“…So, it only remains the calculation of functions ζ θ (µ, ρ) from Eqn. (18). Since R n = σ 2 n I, one obtains…”
Section: F Proof Of Eqn (48) (49) and (50)mentioning
confidence: 99%
“…(18). In this analysis, the vector µ takes the value µ i at the i th row and zero elsewhere and the vector ρ takes the value ρ j at the j th row and zero elsewhere (of course, one can has i = j).…”
Section: ) Unconditional Observation Model Mmentioning
confidence: 99%
“…(44) and after an optimization over the test points. The optimization over the test points can be done over a search grid or by using the ambiguity diagram of the array in order to reduce significantly the computational cost (see [18], [27], [34], [45], [46]). …”
In this paper, the Weiss-Weinstein bound is analyzed in the context of sources localization with a planar array of sensors. Both conditional and unconditional source signal models are studied. First, some results are given in the multiple sources context without specifying the structure of the steering matrix and of the noise covariance matrix.Moreover, the case of an uniform or Gaussian prior are analyzed. Second, these results are applied to the particular case of a single source for two kinds of array geometries: a non-uniform linear array (elevation only) and an arbitrary planar (azimuth and elevation) array.
“…So, it only remains the calculation of functions ζ θ (µ, ρ) from Eqn. (18). Since R n = σ 2 n I, one obtains…”
Section: F Proof Of Eqn (48) (49) and (50)mentioning
confidence: 99%
“…(18). In this analysis, the vector µ takes the value µ i at the i th row and zero elsewhere and the vector ρ takes the value ρ j at the j th row and zero elsewhere (of course, one can has i = j).…”
Section: ) Unconditional Observation Model Mmentioning
confidence: 99%
“…(44) and after an optimization over the test points. The optimization over the test points can be done over a search grid or by using the ambiguity diagram of the array in order to reduce significantly the computational cost (see [18], [27], [34], [45], [46]). …”
In this paper, the Weiss-Weinstein bound is analyzed in the context of sources localization with a planar array of sensors. Both conditional and unconditional source signal models are studied. First, some results are given in the multiple sources context without specifying the structure of the steering matrix and of the noise covariance matrix.Moreover, the case of an uniform or Gaussian prior are analyzed. Second, these results are applied to the particular case of a single source for two kinds of array geometries: a non-uniform linear array (elevation only) and an arbitrary planar (azimuth and elevation) array.
“…The role of the variance of the APSs in the estimation process may be described through a threshold like behavior. The fact that this aspect is not handled by the HCRB is an intrinsic limitation of the method, whereas other bounds have been proposed in literature that account properly for threshold effects (27). Practical conditions for retaining the validity of the results will be provided in the following.…”
Section: On the Validity Of The Hcrb For Insar Applicationsmentioning
confidence: 99%
“…The HCRB (26), (27), (28) applies in the case where some of the unknowns are deterministic and others are random; it unifies the deterministic and Bayesian CRB in such a way as to simultaneously bound the covariance matrix of the unbiased estimates of the deterministic parameters and the mean square errors on the estimates of the random variables (26), (27). Let θ be an unbiased estimator of the deterministic parameters θ, and denote α an estimator of the random variables α.…”
Section: The Hybrid Cramér-rao Bound For Insarmentioning
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