The reconnaissance mode with the cooperation of two unmanned aerial vehicles (UAVs) equipped with airborne visual tracking platforms is a common practice for localizing a target. Apart from the random noises from sensors, the localization performance is much dependent on their cooperative trajectories. In our previous work, we have proposed a cooperative trajectory generating method that proves better than EKF based method. In this letter, an improved online trajectory generating method is proposed to enhance the previous one. First, the least square estimation method has been replaced with a geometric-optimization based estimation method, which can obtain a better estimation performance than the least square method proposed in our previous work; second, in the trajectory optimization phase, the position error caused by estimation method is also considered, which can further improve the optimization performance of the next way points of the two UAVs. The improved method can well be applied to the two-UAV trajectory planning for corporative target localization, and the simulation results confirm that the improved method achieves an obviously better localization performance than our previous method and the EKF-based method.

The compressive sensing has been applied to develop an effective framework for simultaneously localizing multiple targets in wireless sensor networks. Nevertheless, existing methods implicitly use analog measurements, which have infinite bit precision. In this letter, we focus on off-grid target localization using quantized measurements with only several bits. To address this, we propose a novel localization framework for jointly estimating target locations and dealing with quantization errors, based on the novel application of the variational Bayesian Expectation-Maximization methodology. Simulation results highlight its superior performance.

In this work, we address the stationary target localization problem by using Doppler frequency shift (DFS) measurements. Based on the measurement model, the maximum likelihood estimation (MLE) of the target position is reformulated as a constrained weighted least squares (CWLS) problem. However, due to its non-convex nature, it is difficult to solve the problem directly. Thus, in order to yield a semidefinite programming (SDP) problem, we perform a semidefinite relaxation (SDR) technique to relax the CWLS problem. Although the SDP is a relaxation of the original MLE, it can facilitate an accurate estimate without post processing. Simulations are provided to confirm the promising performance of the proposed method.

The compressive sensing (CS) theory has been recognized as a promising technique to achieve the target localization in wireless sensor networks. However, most of the existing works require the prior knowledge of transmitting powers of targets, which is not conformed to the case that the information of targets is completely unknown. To address such a problem, in this paper, we propose a novel CS-based approach for multiple target localization and power estimation. It is achieved by formulating the locations and transmitting powers of targets as a sparse vector in the discrete spatial domain and the received signal strengths (RSSs) of targets are taken to recover the sparse vector. The key point of CS-based localization is the sensing matrix, which is constructed by collecting RSSs from RF emitters in our approach, avoiding the disadvantage of using the radio propagation model. Moreover, since the collection of RSSs to construct the sensing matrix is tedious and time-consuming, we propose a CS-based method for reconstructing the sensing matrix from only a small number of RSS measurements. It is achieved by exploiting the CS theory and designing an difference matrix to reveal the sparsity of the sensing matrix. Finally, simulation results demonstrate the effectiveness and robustness of our localization and power estimation approach.

Target determination based on Doppler frequency shift (DFS) measurements is a nontrivial problem because of the nonlinear relation between the position space and the measurements. The conventional methods such as numerical iterative algorithm and grid searching are used to obtain the solution, while the former requires an initial position estimate and the latter needs huge amount of calculations. In this letter, to avoid the problems appearing in those conventional methods, an effective solution is proposed, in which two best linear unbiased estimators (BULEs) are employed to obtain an explicit solution of the proximate target position. Subsequently, this obtained explicit solution is used to initialize the problem of original maximum likelihood estimation (MLE), which can provide a more accurate estimate.

Because accurate position information plays an important role in wireless sensor networks (WSNs), target localization has attracted considerable attention in recent years. In this paper, based on target spatial domain discretion, the target localization problem is formulated as a sparsity-seeking problem that can be solved by the compressed sensing (CS) technique. To satisfy the robust recovery condition called restricted isometry property (RIP) for CS theory requirement, an orthogonalization preprocessing method named LU (lower triangular matrix, unitary matrix) decomposition is utilized to ensure the observation matrix obeys the RIP. In addition, from the viewpoint of the positioning systems, taking advantage of the joint posterior distribution of model parameters that approximate the sparse prior knowledge of target, the sparse Bayesian learning (SBL) approach is utilized to improve the positioning performance. Simulation results illustrate that the proposed algorithm has higher positioning accuracy in multi-target scenarios than existing algorithms.

Wireless sensor networks (WSNs) are ubiquitous in a wide range of applications requiring the monitoring of physical and environmental variables, such as target localization and identification. One of these applications is the sensing of ferromagnetic objects. In typical applications, the area to be monitored is typically large compared to the sensing radius of each magnetic sensor. On the other hand, the RF communication radii of WSN nodes are invariably larger than the sensing radii. This makes it economical and efficient to design and implement a sparse network in terms of sensor coverage, in which each point in the monitored area is likely to be covered by at most one sensor. This work aims at investigating the sensing potential and limitations (e.g. in terms of localization accuracy on the order of centimeters) of the Honeywell HMC 1002 2-axis magnetometer used in the context of a sparse magnetic WSN. The effect of environmental variations, such as temperature and power supply fluctuations, magnetic noise, and sensor sensitivity, on the target localization and identification performance of a magnetic WSN is examined based on experimental tests. Signal processing strategies that could enable an alternative to the typical “target present/absent” mode of using magnetic sensors, such as providing successive localization information in time, are discussed.

In multi-static sonar systems, the least square (LS) and maximum likelihood (ML) are the typical estimation criteria for target location estimation. The LS localizaiton has the advantage of low computational complexity. On the other hand, the performance of LS can be degraded severely when the target lies on or around the straight line between the source and receiver. We examine mathematically the reason for the performance degradation of LS. Then, we propose a location adaptive — least square (LA-LS) localization that removes the weakness of the LS localizaiton. LA-LS decides the receivers that produce abnormally large measurement errors with a proposed probabilistic measure. LA-LS achieves improved performance of the LS localization by ignoring the information from the selected receivers.

An instrumental variable (IV) based linear estimator is proposed for effective target localization in sensor network by using time-difference-of-arrival (TDOA) measurement. Although some linear estimation approaches have been proposed in much literature, the target localization based on TDOA measurement still has a room for improvement. Therefore, we analyze the estimation errors of existing localization estimators such as the well-known quadratic correction least squares (QCLS) and the robust least squares (RoLS), and demonstrate advantages of the proposition by comparing the estimation errors mathematically and showing localization results through simulation. In addition, a recursive form of the proposition is derived to consider a real time application.