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Two gains play key roles in recently developed‎ MIMO ‎wireless communication systems‎: ‎"spatial diversity‎" ‎gain and "spatial multiplexing‎" ‎gain‎. ‎The diversity gain refers to the capability to decrease the error rate of the MIMO channel‎, ‎while the multiplexing gain implicitly refers to the amount of increase in the capacity of the MIMO channel‎. ‎It has been shown that there is a fundamental tradeoff between these two types of gains‎, ‎meaning interplay between increasing reliability (via an increase in the diversity gain) and increasing data rate (via an increase in the multiplexing gain)‎. ‎On the other hand‎, ‎recently‎, ‎MIMO radars have attracted much attention for their superior ability to enhance the system's performance‎. ‎As a MIMO system‎, ‎it is expected that the mentioned diversity-multiplexing tradeoff exists in a MIMO radar‎, ‎too‎. ‎In this paper‎, ‎this tradeoff is studied and verified in MIMO radars with widely separated antennas‎. ‎In more details‎, ‎it will be shown that increasing dependency between transmit-receive links results in higher diversity gain and lower multiplexing gain‎, ‎and vice versa‎. ‎Then‎, ‎the optimal tradeoff is introduced‎, ‎i.e.‎, ‎the conditions that the MIMO radar system should have‎, ‎so that the diversity-multiplexing tradeoff is at its optimum point‎, ‎are driven‎.

High resolution range profile (HRRP) is being known as one of the most powerful tools for radar target recognition. The main problem with range profile for radar target recognition is its sensitivity to aspect angle. To overcome this problem, consecutive samples of HRRP were assumed to be identically independently distributed (IID) in small frames of aspect angles in most of the related works. Here, considering the physical circumstances of maneuver of an aerial target, we have proposed dynamic system which models the short dependency between consecutive samples of HRRP in segments of the whole HRRP sequence. Dynamic system (DS) is used to model the sequence of PCA (principal component analysis) coefficients extracted from the sequence of HRRPs. Considering this we have proposed a model called PCA+DS. We have also proposed a segmentation algorithm which segments the HRRP sequence reliably. Akaike information criterion (AIC) used to evaluate the quality of data modeling showed that our PCA+DS model outperforms factor analysis (FA) model. In addition, target recognition results using simulated data showed that our method based on PCA+DS achieves better recognition rates compared to the method based on FA.