users (CUs), considering the case of fading channels between the CU and the decision center, a joint spectrum sensing algorithm based on Multiple-Access Channels (MAC) was proposed. On the basis of the system structure and signal modeling, the asymptotic behavior and outage probability of the traditional MAC algorithm were analyzed. Under the constraint of the average transmit power of the CU, the transmit gain of the MAC algorithm was optimized to maximize the detection probability; and the problem of minimizing the number of CUs was also studied in the case of certain Quality of Service (QoS). Simulation results show that the proposed MAC algorithm can ensure good detection performance; in particular, it achieves exponential performance improvement in detection error probability.

Focusing on the reconnoitering problem of Linear Frequency Modulation (LFM) pulse signals, a method to estimate the whole parameters containing frequency modulate rate, center frequency, time of arrival and pulse width, was proposed. Firstly, frequency modulate rate as well as time-frequency relation was estimated based on Fractional Fourier Transform (FrFT), then partial correlation pulses was used for signal accumulation, at last the autocorrelation technology was used to estimate the center frequency, time of arrival and pulse width. The Cramer-Rao Low Bounds (CRLB) for the parameters were derived and the effect on estimation error caused by signal to noise ratio was analyzed. Finally, the effect on estimation error caused by the width of partial accumulation pulse was analyzed, and some advice was given on choosing the width of accumulation pulse. Simulation results show that the estimation error of frequency modulate rate is close to CRLB. When signal to noise ratio is 0 dB without any knowledge of baseband and modulation parameters, the Root Mean Square Error (RMSE) of center frequency is about 10 ^-1MHz orders of magnitude, and the RMSE of time of arrival as well as pulse width is about 10 ^-1 μs orders of magnitude. The estimate error, which is affected by the correlation pulse width, decreases with the increase of correlation pulse width, and then increases. The proposed method is especially applicable to the reconnoitering of new system radar such as chirp radar, and Synthetic Aperture Radar (SAR).

Concerning the problem of detecting unknown Linear Frequency Modulated (LFM) signal, according to the feature that the phase of the signal is stable, a LFM signal detection method based on the frequency domain phase variance weighting was proposed. The proposed method utilized the characteristics that the phase of LFM signal frequency unit was stable, and the phase of noise frequency unit was random, to weight each frequency unit by the phase variance, which could further restrain the background noise energy disturbances, enhanced the Signal-to-Noise Ratio (SNR) gain of signal detection, and achieved detecting unknown LFM signal. Under simulation conditions, when the input average Spectrum Level Ratio (SLR) was greater than -10 dB, compared with phase difference alignment method, the output average SLR of the proposed method was further improved, and with the input the average SLR became higher, the output SLR was further improved. The theoretical analysis and experimental results show that the proposed method can well enhance the energy of LFM signal, restrain the background noise energy, and improve SNR.