Abstract
Conventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars require the inversion of the covariance matrix of the received spatial samples. In these algorithms, the number of received snapshots should be at least equal to the size of the covariance matrix. For large size MIMO antenna arrays, the inversion of the covariance matrix becomes computationally very expensive. Compressive sensing (CS) algorithms which do not require the inversion of the complete covariance matrix can be used for parameter estimation with fewer number of received snapshots. In this work, it is shown that the spatial formulation is best suitable for large MIMO arrays when CS algorithms are used. A temporal formulation is proposed which fits the CS algorithms framework, especially for small size MIMO arrays. A recently proposed low-complexity CS algorithm named support agnostic Bayesian matching pursuit (SABMP) is used to estimate target parameters for both spatial and temporal formulations for the unknown number of targets. The simulation results show the advantage of SABMP algorithm utilizing low number of snapshots and better parameter estimation for both small and large number of antenna elements. Moreover, it is shown by simulations that SABMP is more effective than other existing algorithms at high signal-to-noise ratio.
Highlights
Colocated multiple-input-multiple-output (MIMO) radars have been extensively studied in literature for surveillance applications
The conventional algorithms like Capon and and-phase estimation (APES) require a large number of snapshots for parameter estimation
These algorithms include sparse Bayes [9], Bayesian compressive sensing (BCS) [10] and the fast Bayesian matching pursuit (FBMP) [11]. Another reduced complexity algorithm based on the structure of the sensing matrix is proposed in [12]. In addition to these algorithms, support agnostic Bayesian matching pursuit (SABMP) is proposed in [13] which assumes that the support distribution is unknown and finds the Bayesian estimate for the sparse signal by utilizing noise statistics and sparsity rate
Summary
Colocated multiple-input-multiple-output (MIMO) radars have been extensively studied in literature for surveillance applications. Ali et al EURASIP Journal on Advances in Signal Processing (2017) 2017:6 statistics are known These algorithms include sparse Bayes [9], Bayesian compressive sensing (BCS) [10] and the fast Bayesian matching pursuit (FBMP) [11]. Another reduced complexity algorithm based on the structure of the sensing matrix is proposed in [12] In addition to these algorithms, support agnostic Bayesian matching pursuit (SABMP) is proposed in [13] which assumes that the support distribution is unknown and finds the Bayesian estimate for the sparse signal by utilizing noise statistics and sparsity rate. To estimate the reflection coefficient and location angle of the target, existing CS algorithms can be utilized by formulating the MIMO radar parameter estimation problem as a sparse estimation problem. The complexity of SABMP algorithm is not much effected by the number of receive antenna elements in the spatial formulation
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