Passive source positioning is of great interest due to the numerous applications. Energy based localization methods are popular because of their low cost and simplicity. In this paper, source localization with unknown transmit power is considered based on received signal strength difference (RSSD) measurements. An efficient three stage estimator is presented. First, a nonlinear RSSD-based model is formulated and the corresponding Fisher information for Gaussian distributed noise is derived. Next, a mean squared error (MSE) estimator is developed based on an unknown linear bias and Fisher information minimization. This results in a nonlinear optimization problem to minimize the MSE directly considering the linear bias. Finally, semidefinite relaxation is employed to transform this nonconvex problem into a convex minimization problem. This can be solved efficiently to obtain the optimal solution of the corresponding semidefinite programming (SDP) problem. Necessary and sufficient conditions for the optimality of the proposed RSSD based linear biased MSE SDP method (RLBM-SDP) are derived. Further, the corresponding bias sensitivity is formulated which yields an extension of the proposed method to a bounded RLBM-SDP (BRLBM-SDP) algorithm that includes a new constraint on the norm of the bias sensitivity. The computational complexity of the proposed methods is evaluated. Performance results are presented which confirm the efficiency of the proposed methods for sufficiently large signal to noise ratios.

Passive source localization is an important research area with numerous applications in mobile communications and signal processing. This paper presents an analysis of energy-based localization performance with unknown transmit power using received signal strength (RSS) and RSS difference (RSSD) models. These models are widely used because of their low cost and simple implementation. Previous studies were based on the assumption that the source node transmit power is known, which is not practical in many situations. The Cramér-Rao lower bound for the RSS and RSSD models with correlated noise is derived as a performance benchmark for the mean squared error (MSE) of the location and transmit power estimation. It is shown that the MSE can be factored into two independent terms corresponding to the geometric distribution of the sensors and the channel parameters including the noise variance. The effect of the sensor and source node positions on the location accuracy and the MSE is derived via the geometric dilution of precision (GDOP). The GDOP is then used to evaluate the effect of the joint estimation of unknown power and source location on the performance. Lower bounds on the GDOP are derived to obtain the minimum MSE. The RSSD solution is obtained by formulating the nonlinear and nonconvex objective functions into a convex optimization problem through relaxation and semidefinite programming. Simulation results are presented with the source inside or outside the sensor network.

Source localization is an important field of research with numerous applications in signal processing and wireless communications. In this paper, we present a received signal strength difference (RSSD) based method to localize a source with unknown transmit power in the presence of sensor position uncertainty. It is an efficient low complexity technique which does not require transmit power estimation as with other approaches. This eliminates the uncertainty due to signal propagation parameter variations. A constrained adaptive weighted least squares technique is presented to obtain a least squares initial estimate (LSIE) of the source location. Then, this estimate is improved using a computationally efficient modified Newton method (MNM) with adaptive weights. The bias of the proposed LSIE-MNM method and the Cramér-Rao lower bound (CRLB) of the RSSD based measurement model are derived to determine the effect of sensor position uncertainties on the source location estimate. Results are presented which show that the proposed method achieves the CRLB when the SNR is sufficiently high.

Passive source localization is an important area of research. In this paper, a received signal strength difference (RSSD) based localization technique is presented for wireless sensor networks. The advantage of this solution is that the transmit power does not have to be known. It is an effective low complexity technique as it eliminates the transmit power estimation required with other approaches. The uncertainties due to the signal propagation parameters are eliminated by using the received signal strength differences from known sensors. This yields a set of equations using signals from the source to sensors with known positions assuming a log-normal path loss model. The constrained weighted least squares (CLS) technique is used for location estimation using these equations. Results are presented which show that the performance of the proposed method achieves the Cramer-Rao lower bound (CRLB) for a sufficiently high signal to noise ratio (SNR).

Passive source location determination is a very active research area. In this paper, we present a received signal strength difference (RSSD) source localization method based on a total least square (TLS) estimator. Due to errors in the data vector and system matrix, the least squares (LS) and weighted least squares (WLS) methods are not applicable as they produce large bias in the location estimation. Therefore, an extension of the LS methods, called total least squares (TLS) is used to solve this problem. Due to the relationship between the data vector and system matrix, a modified TLS method is presented to achieve a closed form estimate. The advantage of this approach is that it does not require transmit power estimation as with other methods, and the complexity is low. The received signal strength difference is used to eliminate uncertainties due to the signal propagation parameters. Performance results are presented which show that the performance of the proposed method comes close to achieving the Cram'er-Rao lower bound.

Sperm whales (Physeter macrocephalus) emit acoustic signals when diving to search for food. Because they dive to depths of over 1500 m, conventional localization methods are impractical. Sperm whales produce a series of loud impulsive echolocation sounds called `clicks' at a fairly constant rate, and these signals can be used for localization. A geometric approach is considered using signals from the direct and surface reflection paths. The time difference of arrival (TDOA) between these paths is used for localization. Real sperm whale data from the Atlantic Undersea Test Center (AUTC) is used to evaluate the proposed method.

Reverberation is a major problem for time delay estimation (TDE) in enclosed environments. In this paper, a robust TDE method based on the generalized cross-correlation (GCC) is proposed. An efficient preprocessing technique to calculate the all-pass component is introduced to improve the performance of GCC-based techniques in reverberant conditions. Performance results are given which demonstrate that the proposed approach provides better performance for a wide range of microphone locations and reverberation times. Results in real acoustic environments confirm the effectiveness of the proposed TDE method. Compared to other TDE methods, our solution has low computational complexity and can be employed in real-time applications.

There are many underwater passive algorithms used to determine the range of a target like a diver, using its acoustic signals which are radiated through underwater environment. In this paper, we introduce a new underwater diver range estimation algorithm based on shallow water cavitation. Through this algorithm we apply diver's bubble oscillation frequency and ocean surface bubble's behaviour as two new carrier frequencies for our proposed method. Then, we utilize a dual frequency technique with the above new carrier frequencies, due to its robustness to shallow water noise, in time-frequency domain, yields to a new passive range estimation algorithm. We demonstrate through real data simulations that this algorithm is suitable for passive diver range estimation in shallow water environment. We used SWeIIEx-96 experiment data to compare our simulation result.