Citation: Hu, Q.; Liu, Y.; Mao, R.;
Yang, C. Detection Performance
Evaluation for Marine Wireless
Sensor Networks. Electronics 2022, 11,
3367. https://doi.org/10.3390/
electronics11203367
Academic Editor: Dimitris
Kanellopoulos
Received: 21 August 2022
Accepted: 13 October 2022
Published: 19 October 2022
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Article
Detection Performance Evaluation for Marine Wireless
Sensor Networks
Qi Hu
1
, Yaobo Liu
2
, Ruoxin Mao
1
and Chaoqun Yang
3,
*
1
Nanjing Research Institute of Electronics Technology, Nanjing 210012, China
2
School of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing),
Beijing 100083, China
3
School of Automation, Southeast University, Nanjing 210096, China
* Correspondence: cqyang01@gmail.com
Abstract:
Detection performance evaluation is one of the inevitable problems for marine wireless
sensor networks (MWSNs) deployed for target detection. However, it is a very complicated problem
since it associates many different aspects, such as emitter power, range, radar cross-section, weather,
geography, working mode, and so on. Targeting this problem, this paper incorporates the Poisson
point process model into describing the ranges from sensors to targets. The relationship between
sensors and a target is built from the perspective of detection probabilities. Then, a new consistent,
conservative target detection probability evaluation is derived within a CFAR framework, and the
further global detection probability of the whole MWSN on the target is developed. Additionally, the
rationality of this modeling approach is demonstrated via simulation results, which is in accord with
the actual situation.
Keywords:
constant false alarm rate; covariance intersection; detection probability; poisson point
process; marine wireless sensor networks; modeling approach
1. Introduction
Recently, marine wireless sensor networks (MWSNs), composed of many sensors with the
capability of wireless communication, and primary data processing floating on the sea, have
been widely used in various applications, such as ocean remote sensing [
1
,
2
], maritime search
and rescue [
3
,
4
], target detection (including air vessel and submarine detection) [
5
–
8
], and so on.
Due to their low cost and mobile deployment, the energy budgets, communication capabilities,
and the detection capabilities of the individual sensors in the MWSNs for target detection are
always limited. Thus, to achieve better MWSN detection performance, detection fusion is
necessary. However, it is an intractable task to fuse the individual detection performance from
different sensors and consequently attain the global detection performance evaluation for the
MWSNs. The reasons are two-fold. The first, as mentioned above, is that the communication
capabilities of individual sensors are limited. The second is that the locations of the sensors
usually change randomly because of harsh weather and rolling waves.
Most research in this area is divided into two parts. The main work focuses on the
signal detection area, designed to obtain a high signal-to-noise ratio around the region of
target echo signals [
9
,
10
]. However, no specific estimation of target detection probability
is concerned, and the corresponding detection value is not given [
11
,
12
]. Another part
concentrates on developing filters with unknown target detection probability, attempting to
alleviate the dependency of the algorithm’s performance on the target detection probability.
The fundamental mathematical derivation of a random finite set framework with an
unknown background can be found in [
13
–
15
]. The influence of clutter rate on target
detection probability is analyzed in [
16
]. Furthermore, a direct estimation of the clutter
rate is incorporated into the cardinalized probability hypothesis density filter [
17
], and
the same method is introduced in the Generalized Labeled multi-Bernoulli filter [
18
].
Electronics 2022, 11, 3367. https://doi.org/10.3390/electronics11203367 https://www.mdpi.com/journal/electronics
