| Multiple Input Multiple Output(MIMO)radar is a new radar system with multiple radar receiving antennas and multiple radar transmitting antennas.Compared with traditional radar,MIMO radar uses multiple transmit and receive antennas to obtain more degrees of freedom,improve target detection performance,target parameter estimation accuracy and target recognition ability.Distributed MIMO radar obtains multi-angle echo information of target through multiple separate antennas,and makes full use of the spatial diversity of Radar Cross Section(RCS)of target.It is one of the important technical means to deal with stealth targets.This paper mainly studies the following three aspects:1.Based on the basic principle of distributed MIMO radar,the echo signal model of distributed MIMO radar is established.The detection probability and false alarm probability of distributed MIMO radar and traditional phased array radar are derived by orthogonal decomposition of random variables representing RCS.The detection performance of two radar systems under different RCS fluctuation models is simulated and compared.The simulation results show that the detection performance of distributed MIMO radar is better than that of traditional phased array radar in the case of high signal-to-noise ratio,and the detection performance of distributed MIMO radar is comparable to that of traditional phased array radar in the case of low signal-to-noise ratio.2.Aiming at the independent scattering coefficient between the pair of transmitting and receiving antennas,the distributed MIMO radar data fusion detection algorithm based on Generalized Likelihood Ratio Test(GLRT)is studied:centralized data fusion detection method and double threshold data fusion.Based on the existing signal model,the detection probability expressions and false alarm probability expressions of the two data fusion detection methods are derived,and the detection performance of the two data fusion detection methods is compared through simulation experiments.The simulation results show that compared with the traditional radar detection algorithm,the centralized data fusion detection method can achieve the same detection effect with less signal-to-noise ratio,and the detection performance of the radar system has greatly improved.However,the large amount of data generated by each radar receiving station not only brings great pressure to the communication network,but also brings great computational burden to the data fusion center.Dual threshold data fusion detection method can reduce the amount of data in the detection system by setting local detection threshold.When the local detection threshold is low,the dual-threshold data fusion detection method can maintain the same detection accuracy as the centralized data fusion detection method.However,when the local detection threshold is set higher,the dual threshold data fusion detection method is lacking in detection accuracy compared with the centralized data fusion detection method.3.A distributed MIMO radar data fusion detection algorithm based on multi-dimensional characteristics of targets is proposed.Dual threshold data fusion detection method and centralized data fusion detection method have their own advantages and disadvantages in terms of detection performance and data quantity.In addition,the traditional Constant False Alarm Rate(CFAR)detection method has low accuracy for weak and small echo target recognition.Distributed MIMO radar data fusion detection algorithm based on multi-dimensional characteristics of target combines Markov of echo data with distributed MIMO radar data fusion detection.It makes full use of the multi-dimensional characteristics of target,such as radial distance,radial velocity,echo amplitude,signal-to-noise ratio and Doppler frequency,which are processed by a single station,to process the echo data structurally.By deducing the state transition probability matrix between echo data,the probability that the echo data belongs to the real target is calculated.Finally,the probability value is compared with the threshold value to determine whether the echo data belongs to the real target,and the trajectory of the real target is determined by reverse search.The simulation results show that the distributed MIMO radar data fusion detection algorithm based on multi-dimensional characteristics of the target can reduce the signal-to-noise ratio required for detection.The CFAR probability detection optimizes the detection performance of dim and small targets.By calculating the state transition probability,the influence of noise on target detection is eliminated,and the probability of large SNR noise being misjudged as real target is reduced.The phenomenon of small SNR target being misjudged is also improved.Moreover,the data fusion detection algorithm based on multi-dimensional characteristics of targets proposed in this chapter combines the advantages of centralized data fusion detection method and dual-threshold data fusion detection method.While ensuring the detection accuracy,it greatly reduces the computation of the detection system and improves the overall performance of the distributed MIMO radar detection system. |
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