This article discusses the development of collaborative signal processing (CSP) algorithms for distributed sensor networks, focusing on the University of Wisconsin's (UW) research. The key challenges in sensor networks include efficient information exchange between nodes and collaborative signal processing to gather useful information about the physical world. The article highlights the integration of CSP algorithms with networking/routing algorithms (UW-API) and demonstrates their application in detecting and tracking a single maneuvering target. It also addresses the challenges of tracking multiple targets, emphasizing the need for classification techniques. The performance versus complexity trade-off is discussed, particularly in the context of power consumption and information exchange. The article outlines the importance of space-time sampling and cells for efficient processing and information fusion. It details the detection, localization, and tracking algorithms, including energy detection, target localization using exponential attenuation models, and target tracking through dynamic modeling. The article also explores spectral features and classification algorithms, such as k-Nearest Neighbor, Maximum Likelihood, and Support Vector Machine (SVM), and evaluates their performance on real seismic and acoustic data. Finally, it identifies future research directions, including intrasensor and intersensor collaboration, Doppler-based composite hypothesis testing, and addressing variability in experimental conditions.This article discusses the development of collaborative signal processing (CSP) algorithms for distributed sensor networks, focusing on the University of Wisconsin's (UW) research. The key challenges in sensor networks include efficient information exchange between nodes and collaborative signal processing to gather useful information about the physical world. The article highlights the integration of CSP algorithms with networking/routing algorithms (UW-API) and demonstrates their application in detecting and tracking a single maneuvering target. It also addresses the challenges of tracking multiple targets, emphasizing the need for classification techniques. The performance versus complexity trade-off is discussed, particularly in the context of power consumption and information exchange. The article outlines the importance of space-time sampling and cells for efficient processing and information fusion. It details the detection, localization, and tracking algorithms, including energy detection, target localization using exponential attenuation models, and target tracking through dynamic modeling. The article also explores spectral features and classification algorithms, such as k-Nearest Neighbor, Maximum Likelihood, and Support Vector Machine (SVM), and evaluates their performance on real seismic and acoustic data. Finally, it identifies future research directions, including intrasensor and intersensor collaboration, Doppler-based composite hypothesis testing, and addressing variability in experimental conditions.