Tracking targets based on measurements provided by radar, sonar, or lidar sensors is essential to obtain situational awareness in important applications, including autonomous navigation and applied ocean sciences. A key challenge in multitarget tracking is the unknown association between the available measurements and the targets to be tracked. In particular, robust data association for closely spaced targets requires advanced methods that explicitly model unresolved measurements. Due to limited sensor resolution, the sensor produces a single measurement for two or more actual targets. If not explicitly modeled in the multitarget tracking method, unresolved measurements lead to track losses and thus, to significant tracking errors. In this paper, we propose a scalable data association method for the tracking of multiple potentially unresolved targets. A loopy belief propagation method is presented that efficiently approximates the marginal association probabilities given a set of potentially unresolved measurements. This method scales quadratically in the number of targets and linearly in the number of measurements. Our numerical results demonstrate that the computed approximate marginal association probabilities are close in $L_{1}$ distance to the true marginal association probabilities, which can only be calculated for very small tracking scenarios.

Network localization performance depends on the network geometry and, therefore, node deployment methods are critical for high-accuracy localization. Optimal node deployment is challenging in practical problems due to various uncertainties present in the position knowledge of the deployed nodes. In this paper, we propose a node-deployment method for network localization that accounts for such uncertainties. We develop a framework for the optimal deployment of location-aware networks under bounded disturbances in the positions of the sensing nodes. More specifically, by considering bounded discrepancies in the network geometry, we characterize the optimal deployment according to the D-optimality criterion and assert its implications for the A-optimality and E-optimality criteria. Results show that the proposed optimization-based design achieves a significative improvement according to the D-optimality criterion.

In this work, we propose a Labeled Multi-Bernoulli (LMB) filter for multi-object tracking with a merged measurement model. The finite resolution capabilities of practical sensing systems can lead to scenarios where multiple objects interact and generate merged measurements. In this work, we rely on the tractable LMB model for multi-object tracking and derive the Merged-Measurement LMB (MM-LMB) filter. Subsequently, we achieve an efficient implementation of the MM-LMB filter by relying on the K-shortest paths algorithm to find likely object-set partitions given a particular measurement set. Numerical results of our proposed filter show improved performance with respect to the standard LMB filter.

We propose a general sensor selection (SS) methodology for ocean-of-things (OoT) where a sensing network performs multiobject tracking (MOT) under resource constraints. SS methods address the combinatorial problem of determining the best subset of sensors that maximizes a suitable reward function for a fixed cardinality. The novelty of this article is twofold. First, we propose a tractable information-theoretic reward function for MOT-OoT with an unknown and time-varying number of objects such as ocean vessels. A tractable reward function is essential in order to rapidly evaluate a sensor subset, which is crucial in the high-dimensional problems encountered in OoT. Second, we propose a general cross-entropy SS (CE-SS) methodology that efficiently estimates the probabilities of sensor activations and determines the optimal sensor subset according to the proposed reward function and under the imposed cardinality constraint. The CE-SS algorithm avoids exhaustive searching over the space of all sensor subsets, which is intractable for most OoT applications. The CE-SS methodology, coupled with the proposed reward function, is capable of selecting sensors that lead to more accurate estimates than random selection for both the number of vessels and their trajectories. We demonstrate the effectiveness of our method via numerical simulation in serveral scenarios, including multivessel tracking for OoT with an emulated network of acoustic sensors deployed off the coast of Italy.

The multi-dimensional assignment problem, and by extension the problem of finding the T-best (i.e., the T most likely) multi-sensor assignments, represent the main challenges of centralized and especially distributed multi-sensor tracking. In this paper, we propose a distributed multi-target tracking filter based on the δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) family of labeled random finite set densities. Consensus is reached for high-scoring multi-sensor assignments jointly across the network by employing the cross-entropy method in conjunction with average consensus. This ensures that multi-sensor information is jointly used to select high-scoring multi-assignments without exchanging the measurements across the network and without exploring all possible single-target multi-assignments. In contrast, tracking algorithms that rely on posterior fusion, i.e., merging local posteriors of neighboring nodes until convergence, are suboptimal due to the use of only local information to select the T-best local assignments in the construction of local posteriors. Numerical simulations showcase this performance improvement of the proposed method with respect to a posterior-fusion δ-GLMB filter.

This paper proposes an energy-efficient counting rule for distributed detection by ordering sensor transmissions in wireless sensor networks. In the counting rule-based detection in an N-sensor network, the local sensors transmit binary decisions to the fusion center, where the number of all N local-sensor detections are counted and compared to a threshold. In the ordering scheme, sensors transmit their unquantized statistics to the fusion center in a sequential manner; highly informative sensors enjoy higher priority for transmission. When sufficient evidence is collected at the fusion center for decision making, the transmissions from the sensors are stopped. The ordering scheme achieves the same error probability as the optimum unconstrained energy approach (which requires observations from all the N sensors) with far fewer sensor transmissions. The scheme proposed in this paper improves the energy efficiency of the counting rule detector by ordering the sensor transmissions: each sensor transmits at a time inversely proportional to a function of its observation. The resulting scheme combines the advantages offered by the counting rule (efficient utilization of the network's communication bandwidth, since the local decisions are transmitted in binary form to the fusion center) and ordering sensor transmissions (bandwidth efficiency, since the fusion center need not wait for all the N sensors to transmit their local decisions), thereby leading to significant energy savings. As a concrete example, the problem of target detection in large-scale wireless sensor networks is considered. Under certain conditions the ordering-based counting rule scheme achieves the same detection performance as that of the original counting rule detector with fewer than N/2 sensor transmissions; in some cases, the savings in transmission approaches (N-1).

In this paper we derive a particle flow particle filter implementation of the δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) filter. The bootstrap particle filter δ-GLMB suffers from weight degeneracy for high-dimensional state systems or low measurement noise. In order to avoid weight degeneracy, we employ particle flow to produce a measurement-driven importance distribution that serves as a proposal in the δ-GLMB particle filter. Flow-induced proposals are developed for both types of targets encountered in the δ-GLMB filter, i.e., persistent and birth targets. Numerical simulations reflect the improved performance of the proposed filter with respect to classical bootstrap implementations.

In this paper, we propose a novel model-based approach for 3D underwater scene reconstruction, i.e., bathymetry, for side scan sonar arrays in complex and highly reverberating environments like shallow water areas. The presence of multipath echoes and volume reverberation generates false depth estimates. To improve the resulting bathymetry, this paper proposes and develops an adaptive filter, based on several original geometrical models. This multimodel approach makes it possible to track and separate the direction of arrival trajectories of multiple echoes impinging the array. Echo tracking is perceived as a model-based processing stage, incorporating prior information on the temporal evolution of echoes in order to reject cluttered observations generated by interfering echoes. The results of the proposed filter on simulated and real sonar data showcase the clutter-free and regularized bathymetric reconstruction. Model validation is carried out with goodness of fit tests, and demonstrates the importance of model-based processing for bathymetry reconstruction.

In this paper a 3-D reconstruction method is proposed of sea bottom topography, i.e. bathymetry, for sonar data in highly reverberant and multi-path underwater environments. Recent publications showcase the negative impact of waves involving sea surface reflections on the sea bottom imaging process. The novelty of our proposal is twofold: firstly it relies on the use of Markovian-model based processors for bahymetric reconstruction and involves data association filters. Secondly, we propose a nearest neighbor version of the Integrated Probabilistic Data Association filter, capable of filtering several echo trajectories in the presence of clutter with a relatively reduced complexity compared to other existent methods.