| Cognitive Radar can continually apperceive the dynamic of the external environment and the target, to adaptively choose the working mode, the transmitted waveform, and the signal/data processing algorithm, etc.. It can make the best of varieties of information to process intelligently. Cognitive radar has the adaptive transmitting and knowledge-aided processing capabilities, which are the main differences from the traditional adaptive radar. In the process of target tracking, the a priori information is abundant, it is propitious for the cognitive transmitting and the knowledge aided processing. This thesis is based on the cognitive tracking radar, and its research includes the transmitted waveform selection for target tracking, maneuvering target tracking algorithm, and data association methods, etc.. The main work is summarized as follows:1. The problem of transmitted waveform selection is respectively discussed from the aspects of single pulse and multiple pulses.(1) Single pulse: First, the rectangular pulse signal is selected as the basic waveform, and its ambiguity function is transformed linearly on the delay-Doppler shift plane. The linear frequency modulated(LFM) waveform library and the rotation library are respectively obtained by the flex and rotation transformations. And then, based on the Kalman filter(KF) the optimal waveform is searched respectively in both the libraries by the criteria of maximizing the mutual information and minimizing the mean square error(MMSE). The physic sense and the advantages/disadvantages of the criteria are analyzed in detail. The dimensions of range tracking error and velocity tracking error are inconsistent, and the proportion of range tracking accuracy to velocity tracking accuracy can’t be adjusted by the current methods. Accordingly, aim at the problem the criterion of the weighted MMSE is proposed. The efficiency of the proposed method is verified by the simulation results.(2) Multiple pulses: It is necessary to consider the performance of estimating range/velocity and Doppler tolerance for tracking a maneuvering target. The Cramer-Rao Lower Bound(CRLB) for estimating the range/velocity and Doppler tolerance of three signals(LFM, V-LFM and M sequence) are compared. The simulation results show that the V-LFM waveform can effectively improve the performance of estimating the target range and velocity in the case of a bit loss in the Doppler tolerance.2. A switched KF and interacting multiple model(IMM) algorithm based on theautoregressive(AR) model is proposed for maneuvering target tracking. First, the traditional maneuvering target tracking models and algorithms are introduced, and the basic principle and the advantages/disadvantages of the main algorithms are analyzed in detail. Second, the AR model is applied in target tracking, and the building and solving process of the AR model are introduced. The AR model is an adaptive dynamic model. Because it can’t only satisfy the polynomial constraint of the target motion, but also reduce the noise with the extra degree of freedom. Finally, the idea of variable dimension filter is imported for maneuvering target tracking. The switching of KF-AR(KF based on AR model) and IMM-AR(IMM based on AR model) is triggered by the detection of maneuver onset, in order to adjust to the un-maneuvering and maneuvering motions. The proposed algorithm is essentially a variable structure multiple model(VSMM) algorithm. But its computing amount is much smaller than the traditional VSMM algorithm, and it is valuable in the engineering application. The simulation results demonstrate that the AR model is superior to the traditional discrete-time differential model in the tracking performance on the condition of the same parameters. Especially, when the parameter doesn’t match with the real target motion, the advantage of the AR model is more evident. And the tracking performance of the proposed algorithm is better than the traditional variable dimension filter and IMM filter.3. The problem of data association for maneuvering target is researched. The traditional correlating gate design techniques can not improve the association probability, as they determine the validated measurements only by enlarging the gate volume, which is easy to cause tracking loss. In this thesis, a novel adaptive correlating gate design technique based on the comprehensive interacting multiple model-probabilistic data association IMM-PDA(C-IMMPDA) algorithm is proposed, aided with the knowledge of target state measurements. Since the C-IMMPDA algorithm outperforms the traditional IMM-PDA algorithm in the aspects of tracking performance and computational amount, we design the correlating gate based on the C-IMMPDA algorithm. We take two steps to design the correlating gate. When there are no validated measurements in the correlating gate, the gate is enlarged appropriately by the covariance of maximal maneuvering level at first, in order to guarantee that the validated measurements exist in the gate. And then assume that the target maneuvers in a limited range, the optimal gate center is acquired among the predictive range by minimizing the mean square error, aided by the information of measurements. The proposed algorithm has adjusted thegate center and volume, so the optimal measurement set can be obtained. The simulation results demonstrate that the proposed method can decrease the probability of the tracking loss and improve the tracking accuracy compared with the traditional techniques.4. The problem of multi-target data association is researched. The computing load is very heavy in the low signal-noise-ratio(SNR) case of multi-target tracking for the traditional joint probabilistic data association(JPDA) algorithm. When the tracks of multi-target get approached or crossed, it is easy to lead to combine or even to get wrong tracks for the traditional methods. Since the traditional methods only utilize the information of target position to finish the data association. At first the computing amount of the JPDA algorithm is analyzed theoretically in this thesis. And then the Doppler and high resolution range profile(HRRP) information are respectively applied for multi-target tracking:(1) The application of Doppler information: The Doppler information of multi-target can be obtained synchronously by the moving target detection(MTD) technique in the pulse Doppler radar. And the application of Doppler information is discussed in the aspects of track initialization and maintenance.(2) The application of HRRP information: The HRRP information reveals the property of the target, which is different from the Doppler information. The traditional method doesn’t consider the correlation between the HRRP and the target motion, when it processes the data association with the HRRP. Firstly, the target attitude angle is estimated in real time on the principle that the HRRP is sensitive to the attitude angle. And then, the attitude angle is added to the target measurement state to aid multi-target tracking. In this thesis, the Doppler information and HRRP information are added to the target measurement state to construct a multi-dimension correlating gate. The data association is accomplished with the multi-dimension information. So the problem of multi-target data association is simplified to multiple sub-problems of data association for a single target. And the efficiency of data association is substantially improved.
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