arXiv subjects

Shoab A. Khan

Publications and source records attributed to Shoab A. Khan.

3 recordsLinked to original sources

arXiv2026

Unsupervised Detection of Underground Tunnels in Ground-Penetrating Radar Using Depth-Restricted Reconstruction Scoring

Clandestine tunneling beneath oil and gas pipelines enables fuel theft, smuggling, and sabotage, yet conventional monitoring detects damage only after a pipeline has been compromised. Ground-penetrating radar (GPR) can image such tunnels non-invasively, but manual radargram interpretation does not scale to continuous corridor surveillance, and supervised detectors require tunnel examples that are scarce in practice. We present a fully unsupervised detection pipeline trained exclusively on normal subsurface radargrams collected at a purpose-built field site containing three buried tunnels at 1.5-3 m depth. A denoising convolutional autoencoder learns the structure of anomaly-free ground; at inference, tunnels are flagged by reconstruction error. Our central contribution is a depth-restricted top-k anomaly score, which pools the highest reconstruction errors only within the depth band where tunnels can physically occur. This physically motivated rule raises AUC from 0.986 to 0.994 and cuts missed detections from 74 to 17 of 634 tunnel windows, relative to whole-image scoring, without any retraining or labels. We further show that the optimal top-k fraction interacts with the depth restriction - 1% pooling is best on full images, 5% once scoring is depth-restricted - and that spatial voting across overlapping survey windows helps weak per-image detectors but offers no benefit once the scoring rule is strong. The final system attains AUC 0.994, F1 0.975, recall 0.973, and precision 0.976 on 1,600 field test windows spanning 55 survey lines, at a 1.6% false-alarm rate, using no tunnel labels for training, scoring, or threshold calibration.

cs.CV↗

arXiv2009

A Novel Two-Staged Decision Support based Threat Evaluation and Weapon Assignment Algorithm, Asset-based Dynamic Weapon Scheduling using Artificial Intelligence Techinques

Surveillance control and reporting (SCR) system for air threats play an important role in the defense of a country. SCR system corresponds to air and ground situation management/processing along with information fusion, communication, coordination, simulation and other critical defense oriented tasks. Threat Evaluation and Weapon Assignment (TEWA) sits at the core of SCR system. In such a system, maximal or near maximal utilization of constrained resources is of extreme importance. Manual TEWA systems cannot provide optimality because of different limitations e.g.surface to air missile (SAM) can fire from a distance of 5Km, but manual TEWA systems are constrained by human vision range and other constraints. Current TEWA systems usually work on target-by-target basis using some type of greedy algorithm thus affecting the optimality of the solution and failing in multi-target scenario. his paper relates to a novel two-staged flexible dynamic decision support based optimal threat evaluation and weapon assignment algorithm for multi-target air-borne threats.

cs.AI↗

arXiv2009

A Novel Two-Stage Dynamic Decision Support based Optimal Threat Evaluation and Defensive Resource Scheduling Algorithm for Multi Air-borne threats

This paper presents a novel two-stage flexible dynamic decision support based optimal threat evaluation and defensive resource scheduling algorithm for multi-target air-borne threats. The algorithm provides flexibility and optimality by swapping between two objective functions, i.e. the preferential and subtractive defense strategies as and when required. To further enhance the solution quality, it outlines and divides the critical parameters used in Threat Evaluation and Weapon Assignment (TEWA) into three broad categories (Triggering, Scheduling and Ranking parameters). Proposed algorithm uses a variant of many-to-many Stable Marriage Algorithm (SMA) to solve Threat Evaluation (TE) and Weapon Assignment (WA) problem. In TE stage, Threat Ranking and Threat-Asset pairing is done. Stage two is based on a new flexible dynamic weapon scheduling algorithm, allowing multiple engagements using shoot-look-shoot strategy, to compute near-optimal solution for a range of scenarios. Analysis part of this paper presents the strengths and weaknesses of the proposed algorithm over an alternative greedy algorithm as applied to different offline scenarios.

cs.AI↗