Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present novel lor bounds on the mean-square source-localization error via a network where the sensors are deployed randomly (e.g., scattered from the air). The sensor locations are modeled as a homogenous Poisson point process.  present CRB-type bounds on the expectation of the localization square error, which is not a function of a particular sensor configuration, but rather of the sensor statistics. Thus, it can be evaluated prior to the sensor deployment and provide insights into design issues such as the necessary sensor density. The derived bounds are simple for evaluation, while providing a good prediction of the actual network performance.
['Itsik Bergel', 'Yair Noam']
I am writing a research paper on a idea Source localization via randomly distributed sensors
[{"from": "human", "value": "I am writing a research paper on a idea Source localization via randomly distributed sensors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present novel lor bounds on the mean-square source-localization error via a network where the sensors are deployed randomly (e.g., scattered from the air). The sensor locations are modeled as a homogenous Poisson point process.  present CRB-type bounds on the expectation of the localization square error, which is not a function of a particular sensor configuration, but rather of the sensor statistics. Thus, it can be evaluated prior to the sensor deployment and provide insights into design issues such as the necessary sensor density. The derived bounds are simple for evaluation, while providing a good prediction of the actual network performance."}]