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—The detection probability of maritime vessels may change over time, either slowly or more abruptly, due to effects such as sea state and varying aspect angle. If the varying detectability is not accounted for in the tracking system, tracks may be terminated or lost. The undetected targets may cause dangerous situations in applications such as maritime collision avoidance. In the following, we propose two methods for tracking targets with varying detection probability, both in the integrated probabilistic data association (IPDA) framework. One method uses the number of validated measurements to estimate the detectability of the target, while the other calculates the joint detectability and existence probabilities based on the measure- ment association likelihoods. Both methods show significant improvements over the conventional Markov chain 1 and 2 IPDAs

International audience; Acoustic propagation delay has not been investigated for a continuous wave multistatic sonar tracking system except for the recent study conducted by Jauffret et al. [6], which estimates the trajectory of a constant velocity target. The results showed that the estimate bias caused by the propagation delay is not negligible, especially for a bistatic system. This paper develops an interacting multiple model unscented Gauss-Helmert filter with numerical Jacobian (IMM-UGHF-NJ) to track a maneuvering target with propagation delay using a bistatic sonar system. The IMM-UGHF-NJ can overcome the two tracking challenges introduced by the delay, namely, implicit state transition model and lack of analytical expression of the Doppler shifted frequency in the measurement model. Simulation tests have been conducted, and the results show that the IMM-UGHF-NJ can reduce the estimation error significantly, especially for long range or fast moving targets. Manuscript

One regularity condition for the classical Cramér-Rao lower bound (CRLB) of an unbiased estimator to hold - that the support of the likelihood function (LF) should be independent of the parameter to be estimated - has recently been relaxed to the case of parameter-dependent support as long as the LF is continuous at the boundary of its support. For the case where the LF is not continuous on the boundary of its support, a new modified CRLB - designated the Cramér-Rao-Leibniz lower bound (CRLLB) as it relies on the Leibniz integral rule - has also been presented for the scalar parameter case. The present work derives the multidimensional CRLLB for the case of LF with parameter-dependent support by applying the general Leibniz integral rule to complete the framework of the CRLLB.

One regularity condition for the classical Cramer-Rao lower bound (CRLB) of an unbiased estimator to hold is that the support of the likelihood function (LF) should be independent of the parameter to be estimated. This has been shown to be too stringent and the CRLB has been shown to be valid for the case of parameter-dependent support as long as the LF is continuous at the boundary of its support. For the case where the LF is not continuous at the boundary of its support, a new modified CRLB — designated as the Cramer-Rao-Leibniz lower bound (CRLLB) as it relies on the Leibniz integral rule — has been presented for the scalar parameter and measurement case in [3]. The CRLLB for multidimensional parameter and measurements has been developed in [8]. The present work applies the multidimensional CRLLB to n-dimensional measurement noise with the raised fractional cosine and the truncated Laplace distributions inside an (n − 1)-sphere.

Target motion analysis with wideband passive sonar has received much attention. Maximum likelihood probabilistic data-association (ML-PDA) represents an asymptotically efficient estimator for deterministic target motion, and is especially well-suited for low-observable targets; the results presented here apply to situations with higher signal to noise ratio as well, including of course the situation of a deterministic target observed via clean measurements without false alarms or missed detections. Here we study the inverse problem, namely, how to identify the observing platform (following a two-leg motion model) from the results of the target estimation process, i.e. the estimated target state and the Fisher information matrix, quantities we assume an eavesdropper might intercept. We tackle the problem and we present observability properties, with supporting simulation results., Comment: To appear in IEEE Transactions on Aerospace and Electronic Systems