An Efficient Estimator for Source Localization Using TD and AOA Measurements in MIMO Radar Systems

Optimisation of practically constrained waveforms for Rician target detection with multiple‐input‐multiple‐output radar

“…The authors declare that they have no known conflict of interest that could have appeared to influence the work reported in this paper. The waveform can be synthesised by setting R H = 0 in Equation (12). Then it can be checked that the waveform admits a closed-form solution:…”

“…Different from the phased array radar, colocated MIMO radar can transmit arbitrary waveforms to gain extra degrees of freedom so as to achieve better detection and estimation performance [4][5][6]. MIMO radar has a lot of advantages, such as in fading mitigation [7], resolution enhancement [8][9][10][11], higher target localisation accuracy [12][13][14] and improved interference and jamming suppression capability [15]. In addition, MIMO radar has excellent performance in multiple target detection [16,17], parameter estimation [18][19][20], parameter identifiability [4] and target tracking [21][22][23] as well.…”

Localization algorithm of three-dimensional position and velocity for moving target in multistatic radar system

“…A number of proposed methods use a single parameter or combination of two parameters (such as TD-AOA, TD-DS) to determine the target position, or velocity simultaneously. Literatures [2][3][4] optimize the calculation process of the classic two-step weighted least squares (TSWLS) and integrate different measurement parameters to get the closed form solution of the target position and velocity. Reference [5] formulates the localization model as a semi-definite programming problem and utilize the semi-definite relaxation (SDR) for resolution, but this algorithm introduces additional bias and not tight enough to deal with the non-convex constructure.…”

Overcoming Unknown Measurement Noise Powers in Multistatic Target Localization: A Cyclic Minimization and Joint Estimation Algorithm

“…For target position estimation, some studies utilize TD information as the measurement, and presents some effective localization algorithms such as the singular value decomposition (SVD) algorithm in [5], the two stage weighted least squares (2WLS) algorithm in [6], the convex optimization in [7], the majorization-minimization in [8], to estimate the target position. When the receiver has goniometric capability, TD and AOA measurements can be jointly used to enhance localization accuracy, such as the 2WLS solution in [9], the one stage weighted least squares (1WLS) solution in [10], [11], quadratic constraint [12], and the convex solution in [13]. For the position and velocity estimation of moving targets, DS measurement can also be used in addition to TD measurement to estimate accurately the target position and velocity, such as the iterative Lagrange programming neural network based algorithm in [14], the TSWLS and its variants in [15][16][17][18][19].…”

“…For the position and velocity estimation of moving targets, DS measurement can also be used in addition to TD measurement to estimate accurately the target position and velocity, such as the iterative Lagrange programming neural network based algorithm in [14], the TSWLS and its variants in [15][16][17][18][19]. However, the above mentioned studies employ either one (TD-based localization [5][6][7][8]) or two (hybrid TD-AOA localization [9][10][11][12][13], hybrid TD-DS localization [14][15][16][17][18][19]) of the TD, DS and AOA measurements for target location. The hybrid TD-DS-AOA localization method, which uses all three types of measurements, offers theoretically higher localization accuracy and robustness by providing estimates for both target position and velocity.…”