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This paper introduces the theoretical basis of spatial time-frequency distributions in direction-of-arrival (DOA) estimation, and then proposes a new method through analysis to non-stationary signals' time-frequency distributions and extraction of their cross terms. The work done by the previous researchers almost based on paying attention to the auto terms information and suppressing of cross terms, and this paper examines the signal cross terms from a new perspective. Study found that cross terms between signals do contain rich information, and using cross terms information alone can calculate out the direction of the source signals. In particular, when the strength between the signals is different, the auto terms information of the weak signal have a certain possibility can not be detected. Therefore, extraction of cross terms information to estimate the DOA of signals is much easier. Simulation experiments verified these characteristics.

Double-frequency phase difference method is a simple and effective means for measuring the group delay of Frequency-Translating Devices (FTD). Especially for measuring the group delay of FTD which cannot accept the other local oscillator, the method shows the irreplaceable advantage. In order to obtain an accurate description of the group delay characteristics in the pass band of FTD, two improved methods are put forward in this paper which are variable aperture measurement method and multiple-frequency phase difference(MFPD) method on the basis of the measuring group delay by using double-frequency phase difference(DFPD) method. The variable aperture measurement method uses genetic algorithm (GA) to optimize the measurement aperture and the measurement interval on the basis of the rate of the group delay, and the measured data is handled with the cubic spline interpolation and Taylor series expansion method. Finally, an accurate group delay characteristics curve of the system is fitted. The MFPD method turns the double-frequency into the multiple-frequency on the basis of the DFPD method, which improves the testing efficiency and the measurement accuracy in a great extent. However, the premise is that the measured output spectrum should have enough signal-to-noise ratio. The simulation results show that these two improved methods are equally effective in reducing the measurement error and improving the measurement accuracy greatly.

This system centers on high-performance microcontroller AT89S52. Through the motor control on ASSP chip MMC-1 which controlling two DC motors, it can achieve accuracy position control of the moving acoustic. The buzzer can make an aural signal on a certain frequency using the microcontroller AT89S52. Because the distances between the three audio receivers and the moving acoustic are different, the signal they receive has a certain time difference. Then the control system makes a control signal according to the time difference. This signal is transmitted to the moving sound source through a wireless transceiver module and guides its motion.

Group delay is a key parameter describing the phase characteristic of the signal transmission system, which can also measure the phase linearity of the continuous signal passing through transmission system. The group delay of Frequency-Translating Devices (FTD) is measured by the double-frequency phase difference method in this paper. But the measurement error will increase in the FTD whose group delay changes greatly. In view of this problem, an optimization method is put forward. Combined with polynomial fitting of high order, this proposed method, which uses the reasonable selection of the parameters such as measuring aperture and measurement interval, can improve the measurement precision and the test efficiency.

Azimuthal measurements of the high frequency ground wave over-the-horizon (OTH) radar are either coarse or unavailable in an actual environment. It is very hard for a single station OTH radar to performance effective tracking under that condition. A bi-station OTH radar system is proposed to triangulate target tracks using range and Doppler measurements only. Multi-sensor data association and combining positioning are key issues of this system. This paper focuses on positioning and tracking filter algorithm. The locating and tracking model is established and the extended Kalman filter (EKF) is used to solve the bi-station nonlinear tracking problem. Simulation result shows that EKF can handle this well, and the bi-station OTH radar system can meet or slightly better than the accuracy of the conventional single station OTH radar tracking system with poor bearing resolution.

In a complex environment radar plot is often changing dramatically, non-uniform, discontinuous, uncertain and containing lots of false alarms. It is difficult for tracking system to form tracks in such complex environment. Traditional track formation methods can not accurately determine the size of track initiation gate and select specific initiation criteria. In this paper, an intelligent information processing method is proposed to solve track initiation in complex environments. The track initiation is considered as a progress of plot identification, classification and fuzzy information processing. According to the integration and fusion of neural network (NN), fuzzy reasoning (FR) and expert system (ES) technology, an intellectualized track initiation knowledge reasoning system is constructed which can realize structural, functional, algorithmic and hierarchical complementation. Further more the adaptive robust learning algorithm and the weighted synthesis reasoning algorithm are used in this system. Real data experiment shows that the proposed knowledge engineering method can effectively solve track initiation problem in complex conditions.

Though a Monostatic-Bistatic Composite High Frequency Surface Wave Radar Network (MBC-HFSWR Network) is established to cover the shortage of the monostatic radar, how to judge the real target update from the plausible measurements to initiate tracks quickly and accurately still remains a challenging problem. A novel method utilizing unconventional characteristics for track initiation is proposed in this paper, which uses more accurate information and avoids the problem of low detection precision of high frequency radar. A highly intelligent information fusion model which is a combination of Neural network, Fuzzy reasoning and Expert system (NFE model) is presented to solve the problem above and the validity of the provided method is demonstrated by Monte Carlo simulations.