Hatem Hmam

Optimal angular sensor separation for AOA localization

This paper establishes the angular separation requirements for angle-of-arrival (AOA) sensors in order to achieve the best mean squared error (MSE) localization performance for arbitrary but fixed sensor ranges. Optimal sensor placement for localization arises in several practical applications such as trajectory optimization for moving sensor platforms, e.g., unmanned aerial vehicles (UAVs). In optimal UAV path planning the angular separation between UAVs is an important parameter that has a significant impact on fuel efficiency and inter-UAV distance constraints. The paper shows that optimal angular sensor separation is in general not unique, and that when all sensors are equidistant from the emitter, there may exist optimal sensor configurations with non-uniform angular sensor separation in addition to equiangular separation. The results of the paper are illustrated with extensive simulation studies.

Optimal non-iterative pose estimation via convex relaxation

In this paper we present a convex relaxation method that globally solves for the camera position and orientation given a set of image pixel measurements associated with a scene of reference points of known three-dimensional positions. The approach formulates the pose optimization problem as a semidefinite positive relaxation (SDR) program. A comprehensive comparative performance analysis, carried out in the computer simulations section, demonstrates the superior performance of the relaxation method over existing approaches. The computational complexity of the method is O(n), where n is the number of reference points, and is applicable to both coplanar and non-coplanar reference point configurations. The average run-time recorded is 50ms for an average point count of 100.

Passive Localization of Scanning Emitters

The joint estimation problem of the scan rate and the position of a scanning emitter is investigated. The proposed approach takes advantage of the uniform rotating motion of the antenna main beam as it sweeps across a number of separate receivers. A joint estimator is developed based on a nonlinear least-squares (NLS) estimation. Several simulation examples are presented to compare the performance of the proposed estimator with the Cramer-Rao lower bound (CRLB).

Cooperative Self-Localization of Mobile Agents

This paper considers the problem of localizing multiple agents, e.g. unmanned aerial vehicles (UAVs), robots, etc., moving in two-dimensional space when the known data comprise 1) the inter-agent distances, and 2) the angle subtended at each agent by lines drawn from two landmarks at known positions. Later it is shown that this result has direct application in a different general robotic problem, viz. robot-to-robot relative pose determination (relative reference frame determination), using measured distances. The methods proposed are validated through simulations and experiments.

Centralized path planning for unmanned aerial vehicles with a heterogeneous mix of sensors

This paper is concerned with real-time optimal UAV path planning in a multi-emitter geolocation environment. All UAVs are assumed to be controlled by a central processing unit. A UAV waypoint-update/steering algorithm is developed based on maximizing the determinant of Fisher information matrix for localization of stationary emitters. Soft and hard geometric constraints for threat/collision avoidance are also implemented. An effective joint path optimization and dynamic sensor allocation algorithm is proposed to handle communication bandwidth constraints. The performance of the developed steering algorithm is illustrated with extensive simulation examples.

Approximating the SNR value in detection problems

The inverse problem of finding the required signal-to-noise ratio (SNR) given a set of probability parameters and number of samples is a nontrivial problem. Several past attempts have proposed simple approximations for the SNR, but the achieved accuracy varied across the parameter space and was at times poor. The approximation equations proposed here are considerably more accurate over a larger parameter space.

Distributed path optimization of multiple UAVs for AOA target localization

This paper is concerned with unmanned aerial vehicle (UAV) path optimization for AOA target localization via distributed processing. A distributed UAV path optimization algorithm based on gradient descent method is developed using the diffusion extended Kalman filter (DEKF). With this algorithm, a group of UAVs can realize self-adaptive path optimization in order to improve estimation performance. The presented distributed path optimization strategy aims to minimize the estimation mean squared error (MSE) by minimizing the trace of the error covariance matrix. The UAV dynamic communication topology caused by communication range constraint is analyzed. Furthermore, the UAV 6-degree-of-freedom (DOF) dynamic modeling is taken into consideration to generate realistic UAV trajectories. The properties and effectiveness of the proposed algorithm are discussed and verified with simulation examples.

Mobile Platform Self-Localization

This paper proposes a detailed bearing-only algorithm for mobile platform self-localization with respect to three beacons or landmarks whose positions are known. The derivation process of the mobile platform position is split into two stages, where the first stage determines the location region of the platform whereas the second stage fine-tunes this coarse location into one point within this region and calculates the platform heading. Previous approaches to self-localization either have applicability limitations or do not propose a formulation that provides a clear insight into the behaviour of the platform position solution in terms of platform/beacon geometry.

SNR calculation procedure for target types 0, 1, 2, 3 --

This paper presents approximation equations for the signal-to-noise ratio (SNR) for Marcum, Swerling type I, II and III targets. The proposed approximations hold over the entire detection probability interval [0.05 0.999] and for all false alarm probabilities ranging from 10/sup -3/ to 10/sup -12/, with the number of pulses M varying from 1 to 20. The achieved accuracy is within 0.05 dB for most cases.

Distributed pseudolinear estimation and UAV path optimization for 3D AOA target tracking

We address the problem of angle-of-arrival (AOA) target tracking using multiple unmanned aerial vehicles (UAVs) in three-dimensional (3D) space. A distributed 3D AOA target tracking method is proposed consisting of a distributed estimator and path optimization algorithm for multiple UAVs. First a novel 3D distributed pseudolinear Kalman filter (DPLKF) is developed to improve the stability of an extended Kalman filter solution. The DPLKF consists of two coupled filters; viz., an xy-DPLKF and a z-DPLKF. The bias problem of the 3D DPLKF is analyzed and a bias reduction method is proposed. A distributed path optimization algorithm is developed subject to communication range constraints and no-fly zones. This algorithm computes UAV waypoints using gradient-descent optimization on the xy-plane and grid search along the z-axis. To improve the tracking performance, the trace of the error covariance matrix is minimized. The properties and effectiveness of the proposed strategy are discussed and validated with simulation examples. HighlightsA novel distributed pseudolinear Kalman filter (DPLKF) is developed for 3D AOA target tracking.The bias problem of the 3D DPLKF is analyzed and a bias reduction method is proposed.A distributed path optimization algorithm is developed subject to communication range constraints and no-fly zones.