Untangling first and second order statistics contributions in multipath scenarios

Lubeigt, Corentin; Ortega, Lorenzo; Vilà‐Valls, Jordi; Chaumette, Éric

doi:10.1016/j.sigpro.2022.108868

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…This work investigates the DOA estimation performance degradation due to the presence of radar echo model misspecification, where the assumed model by the estimator does not consider the presence of multipath. Deriving estimators which consider the accurate multipath propagation model is impractical for the following reasons: 1) the multipath geometry is typically unknown in practical applications, 2) even if the model is perfectly known, the resulting DOA estimation algorithm would be significantly more computationally complex compared to the conventional approaches, since it is required to estimate the multipath parameters jointly, and 3) in some scenarios, more precise models which include additional parameters, may provide worse performance compared to misspecified models (see for example [51,57]).…”

Section: Discussionmentioning

confidence: 99%

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Misspecified Cramér-Rao Bound for Multipath Model in MIMO Radar

Moshe

Bilik

Tabrikian

2022

2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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Autonomous driving and advanced active safety features require accurate high-resolution sensing capabilities.Automotive radars are the key component of the vehicle sensing suit. However, when these radars operate in proximity to flat surfaces, such as roads and guardrails, they experience a multipath phenomenon that can degrade the accuracy of the direction-of-arrival (DOA) estimation. Presence of multipath leads to misspecification in the radar data model, resulting in estimation performance degradation, which cannot be reliably predicted by conventional performance bounds. In this paper, the misspecified Cramér-Rao bound (MCRB), which accounts for model misspecification, is derived for the problem of DOA estimation in the presence of multipath which is ignored by the estimator. Analytical relations between the MCRB and the Cramér-Rao bound are established, and the DOA estimation performance degradation due to multipath is investigated. The results show that the MCRB reliably predicts the asymptotic performance of the misspecified maximum-likelihood estimator and therefore, can serve as an efficient tool for automotive radar performance evaluation and system design.

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Section: Discussionmentioning

confidence: 99%

“…[49,50]). The MCRB for range and Doppler estimation in ranging applications, such as globlal navigation satellite systems (GNSS) or single-input single-output (SISO) radar/sonar was introduced in [51].…”

Section: Introductionmentioning

confidence: 99%

Misspecified Cramér-Rao Bound for Multipath Model in MIMO Radar

Moshe

Bilik

Tabrikian

2022

2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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“…The maximum number of iterations is set to 20 and the number of Monte Carlo is set to 1000 runs. In the results one can observe i) the √ CRB (refer to [11]), which represents the asymptotic estimation performance of the parameters without any source of interference, ii) the √ M CRB + Bias 2 which represents the asymptotic estimation performance of the parameters with an interference source (refer to [12], [18]) and describes the MSE of the misspecified maximum likelihood estimator [19], and iii) the Root MSE ( √ M SE) of the proposed EM algorithm. Note that the EM algorithm is not biased and it is able to correct the effects generated by the interference.…”

Section: Methodsmentioning

confidence: 99%

An EM Approach for GNSS Parameters of Interest Estimation Under Constant Modulus Interference

Lesouple,

Ortega

2023

2023 31st European Signal Processing Conference (EUSIPCO)

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Interferences are an important threat for applications relying on Global Navigation Satellite Systems (GNSS). Interferences degrade GNSS performance, and can lead to denial of service. The most notable intentional interference family is characterized by its constant envelope, e.g. chirp and tone interferences. Due to its simple structure, the space to search the interference contribution yields to complex circles, allowing the introduction of some latent variables related to those circles. In order to mitigate the interference effect, we compute the maximum likelihood estimator of the parameters of interest (time delay and Doppler shift) in presence of those latent variables. Thus, we resort to the Expectation Maximization algorithm which has already been proved to be efficient in such cases. Experiments conducted on synthetic signals highlight the efficiency of the proposed algorithm.

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“…• the √ CRB (as referred to in [11]), which signifies the asymptotic estimation performance of the parameters in the absence of interference • the MCRB + Bias 2 , representing the asymptotic estimation performance of the parameters when the receiver is unaware of the presence of interference (as discussed in [13,27]). This includes the root MSE of the misspecified maximum likelihood esti- mator, as mentioned in [12].…”

Section: • Scenariomentioning

confidence: 99%

“…To evaluate the performance of our proposed algorithm, we compare it against the theoretical limits of time-delay and Doppler shift estimation under the following particular cases: i) we consider the scenario where no interference corrupts the GNSS signal. Note that this is the best possible scenario and the theoretical limits are provided by the Cramér-Rao bound (CRB) derived in [11], ii) the misspecified conditional model [12,13]. In this scenario, the signal is corrupted by an interference but the receiver estimates the parameters of interest without considering it.…”

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confidence: 99%

Bayesian EM approach for GNSS parameters of interest estimation under constant modulus interference

Lesouple,

Ortega

2024

EURASIP J. Adv. Signal Process.

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Interferences pose a significant risk to applications that rely on global navigation satellite systems (GNSSs). They have the potential to degrade GNSS performance and even result in service disruptions. The most notable type of intentional interference is characterized by a constant modulus, such as chirp and tone interferences. These interferences have a straightforward structure, leading to the creation of complex circles when attempting to identify their contribution. To address the interference and improve the situation, we calculate the maximum likelihood estimator for the relevant parameters (time delay and Doppler shift) while considering the presence of these latent variables. To achieve this, we employ the expectation–maximization algorithm, which has previously demonstrated its effectiveness in similar scenarios. Experiments conducted using synthetic signals confirm the efficiency of the proposed algorithm.

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Untangling first and second order statistics contributions in multipath scenarios

Cited by 12 publications

References 8 publications

Misspecified Cramér-Rao Bound for Multipath Model in MIMO Radar

Misspecified Cramér-Rao Bound for Multipath Model in MIMO Radar

An EM Approach for GNSS Parameters of Interest Estimation Under Constant Modulus Interference

Bayesian EM approach for GNSS parameters of interest estimation under constant modulus interference

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