Reduced‐dimension DOD and DOA estimation through projection filtering in bistatic MIMO radar with jammer discrimination

Yeo, Kwanggoo; Chung, Young-Seek; Yang, Hoon‐Gee; Kim, Jong-Mann; Chung, Wonzoo

doi:10.1049/iet-rsn.2016.0627

Cited by 14 publications

(9 citation statements)

References 11 publications

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“…In this section, we develop an estimator to solve the conditional expectations in (5) and (6). To find the state estimate x k in (5), we first invoke the projection theorem [25] and average over the measurement models conditioned on information ℐ N to remove the uncertainty in the packet arrival process.…”

Section: Estimator Designmentioning

confidence: 99%

“…Due to such ubiquitous nature of such problems, state estimation subject to partial information is a topic of practical significance. These estimation problems are difficult to handle in a theoretical framework because of the time-varying and statedependent nature of packet arrival statistics, yet are of practical significance in many applications [5][6][7]. Most existing optimal estimators for packet loss assume that the packet arrival process has stationary statistics, which is not true for state-dependent packet losses [8].…”

Section: Introductionmentioning

confidence: 99%

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Kalman filtering with state‐dependent packet losses

Thapliyal

Nandiganahalli

Hwang

2019

IET Control Theory & Applications

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This study addresses the problem of state estimation for discrete-time, linear time invariant systems subject to packet losses, which occur in specific regions of the state space. Most practical estimation problems are characterised by occurrences of loss of observation packets, which makes the packet arrival process a non-stationary statistic, making the analysis and design of such an estimator challenging. This estimation problem subject to state-dependent packet losses is formulated using a state-dependent hybrid measurement model and solved using the projection theorem-based approach to obtain minimum mean square error state estimates. By systematically utilising the a priori information of the regions where the packet loss is likely to occur, the proposed estimator takes the Kalman filter structure with the modified algebraic Riccati iteration for the error covariance matrix being stochastic due to the probabilistic packet arrival process. Finally, the proposed estimator is demonstrated using an illustrative two-dimensional aircraft tracking example with state-dependent packet loss and is shown to have improved performance over the baseline packet loss algorithm.

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Section: Estimator Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Kalman filtering with state‐dependent packet losses

Thapliyal

Nandiganahalli

Hwang

2019

IET Control Theory & Applications

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“…The challenge lies in the fact that MSRS has high spatial resolution, because it synthesises large virtual antenna aperture by fusing received signals from widely separated radar systems. The enhanced angular resolution enables MSRS to distinguish the radar returns and disruptive jamming signals in spatial domain, e.g., by using some jamming suppression methods like subspace-based method [4], blind separation method [5] or adaptive filter method [6].…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation for Random Selection of Distributed Jammer Towards Multistatic Radar System

2021

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The random array subset selection (RASS) jammer, which randomly selects a subset out of the transmit array antenna from time to time, was verified to effectively counter against multistatic radar system (MSRS). However, the countermeasure ability of RASS is restricted, because the probabilities of activating antenna elements for transmission are constrained to be identical. To further enhance the countermeasure ability, we propose the resource allocation of distributed jammer under random selection (RADJRS) in this paper, where antenna elements of the jammer are activated randomly with different probabilities, abandoning the previous constraint and generalizing RASS. We establish an optimization model with respect to the selection probabilities, whose objective function is minimizing the output-to-input jamming energy ratio of the jamming suppression method used in MSRS. This non-convex optimization model is then relaxed into a convex problem, facilitating the solution of the problem. Numerical results verify the feasibility of the relaxation and demonstrate that RADJRS outperforms the RASS when fighting against MSRS.

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“…MSRS relies on jamming suppression methods, as discussed in some existing literature [2][3][4]. The basic idea behind these methods is to use the high spatial resolution of MSRS, resulted from the large virtual aperture, to identify radar echoes from the strong jamming signals.…”

Section: Introductionmentioning

confidence: 99%

A Random Antenna Subset Selection Jamming Method against Multistatic Radar System

Wang

Liu

Huang

2020

2020 IEEE Radar Conference (RadarConf20)

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multistatic radar system (MSRS) is considered an effective scheme to suppress mainlobe jamming, since it has higher spatial resolution enabling jamming cancellation from spatial domain. To develop electronic countermeasures against MSRS, a random array subset selection (RASS) jamming method is proposed in this paper. In the RASS jammer, elements of the array antenna are activated randomly, leading to stable mainlobe and random sidelobes, different from the traditional jammer that applies the complete antenna array enjoying constant mainlobe and sidelobes. We study the covariance matrix of jamming signals received by radars, and derive its rank, revealing that the covariance matrix is of full rank. We also calculate the output jamming to signal and noise ratio (JSNR) after the subspacebased jamming suppression methods used in MSRS under the proposed jamming method, which demonstrates that the full rank property invalidates such suppression methods. Numerical results verify our analytical deduction and exhibit the improved countermeasure performance of our proposed RASS jamming method compared to the traditional one.

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Reduced‐dimension DOD and DOA estimation through projection filtering in bistatic MIMO radar with jammer discrimination

Cited by 14 publications

References 11 publications

Kalman filtering with state‐dependent packet losses

Kalman filtering with state‐dependent packet losses

Resource Allocation for Random Selection of Distributed Jammer Towards Multistatic Radar System

A Random Antenna Subset Selection Jamming Method against Multistatic Radar System

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