Ke He

TDOA-Based source collaborative localization via semidefinite relaxation in sensor networks

The time delay of arrival- (TDOA-) based source localization using a wireless sensor network has been considered in this paper. The maximum likelihood estimate (MLE) is formulated by taking the correlated TDOA noise into account, which is caused by the difference with the TOA of the reference sensor. The global optimal solution is difficult to obtain due to the nonconvex nature of the ML function. We propose an alternative semidefinite programming method, which transforms the original ML problem into a convex one by relaxing nonconvex equalities into convex matrix inequalities. In addition, the source localization algorithm in the presence of sensor location errors and non-line-of-sight (NLOS) observations is developed. Our simulation results demonstrate the potential advantages of the proposed method. Furthermore, the proposed source localization algorithm by taking the NLOS TOA measurements as the constraints of the convex problem can provide a good estimate.

Target localization based on structured total least squares with hybrid TDOA-AOA measurements

A weight least squares (WLS) method which is based on the first order Taylor expansions of the noise terms is developed and it reduces the estimation bias arising from the least squares (LS) method when the target is outside the convex hull formed by sensors.A novel localization method based on structured total least squares (STLS) is also developed in this paper to further reduce the estimation bias that easily arise from the traditional methods, while the RMSE of the STLS is comparable to the other methods specially when the target is outside the convex hull formed by sensors.The STLS method makes great use of the special sparse structure of the constructed measurement matrix, and thus improves the estimation accuracy.The performance improvement achieved by the WLS and STLS methods is demonstrated with respect to the LS method in 3 different geometry scenarios. In this paper, we focus on the target localization problem which finds broad applications in radar, sonar and wireless sensor networks. A pseudolinear overdetermined system of equations is constructed from the nonlinear hybrid TDOA-AOA measurements about target location. Considering the matrix and vector in the constructed pseudolinear system are both contaminated by the measurement noise, a new weight least squares (WLS) method which is based on the first order Taylor expansions of the noise terms is developed in this paper and it can reduce the estimation bias that arise from the least squares (LS) method. In particular we focus on constructing a localization algorithm to reduce the bias that easily arise from the traditional methods. Thus in addition, a novel structured total least squares (STLS) method is also developed in this paper to further reduce the estimation bias specially when the target is outside the convex hull formed by sensors. Numerical examples show the superiority of the proposed STLS method in estimation accuracy compared with the LS method, total least squares (TLS) method and the proposed WLS method.

Research on doppler frequency shift estimation in low Echo-to-Reverberation Ratio condition

Using the relationship between the number of zero-crossings and signal frequency, A Variable Bandwidth Band-pass Filter-Zero-Crossing Detection is proposed. This method can be used to estimate the echo frequency in Doppler Velocity Measurement. According to computer simulation, the estimation error of echo is 2Hz approximately (Echo-to-Reverberation Ratio is −15dB, echo frequency is 10360Hz); according to experiment, the estimation error of echo is 4Hz approximately (Echo-to-Reverberation Ratio is −3dB, echo frequency is 8080Hz). The amount of calculation of this method is lower than FFT. Therefore, this method has a good application prospects.