Antenna allocation for MIMO radars with collocated antennas (original) (raw)
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This paper concerns with the sensor management problem in collocated Multiple-Input Multiple-Output (MIMO) radars. After deriving the Cramer-Rao Lower Bound (CRLB) as a performance measure, the antenna allocation problem is formulated as a standard Semi-definite Programming (SDP) for the single-target case. In addition, for multiple unresolved target scenarios, a sampling-based algorithm is proposed to deal with the non-convexity of the cost function. Simulations confirm the superiority of the localization results under the optimal structure.
Localization, Tracking, and Antenna Allocation in Multiple-Input Multiple-Output Radars
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This thesis concerns with the localization, tracking, and sensor management in the Multiple-Input Multiple-Output (MIMO) radar systems. The collocated and widely-separated MIMO radars are separately discussed and the signal models are derived for both structures. The first chapter of the thesis is dedicated to the tracking and localization in collocated MIMO radars. A novel signal model is first formulated and the localization algorithm is developed for the derived signal model to estimate the location of multiple targets falling in the same resolution cell. Furthermore, a novel tracking algorithm is proposed in which the maximum bound on the number of uniquely detectable targets in the same cell is relaxed. The performance of the tracking and localization algorithms is finally evaluated using the tracking Posterior Cramer-Rao Lower Bound (PCRLB). After showing the impact of the antennas position on the localization CRLB, a novel sensor management technique is developed for the coll...
Target localization accuracy and multiple target localization: Tradeoff in MIMO radars
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Coherent multiple-input multiple-output (MIMO) radar systems with co-located antennas, form monostatic virtual arrays by discrete convolution of a bistatic setup of transmitters and receivers. Thereby, a trade-off between maximum array dimension, element spacing and hardware efforts exists. In terms of estimating the direction of arrival, the covariance matrix of the array element signals plays an important role. Here, minimum redundancy arrays aim at a hardware reduction with signal reconstruction by exploiting the Toeplitz characteristics of the covariance matrix. However, the discrete spatial convolution complicates the finding of an optimal antenna setup with minimum redundancy. Combinatorial effort is the consequence. This paper presents a possible simplified algorithm in order to find MIMO array setups of maximum dimension with minimum redundancy.
Cramer Rao bound on target localization estimation in MIMO radar systems
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This paper presents an analysis of target localization accuracy, attainable by the use of MIMO (Multiple-Input Multiple-Output) radar systems, configured with multiple transmit and receive antennas, widely distributed over a given area. The Cramer-Rao lower bound (CRLB) for target localization is developed for coherent processing. It is shown that the localization estimation accuracy can be approximated as inversely proportional to the carrier frequency in the coherent case. Evaluation of the relation between sensors locations, target location, and localization accuracy is provided by a metric known as geometric dilution of precision (GDOP). GDOP contours map the relative performance accuracy for a given layout of radars over a given geographic area.
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This paper studies the velocity estimation performance for multiple-input multiple-output (MIMO) radar with widely spaced antennas. We derive the Cramer-Rao bound (CRB) for velocity estimation and study the optimized system/configuration design based on CRB. General results are presented for an extended target with reflectivity varying with look angle. Then detailed analysis is provided for a simplified case, assuming an isotropic scatterer. For given transmitted signals, optimal antenna placement is analyzed in the sense of minimizing the CRB of the velocity estimation error. We show that when all antennas are located at approximately the same distance from the target, symmetrical placement is optimal and the relative position of transmitters and receivers can be arbitrary under the orthogonal received signal assumption. In this case, it is also shown that for MIMO radar with optimal placement, velocity estimation accuracy can be improved by increasing either the signal time duration or the product of the number of transmit and receive antennas.
Target localization accuracy gain in MIMO radar-based systems
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This paper presents an analysis of target localization accuracy, attainable by the use of MIMO (Multiple-Input Multiple-Output) radar systems, configured with multiple transmit and receive sensors, widely distributed over a given area. The Cramer-Rao lower bound (CRLB) for target localization accuracy is developed for both coherent and noncoherent processing. Coherent processing requires a common phase reference for all transmit and receive sensors.
Target localization techniques and tools for MIMO radar
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MIMO (multiple-input multiple-output) radar refers to an architecture that employs multiple, spatially distributed transmitters and receivers. The widely spaced antenna structure suggests unique features that set MIMO radar apart from other radar systems, making it strongly related to MIMO communications. The widely separated transmit/receive antennas capture different aspects of the target cross section that can be exploited to obtain diversity gain for detection and estimation of the target's various parameters, such as angle of arrival, and Doppler.
IEEE Transactions on Signal Processing, 2022
A new algorithm for 3D localization in multiplatform radar networks, comprising one transmitter and multiple receivers, is proposed. To take advantage of the monostatic sensor radiation pattern features, ad-hoc constraints are imposed in the target localization process. Therefore, the localization problem is formulated as a non-convex constrained Least Squares (LS) optimization problem which is globally solved in a quasi-closedform leveraging Karush-Kuhn-Tucker (KKT) conditions. The performance of the new algorithm is assessed in terms of Root Mean Square Error (RMSE) in comparison with the benchmark Cramer Rao Lower Bound (CRLB) and some competitors from the open literature. The results corroborate the effectiveness of the new strategy which is capable of ensuring a lower RMSE than the counterpart methodologies especially in the low Signal to Noise Ratio (SNR) regime.
Joint Power allocation and target detection in distributed MIMO radars
IET Radar, Sonar & Navigation, 2021
Here, a power allocation method jointly optimising the total transmit power and the probability of interception is proposed based on target detection applications in distributed multiple-input multiple-output (MIMO) radars. Power allocation is performed by solving a non-linear constrained optimisation problem formulated to minimise the total transmit power based on target detection applications. Then, the Neyman-Pearson detector is designed under the Rayleigh scatter model and the Lagrangian method is used to solve the optimisation problem. Moreover, a positioning algorithm optimising the placement of transmitters and receivers in distributed MIMO radars is applied to minimise the total transmit power satisfying the target detection criterion. The results show that uniform power allocation is not the optimal strategy and the proposed power allocation algorithm provides either better target detection performance for the same power budget, or requires less power to provide the same detection performance. Numerical simulations and the theoretic analysis confirm the effectiveness of the proposed algorithm. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.