Xianrong Wan

Spatial difference smoothing for coherent sources location in MIMO radar

In this paper, we develop a group of spatial difference smoothing (SDS) techniques in multiple-input multiple-output (MIMO) radar to facilitate the directions-of-departure (DODs) and directions-of-arrival (DOAs) estimation of coherent signals under unknown correlated noise. The conventional SDS technique, which exploits the difference between the forward spatially smoothed covariance matrix and the backward one, has been used to reduce noise for coherent sources location. However, it fails when the number of sources is odd. To solve the problem, we refine the SDS technique into asymmetric SDS (A-SDS) technique. Meanwhile, two smoothing methods exist in MIMO radar, which are transmit-receive spatial smoothing relying on the rotational invariance property of array and transmit-receive diversity smoothing brought by waveform diversity. Then the general SDS and A-SDS techniques using the spatial smoothing are specialized into transmit-receive diversity SDS (TRD-SDS) and asymmetric TRD-SDS (A-TRD-SDS) techniques using the diversity smoothing. Further, the forward, backward, and combined forward-backward forms of these four kinds of smoothing techniques are discussed. Based on them, eigenstructure algorithms are applied for DOD and DOA estimation. The proposed techniques can be used in bistatic or monostatic MIMO radar under spatially colored or white noise, and their effectiveness is confirmed by simulations. Four kinds of spatial difference smoothing (SDS) techniques in MIMO radar are developed to estimate the DOD and DOA of coherent sources under unknown spatially colored noise.The failure of conventional SDS is analyzed, and modified techniques are proposed.The diversity smoothing in MIMO radar is incorporated in SDS techniques.The forward, backward, and combined forward-backward forms of these SDS techniques are introduced.The aforementioned techniques are compared and extended from bistatic to monostastic MIMO radar.

Adaptive weight matrix design and parameter estimation via sparse modeling for MIMO radar

Adaptive weight matrix design and parameter estimation via sparse modeling are proposed for colocated multiple-input multiple-output radar.The sensing matrix design and transmit weight matrix are implemented in an iterative cyclic way.The angle-amplitude estimation performance of the proposed approach is examined by analyzing the CramerRao lower bound.Numerical results demonstrate the significant performance improvement by the proposed design approach. Although sparse representation and sparse recovery algorithms for colocated multiple-input multiple-output (MIMO) radar have received much attention, incoherence of the sensing matrix received few discussions. In this paper, we propose adaptive weight matrix design and parameter estimation via sparse modeling to improve the recovery performance for MIMO radar. First, a sparse framework is formulated for the MIMO array with decoupled transmit weight matrix and steering matrix. Next, a two stage method is proposed to optimize the two matrices to improve the DOA estimation performance. Finally, a sparse recovery approach based on lq (0

Side Peak Interference Mitigation in FM-Based Passive Radar Via Detection Identification

In frequency-modulation (FM)-based passive radar, the strong side peaks randomly appearing in the ambiguity function will generate a false alarm of target detection. To mitigate this side peak interference, this paper starts with a detailed analysis of the structure and ambiguity function of FM stereo signal. Then, it expounds the formation and characteristics of side peaks, together with a side peak identification method, which identifies and discards the false detections of the same range and Doppler caused by side peaks, respectively. The performance analysis, conducted using both simulated data and real recorded datasets, proves that the proposed method can eliminate the false targets caused by side peaks thus improving the detection performance in FM-passive radar.

Joint Placement of Transmitters and Receivers for Distributed MIMO Radars

Geometry configuration is crucial to the system performance in distributed multiple-input–multiple-output (MIMO) radars. Sensor placement in distributed MIMO radars is different from the conventional sensor placement problem as transmitters and receivers need to cooperate with each other to perform sensing. In this paper, we establish a combinatorial optimization model for the joint placement of transmitters and receivers. The proposed algorithm first transforms the original model into an equivalent model with convex constraints via convex relaxation. Then, the nonconvex objective function is further replaced with a new convex surrogate. The surrogate is shown to converge to a good approximation of the original optimal solution. The performance of the proposed algorithm is validated using numerical simulations.