Optimal sensor placement for Doppler shift target localization

Abstract

A key component of cognitive radar is intelligent signal processing which encompasses an algorithmic decisionmaking process for adaptive transmitted waveform selection and online optimal path planning for moving radar platforms in order to deal with non-stationary and uncertain surrounding environments. This paper focuses on optimal radar trajectories in the cognitive radar context. In particular, the optimal sensor placement problem for stationary target localization by multiple moving Doppler-shift radars is considered. A solution based on maximizing the determinant of the Fisher information matrix is proposed and analyzed. The analytical findings of the paper are demonstrated by way of extensive simulations incorporating unmanned aerial vehicles equipped with Doppler radar in a number of different scenarios.

Optimal Geometry Analysis for Target Localization With Bayesian Priors

“…Note that, different to (22), the starting index of the summation within the minimization operation in ( 27) is 1 instead of 0. Therefore, the Bayesian priors have no influence on the optimal geometry analysis and the geometry optimization problem mathematically boils down to that of the classical non-Bayesian target localization in [1].…”

“…Remark 4. The optimality condition stated in (22) of Theorem 2 is more general than the optimality condition ( 12) of [25]. Note that the optimal condition (22) in Theorem 2 reduces to the condition (12) of [25] only if…”

“…Content may change prior to final publication. moving sensor platforms as considered in [22]. This is because the information matrix for such Doppler localization problem can be expressed in the form ( 13) by defining c 2 k = ω 2 k /σ 2 k (ω k is the angular velocity of sensor k with respect to the target) and θ k = ψ k + π/2.…”

“…Focusing on the parameter optimization formulation, optimal sensor-target geometries have been derived for various target localization problems including angle-of-arrival (i.e., bearing) based localization in [1], [2], [9]- [12], time-ofarrival (i.e., range) based localization in [1], [2], [12]- [18], time-difference-of-arrival based localization in [19], [20], received signal strength (RSS) based localization in [2], [12], [21], Doppler based localization in [22], [23], and hybrid-sensor based localization in [24]. In these works, the Cramér-Rao lower bound (CRLB) are used to characterize the target position estimation performance.…”

Automated and Optimized Sensor Deployment using Building Models and Electromagnetic Simulation

“…While the practicality of a system is the key to its widespread adoption in industry, the practical aspects of tracking systems applying to construction have not been adequately addressed, perhaps because such application involve a number of factors beyond accuracy and cost, including deployment, system coordination, system management, and form factor, all of which are thoroughly reviewed in an article by Li et al (2016a). Recent research in various domains including electrical engineering and computer science explored a number of theoretical approaches for sensor deployment, such as multi-objective optimization (Domingo-Perez et al 2016), convex optimization with estimation theory (Moreno-Salinas et al 2013), signal energy loss (Cho et al 2018) and the Fisher information matrix-based optimization (Nguyen and Dogancay 2015). These studies present advanced mathematical algorithmic approaches to solving the complex phenomena between signals and the environment and demonstrate the performance through computer simulation.…”